Continue # Infosys Whitepaper
cribe’ who notes down the  key observations.<|endoftext|>

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  Benefits of Chaos Engineering To ignore chaos engineering is to embrace crisis engineering.  Proactive QE teams have made chaos engineering a part of their  regular operations by exposing their staff to chaos tests and  collaboratively experimenting with other business units to refine  testing and improve enterprise systems.<|endoftext|>Chaos engineering delivers several benefits such as:  • Reduced detection time – Early identification of issues caused  due to failures occurring in live environments, making it easy to  proactively identify which component may cause issues • Knowing the path to recovery – Chaos engineering helps  predict system behavior in case of failure events and thus works  towards protecting the system to avoid major outages • Being prepared for the unexpected – It helps chart mitigation  steps by experimenting with known system failures in a  controlled environment • Highly-available systems – Enables setting alerts and  automating mitigation actions when known failures occur in a  live environment, thereby reducing system downtime • Improved customer satisfaction – Helps avoid service  disruptions by detecting and preventing component outages,  thereby enhancing user experience, increasing customer  retention, and improving customer acquisition  Chaos engineering brings about cultural changes and maturity  in the way an enterprise designs and develops its applications.  However, its success calls for strong commitment from all levels  across the organization.<|endoftext|>{{ img-description : different tree varieties on a blue background, in the style of vray tracing, calming symmetry, multilayered dimensions, eco-kinetic  }}

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 © 2023 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected Conclusion System failures can prove very costly for enterprises, making it critical for organizations to focus on quality engineering practices. Chaos  engineering is one such practice that boosts resilience, flexibility, and velocity while ensuring the smooth functioning of distributed  enterprise systems. It allows organizations to introduce attacks that identify system weaknesses so they can rectify issues proactively. By  identifying and fixing failure points early in the lifecycle, organizations can be prepared for the unexpected, recover faster from disruptions,  increase efficiency, and reduce cost. Ultimately, it culminates in better business outcomes and customer experience.<|endoftext|>About the
Authors Harleen Bedi   Senior Industry Principal Jack Hinduja   Lead Consultant Harleen is a Senior IT Consultant with Infosys. She focuses on developing and promoting IT offerings for quality  engineering based on emerging technologies such as AI, cloud, big data, etc. Harleen builds, articulates, and deploys QE  strategies and innovations for enterprises, helping clients meet their business objectives.<|endoftext|>Jack Hinduja is a Lead Consultant at Infosys with over 15 years of experience in the telecom and banking sectors.  He has led quality assurance and validation projects for enterprises across the globe. Jack is responsible for driving  transformation in digital quality assurance and implementing performance and chaos engineering practices in various  enterprises.

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# Infosys Whitepaper
Title: Improve Software Quality and Accelerate Delivery by Adopting Service Virtualization Wisely
Author: Infosys Limited
Format: PDF 1.7

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 VIEW POINT IMPROVE SOFTWARE QUALITY AND  ACCELERATE DELIVERY BY ADOPTING  SERVICE VIRTUALIZATION WISELY -Manoj Aggarwal, Delivery Manager

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 Context  As business applications moved from  tightly coupled solutions to service-based  distributed architecture, it opened a new  range of business and technical possibilities.  Services-based architecture enables IT  solution providers to expose their business  solutions to consumers in a loosely coupled  on-call consumer / provider model. Within  the organization landscape, it provides  enterprise application teams a mechanism  to deploy reusable business functions as  services for internal consumption.<|endoftext|>Though services have improved the software  delivery process, there are some key lessons  to be learnt. Business applications depend  on SOA based services for critical business  functions. This makes developers and testers  realize that services behavior knowledge  and service availability are the key factors  that will define the success and quality of  the solutions developed by leveraging these  services. To address these dependencies,  developers started ‘mocking and stubbing’  of the service for development and unit  testing. Testing teams depended heavily  on the proactive services environment  blocking to ensure availability of services  and data for testing. Both these approaches  lead to increased effort and timelines for  development and testing. However, with  software development moving to shorter  cycles in Agile and DevOps world and  services becoming increasingly complex,  these service dependencies became a key  obstacle in faster delivery, especially in  situations involving parallel development.<|endoftext|>Drawing from the hardware virtualization  experience, virtualization of services  evolved into an industry standard practice  that allowed both developers and testers  with a realistic approach to handle services  dependencies. Developers managed  to integrate code with the virtualized  services for ‘live like’ code integration in  development environment, enabling these  to test and capture potential code issues  early. Application testers benefited from  the reduced environment and test data  dependency result in faster execution and  effective leveraging of the automated  testing scripts.<|endoftext|>When To Go for Service Virtualization As awareness of the service virtualization and its associated benefits percolated to IT  teams, both providers and consumers of services started to deploy virtualization solutions  at a brisk pace. A fair number of implementations delivered significant business benefits  ranging from 20-30% infra cost reduction, 10-15% reduction in delivery timelines, and  15-20% reduction in defect slippage. A significant majority of virtualization solutions  continued to struggle with justification of investments in the development and  maintenance of these solutions.<|endoftext|>At Infosys, we believe a holistic analysis of the service consumption pattern such as a service  user base, data requirement, and cost of consumption are some parameters that help  decide the best fit for service virtualization solution deployment. One more factor that will  increasingly define virtualization deployment is the software development methodology  followed by IT teams.<|endoftext|>External Document © 2018 Infosys Limited

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 Service virtualization works best for service  providers in a number of instances. This includes  the following:  •  Services are yet to be developed and consumers need  services for their development / testing to meet rollout  timelines •  Services consumer base is very high and availability of  test data / environment is a challenge • Maintaining live services environment is expensive •  Services behavior is fairly stable and test data / scenarios  can be covered with the limited data set Service virtualization works best for service  consumers in a few instances. This includes the  following: •  Services provider cannot provide services for the  development  /  testing team •  Provider does not provide a live / virtualized service  economically •  Availability of services and data in test environment is a  challenge •  Service that is frequently needed by development  teams due to the volatile nature of the consuming  application It is not recommended for both consumer and provider in the following instances: Services are readily  available and inexpensive  to maintain Services behavior  changes very frequently Services need live data for  testing and development Services have complex  business logic with volatile  test data External Document © 2018 Infosys Limited

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 Services Identification & Solution  Definition Build & Deploy Maintain Services (Heal Services) Analyze architecture Landscape Virtual Service Design Documentation Continuous improvements and  enhancements to support new  requirements Identify constrained systems Build Virtual Services  Republish new versions and refresh test  data  Formulate high level Strategy  Test Virtual Services to ensure  completeness & correctness Decommission end of life virtualized  services Perform tool fitment analysis Deploy & Publish Virtual Endpoints  Feasibility analysis Service Virtualization Approach Infosys recommends a three-step process for service virtualization as depicted below: Conclusion  Service Virtualization has evolved into a key component of software development lifecycle. Effective deployment of service virtualization  can bring immense benefits with improved quality of the code and faster delivery. However, all services need not to be virtualized and  project teams should plan switch to live services at an appropriate juncture to ensure applications are tested in the ‘LIVE’ environment  and a reasonable balance is maintained between the real and virtual world of services.<|endoftext|>© 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected

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# Infosys Whitepaper
Title: Do more with less in software testing
Author: Infosys Limited
Format: PDF 1.6

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 WHITE PAPER DO MORE WITH LESS IN SOFTWARE  TESTING Abstract Faced with pressure to deliver more despite ever-shrinking  budgets and shorter timelines, most companies struggle to  balance the cost of innovation with business demands. Testing  is a critical area where neither speed nor quality of output can  be compromised as this leads to negative business impact. This  paper explains some cost-effective strategies that enable testing  organizations to improve efficiency within their testing teams  while ensuring high-quality output.<|endoftext|>Indumathi Devi

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 External Document © 2018 Infosys Limited Introduction  Even as the demands for agile software  increase exponentially, testing budgets  continue to shrink. Software testing  teams within most organizations struggle  to deliver quality software in shorter  timelines and tighter budgets. Further,  most software tests tend to reside in  silos making integration, collaboration  and automation challenging. Thus,  organizations need innovative testing  solutions and strategies to balance quality,  speed and cost.

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 External Document © 2018 Infosys Limited Agile testing solutions and strategies 1. Test with an end-user  mindset  The job of testing goes beyond checking  software against preset requirements or  logging defects. It involves monitoring  how the system behaves when it is actually  being used by an end-user. A common  complaint against testers is that they do  not test software from the perspective of  business and end-users. Effective risk- based testers are those who understand  the system’s end-users and deliver value- added testing services that ensure quality  products and meet clients’ expectations.  To do this, testers must evaluate products  by undertaking real business user journeys  across the system and test commonly  used workflows in short testing windows.  By mimicking real user journeys, such  testers identify higher number of critical  production defects.<|endoftext|>2. Empower cross functional  teams   Agile and DevOps methodologies in  software testing are forcing teams to work  together across the software development  lifecycle (SDLC). Engendering test  independence is not about separating  testing from development as this can  lead to conflicts between developers  and testers. Most teams have a polarized  dynamic where testers search for defects  and must prove how the program is  erroneous while programmers defend their  code and applications. Cross-functional  teams eliminate such conflict by gathering  members with different specializations  who share accountability and work toward  common goals. For instance, ‘testers’ are  simply team members with testing as their  primary specialization and ‘programmers’  are those within the team who specialize  in coding.  This team structure encourages people  to work with a collaborative mindset and  sharpen their expertise. Such teams can be  small, with as few as 4-8 members who are  responsible for a single requirement or part  of the product backlog. Cross-functional  teams provide complete ownership and  freedom to ensure high-quality output – an  important step to realizing the potential of  agile.  3. Automate the automation  Running an entire test suite manually  is time-consuming, error-prone and,  often, impossible. While some companies  are yet to on-board agile and DevOps  capabilities, others have already integrated  the practice of continuous integration (CI)  and continuous delivery (CD) into their  testing services. Irrespective of the level of  DevOps maturity, CI/CD will provide only  limited value if not paired with the right  kind and degree of testing automation.  Thus, organizations need a robust, scalable  and maintainable test automation suite  covering the areas of unit, API, functional,  and performance testing. Automated  testing saves effort, increases accuracy,  improves test coverage, and reduces  cycle time. To ensure automation success,  organizations must focus on: •   Automating the right set of tests,  particularly business-critical end-user  journeys and frequently used workflows •   Integrating various components that  may change continuously and need to  be regressed frequently •   Automating data redundant tests •   Using the right set of automation tools  and frameworks •   Moving beyond just the user interface  and automating unit, APIs and non- functional tests •   Continuous maintenance and use of  automated test suite  On its own, test automation is important.  However, when automation is integrated  with a CI/CD pipeline to run every time  new code is pushed, the benefits in time,  cost and quality are multiplied.

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 © 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected Conclusion About the author In a fast-paced and evolving digital world, companies want their IT partners to do more  with less. When it comes to software testing, this places heavy pressure on software testers  who are required to push high-quality code faster at lower cost. To streamline software  testing, organizations need an approach where their testers adopt an end-user mindset by  testing real user journeys and critical business transactions. They must also create efficient  cross-functional teams that collaborate to achieve common goals and deliver value-added  testing services. Finally, automating different layers of testing and practicing CI/CD will  facilitate continuous testing and reduce time-to-market. These cost-effective strategies will  help software testing professionals improve productivity and deliver more with less.<|endoftext|>Indumathi Devi is a Project Manager at Infosys with over 15 years of experience in software testing. She has effectively executed multiple  software testing projects. Working in different domains and technology stacks, she has assisted numerous clients in implementing robust  software testing solutions for manual as well as automated testing.<|endoftext|>

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# Infosys Whitepaper
Title: Solving the Test Data Challenge to Accelerate Digital Transformation
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER SOLVING THE TEST DATA  CHALLENGE TO ACCELERATE DIGITAL  TRANSFORMATION Abstract Organizations are increasingly adapting to the need to deliver products and  services faster while continuously responding to market changes.<|endoftext|>In the age of mobile apps, test automation is not new. But traditional test  data management (TDM) approaches are unable to help app development  teams address modern delivery challenges. Companies are increasingly  struggling to keep up with the pace of development, maintain quality of  delivery, and minimize the risk of a data breach.<|endoftext|>This white paper illustrates the need for a smart, next-gen TDM solution to  accelerate digital transformation by applying best practices in TDM, zero- trust architecture, and best-in-class test data generation capabilities.  {{ img-description : two people looking out of a glass door in a building, in the style of light navy and dark azure, security camera art, webcore, uniformly staged images, selective focus, grainy, masculine (to left) }}

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 External Document © 2021 Infosys Limited

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 External Document © 2021 Infosys Limited Traditional Test Data Management.........................................................................................4 Why the New Normal was not Enough?.................................................................................4 Five Key Drivers and Best Practices in Test Data Management.........................................5 Future-proof Test Data through Next-gen TDM Innovation..............................................8 Accelerate through Next-gen TDM Reference Architecture...............................................9 The Way Forward - Building Evolutionary Test Data for your Enterprise...................... 11 About the
Authors................................................................................................................... 12 Figure 1. Key focus areas emerging in test data management.........................................4 Figure 2. Key drivers and best practices in TDM...................................................................5 Figure 3. Zero trust architecture..............................................................................................6 Figure 4. Stakeholder experience............................................................................................7 Figure 5. Focus areas of Infosys Next-Gen TDM....................................................................8 Figure 6. Infosys Next-Gen TDM reference architecture.....................................................9 Figure 7. Contextual test data and its different formats.................................................. 10 Table of Contents Table of Figures

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 External Document © 2021 Infosys Limited Traditional Test Data Management  Test data management (TDM) should ensure that test data is of the highest possible quality and available to users. In the digital age,  managing test data using traditional TDM practices is challenging due to its inability to accelerate cloud adoption, protect customer data,  provide reliable data, avoid data graveyards, ensure data consistency, and automate and provision test data.<|endoftext|>Why the New Normal was not Enough? While the ‘new normal’ has become a catchword in 2021, in the world of testing, this ‘normal’ was not effective for many organizations. The  pressure to adapt to changing customer expectations, new technology trends, changing regulatory norms, increased cybersecurity threats,  and scarcity of niche skills has raised many challenges for organizations. In light of this, many are wondering whether they should revisit their  test data strategy.  Figure 1. Key focus areas emerging in test data management As time-to-market for products and  services becomes critical, test data  generation and provisioning emerge as  bottlenecks to efficiency. Further, test data  management has been represented as  the weak link for organizations looking to  accelerate digital transformation through  continuous integration and delivery. High  quality test data is a prerequisite to train  machine learning (ML) models for accurate  business insights and outcome predictions.  To build a competitive difference,  organizations today are investing in three key  focus areas in test data management (refer  Figure 1): • New business models – With a strong focus  on customer experience, organizations  must adopt new business models and  accelerate innovation. There is a need to  generate data that can be controlled and  is realistic as well as accurate to meet real- world production needs.  • Hyper-productivity – Automation  and iterative agile processes push the  need for better testing experiences  with faster and more efficient data  provisioning, allowing organizations to  do more with less.<|endoftext|>• New digital workplace – Millions of  employees are working from home.  Organizations must focus on building  a secure, new-age digital workplace to  support remote working.<|endoftext|>Changing Customer  Expectations Head winds – New  Tech Trends Regulatory  Changes Cyber Security  Threats Skill Scarcity APPS Hyper productivity - Agile New Business Models  - Multi-cloud  environment Employees Partners Customers New Digital  Workplace Community Data Privacy and  Security

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 External Document © 2021 Infosys Limited Five Key Drivers and Best  Practices in Test Data  Management Companies are increasingly struggling to  keep up with the pace of development,  maintain quality of delivery, and achieve  absolute data privacy. On-demand  synthetic test data is a clear alternative to  the traditional approach of sub-setting,  masking, and reserving production data  for key business analytics and testing. In  this context, three key questions to ask are: 1. What are the drivers and best practices  to be considered while building a test  data strategy?  2. How can CIOs decide what is the right  direction for their test data strategy? 3. What are the trade-offs in test data  management? There are five elements – cost, quality,  security and privacy, tester experience, and  data for AI – that drive a successful test  data management strategy. Understanding  the best-practices around these will guide  CIOs in making the right decision.  Figure 2. Key drivers and best practices in TDM Accelerate Next-gen TDM Adapting for agile & devOps | Self-serviced data provisioning | Increased test data automation Test data automation {{ img-description : a man in glasses is holding a tablet in front of a room full of server racks, in the style of light gray and indigo, uniformly staged images, restored and repurposed, ue5, traditional  }}

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 External Document © 2021 Infosys Limited Key Drivers Impact on Test Data Strategy Best Practices 1.  Cost What is the return on investment (ROI) and  acceptable investment to create, manage,  process, and, most importantly, dispose of test  data?  Production data must be collected, processed,  retained, and disposed of. The processing and  storage cost must offset the investment in TDM  products. Procurement, customization, and  support costs need to be considered.<|endoftext|>• Test data as a service – Test data on cloud with a subscription for  testers can lower the provisioning of full-scale TDM.<|endoftext|>2.  Quality Do we have the right quality of data? Can we  get complete control over the data? Can we  generate test data in any format? Testers have very limited control over the data  provided by production. The test data is usually  a subset of data from production and cannot  cater to all the use cases including negative and  other edge use cases. Further, there is a need to  generate electronic data interchange (EDI) files,  images, and even audio files for some of the use  cases.<|endoftext|>• TDM suite can help build a subset of data designed with realistic and  referentially intact test data from across the distributed data sources  with minimal cost and administrative effort.<|endoftext|>• Synthetic data generators should have the breadth to cover key data  types and file formats along with the ability to generate high-quality  data sets, whether structured or unstructured, across images, audio  files, and file formats.<|endoftext|>3.  Security  and privacy Do we have the right data privacy controls while  accessing data for testing? How do we handle a  data privacy breach? The focus on privacy and security of the data  used for testing is increasing. Complying with  GDPR and ensuring the right data privacy  controls is a catalyst for organizations to move  away from using direct production data for  testing purposes. There is increased adoption  of masking, sub-setting, and synthetic data  generation to avoid critical data breaches when  using sensitive customer, partner, or employee  data.<|endoftext|>• Zero trust architecture provides a data-first approach, which is secure  by design for each workload and identity-aware for every persona in  the test management process including testers, developers, release  managers, and data analysts.<|endoftext|>         Figure 3. Zero trust architecture • To ensure security of sensitive information, organizations can create  realistic data in non-production environments without exposing  sensitive data to unauthorized users. Enterprises can leverage  contextual data masking techniques to anonymize key data elements  across an enterprise.<|endoftext|>


Continue # Infosys Whitepaper
Analyze Discover Data Masking Data Generation Data Copy / Sub-Set Validate Export & Refresh Virtualize Production Production Clone Production Sub-set Gold Copy Gold Copy (Sub-set) Gold Copy (SyntheticData) Masking Data Generation Sub-setting Non-Production DB Logs Files Virtualized Clone Developer Tester Release Manager Data Privacy Architecture Sensitive Data Discovery Self Service Virtualize Test Data Set up Contextual Synthetic Data Data Sub-setting Test Environment Provisioning Monitoring Diferential Privacy Data Scientist / Analyst

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 4.  Tester  experience Are we building the right experience for the  tester? Is it easy for testers to get the data they  need for their tests? Customers struggle to meet the agile  development and testing demands of iterative  cycles. Testers are often forced to manually  modify the production data into usable values  for their tests. Teams struggle to effectively  standardize and sub-set the production data  that has been masked and moved to test data.<|endoftext|>Figure 4. Stakeholder experience • Test data automation puts the focus on tester experience by enabling  a streamlined and consistent process with automated workflows of  self-service test data provisioning • Test data virtualization allows applications to automatically deliver  virtual copies of production data for non-production use cases. It also  reduces the storage space required.<|endoftext|>5.  Data for AI Do we understand insights generated by the  data?  The probabilistic nature of AI makes it very  complex to generate test data for training AI  models.   • Adopt mechanisms for data discovery, exploration and due diligence.  Data resides in different formats across systems. Enterprises must  identify patterns across multiple systems and file formats and provide  a correct depiction of the data types, locations, and compliance  rules according to industry-specific regulations. They should also  focus on identifying patterns, defects, sub-optimal performance, and  underlying risks in the data.<|endoftext|>• For data augmentation, analysts and data scientists can be provided  with datasets for analysis. The datasets must be resistant to  reconstruction through differential privacy for effective data privacy  protection.<|endoftext|>External Document © 2021 Infosys Limited Stakeholder Experience Developer Not able to get the  right data for development Customer We are prepared to move the IT delivery to another consulting frm if you cannot handle Data Privacy and Security IT Head Not able to provision and create gold copy for testing Tester We need the right test data without sensitive  data, else we cannot fnish testing Business Sponsor We want zero defects, and no data security breaches in Product Development Scrum Master We had to meet critical business requirements; we  could not provision the right data for Testing {{ img-description : two people are looking at computers in a server room, in the style of miscellaneous academia, light silver and indigo, glazed surfaces, iso 200, rubens, weathercore, traditional craftsmanship }}

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 1. Tester user experience – Testers  need to assess business and technical  requirements from the perspective of  testability as well as end users. Infosys  Next-Gen TDM provides a framework  that includes testers and gives them a  360-degree view of the TDM process.<|endoftext|>2. AI-driven data discovery – Modern test  data resides on a tower of abstractions,  patterns, test data sources, and privacy  dependencies. One of the key features  of Infosys Next-Gen TDM is smart data  discovery of structured and unstructured  data using AI. This helps uncover: •  Sensitive data (PII/PHI/SPI) to avoid data  privacy breaches •  Data lineages to build the right contextual  data while maintaining referential  integrity across child and parent tables 3. Data virtualization – This is needed for  organizations to access heterogeneous  data sources. Infosys Next-Gen TDM  provides a lightweight query engine that  enables testers to mine lightweight copies  that are protected.<|endoftext|>4. Data provisioning – There are numerous  challenges faced by testing teams in  getting access to the right data. Large  enterprises need approvals to access data  from businesses and app owners. Infosys  Next-Gen TDM provides an automated  workflow for intelligent data provisioning.  With this, testers can request data and  manage entitlements as well as approvals  through a simplified UX.<|endoftext|>5. Privacy-preserving synthetic data – It  is important to protect personal data  residing in the data sources being  curated for test data. There is always a  risk of personal data being compromised  when there is a large amount of training  or testing data involved. It can result  in giving too much access to sensitive  information. Improper disclosure of such  data can have adverse consequences  for a data subject’s private information.  It may put data subject at more risk of  stalking and harassment. Cybercriminals  can also use data subject’s bank details  or credit card details to degrade subject’s  credit rating. Privacy-preserving synthetic  data focuses on ensuring that the data  is not compromised while maximizing  the utility of the data. Differential privacy  prevents linkage attacks, which cause  records to be re-identified even after  being anonymized for testing.<|endoftext|>6. Smart augmentation of contextual  datasets – Dynamic data can change  its state during an application testing  process. To generate dynamic data, the  tester should be able to input the business  rules and build both positive and negative  test cases. Infosys Next-Gen TDM provides  a configurable rules engine that generates  test data dynamically and validates this  against changing business rules.   7. Image and audio file generation – Infosys  Next-Gen TDM can create audio files and  image datasets for AR/VR testing using  deep learning capabilities.<|endoftext|>8. Special file formats – Customers need  access to special communication formats  such as JSON, XML, and SWIFT, or specific  ones such as EDI files. Infosys Next-Gen  TDM provides templates for generating  various file formats.<|endoftext|>9. Intelligent automation – Built-in  connectors for scheduling the processes  of data discovery, protection, and data  generation allows testers to model,  design, generate, and manage their own  test datasets. These connectors include  plug-ins to the CI/CD pipeline, which  integrate data automation and test  automation.<|endoftext|>Future-proof Test Data through Next-gen TDM Innovation Every organization needs simplified testing models that can support a diverse set of data types. This has never been a higher priority.  Infosys Next-Gen TDM supports digital transformation by focusing on 9 key areas of innovation (see Figure 5). The offering leverages the  latest advances from data science in test data management, giving enterprises the right tools to engineer appropriate test data.<|endoftext|>Figure 5. Focus areas of Infosys Next-Gen TDM Model Development Training Testing Discover & Plan Explore Enrich Manage 2. AI Driven  Data  Discovery 1. Tester UX 9. Intelligent Automation 3. Data  Virtualization 4. Data  Provisioning 5. Privacy  Preserving  Synthetic  Data 6. Smart  Augmentation  of Training  Data Sets 7. Image,  Audio 8.  Special
Formats (EDI,  SWIFT) External Document © 2021 Infosys Limited

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 Accelerate through Next-Gen TDM Reference Architecture As organizations look to deliver high-quality applications at minimum cost, they need a test data management (TDM) strategy that supports  both waterfall and agile delivery models. With the rapid adoption of DevOps and increased focus on automation, there is also increasing  demand for data privacy. Enterprises are fast moving from traditional TDM to modern TDM in order to meet the needs of the current  development and testing landscape.  Infosys Next-Gen TDM focuses on increasing automation and improving the security of test data across cloud as well as on-premises data  sources.  Figure 6. Infosys Next-Gen TDM reference architecture Production Non-Production on Premise Data Source Logs Files Non-Production on Cloud Tester Developer ReleaseManager Data Scientist Self Service Portal Next Gen TDM Cloud Apps Commercial Testing Tools CI/CD Pipeline Data Masking Data Discovery Data Generation Gold Copy Data Mining Data Sub-setting Data Virtualization Data Quality Diferential Privacy Automated workfow Data Reservation Data Provisioning Data Generation Data base Refresh Unit and Functional Testing Integration, Regression and Performance Testing External Document © 2021 Infosys Limited

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 1. User experience – Infosys Next-Gen  TDM focuses on building specific data  experiences for each persona, i.e., tester,  release manager, developer, and data  scientist. Its self-service capabilities  offer simplified intent-driven design  for better data provisioning and  generation.<|endoftext|>2. Contextual test data generation –  There is a library of algorithms that  helps teams generate different data  types and formats including images,  EDI files, and other unstructured data.<|endoftext|>Figure 7. Contextual test data and


Continue # Infosys Whitepaper
 its different formats The focus areas in digital transformation through this approach are: 3. Data protection for multiple data  sources – Infosys Next-Gen TDM  connects to multiple data sources on  cloud and on-premises. It provides a  framework of reusable components for  gold copy creation and sub-set gold  copy. Data is masked and protected  through a library of algorithms for  various data types.<|endoftext|>4. Data augmentation – The accuracy of  AI and ML algorithms depends on the  quality of training data and the scale  of data used. The larger the volume  and more diverse the training data  used, the more accurate and robust  the model will be. Infosys Next-Gen  TDM generates high volumes of data  based on a predefined data model, data  attributes, and patterns of data variation  for training, validating, and testing AL/ ML algorithms.<|endoftext|>5. Integration through external tools – To  enable full-fledged DevSecOps, Infosys  Next-Gen TDM has a library of adaptors  that connect to the various orchestration  tools in the automation pipeline.<|endoftext|>Diferential privacy & resistance to reconstruction Data protection Generalization Perturbing data Structured data Pre-set Files Unstructured Data Images Communication
Format Provide structured data for analytics Data generation of fles Logs and chat transcripts Provide images for UX testing / AR-VR Kits XML, JSON, SWIFT External Document © 2021 Infosys Limited

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 The Way Forward: Building  Evolutionary Test Data for  Your Enterprise  Production and synthetic test data can  coexist in a testing environment, either  to optimize their role in various testing  operations or as part of a transition from  one to the other. This may require the  organization to think differently about  test data and develop a roadmap for  long-term continuous testing. To solve  test data challenges, enterprises should  focus on using evolutionary architecture  to build contextual test data using a  three-pronged strategy: •  AI-assisted data prep: Fitness  functions – Focus on identifying  the key dimensions of data that  need to be generated for testing.  Enhance feature engineering across  multi-role teams to build the key  fitness functions and models for data  generation across each data domain  and data type.<|endoftext|>•  Focus on incremental change – Help  data architects focus on incremental  change by defining each stage  of test data management based  on the tester’s experience. This  will enable testers to selectively  pick the right data for different  deployment pipelines running on  different schedules. Partitioning  test data around operational goals  allows testers to track the health and  operational metrics of the test data.  •  Immutable test data suite – Focus  on building an immutable test data  environment with best-of-breed tools  and in-house innovation to ensure  the right tool choice for test data  generation. This helps enterprises  choose the tools best suited to their  need, thereby optimizing total cost of  ownership (TCO).<|endoftext|>External Document © 2021 Infosys Limited {{ img-description : two businesspeople standing in a rack of servers, in the style of found-object-centric, night photography, metalworking mastery, studyplace, ferrania p30, security camera, 32k uhd (to right) }}

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 © 2021 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com  Infosys.com | NYSE: INFY Stay Connected About the
Authors Avin Sharma       Consultant at Infosys Center for Emerging Technology Solutions (ICETS) He is currently part of the product team of Infosys Enterprise Data Privacy Suite, Data for Digital ICETS. His focus includes product  management, data privacy, and pre-sales.<|endoftext|>Ajay Kumar Kachottil       Technology Architect at Infosys with over 13 years of experience in test data management and data validation services.<|endoftext|>He has implemented multiple test data management solutions for various global financial leaders across geographies.<|endoftext|>Karthik Nagarajan        Industry Principal Consultant at Infosys Center for Emerging Technology Solutions (ICETS).<|endoftext|>He has more than 15 years of experience in customer experience solution architecture, product development, and business development.  He currently works with the product team of Infosys Enterprise Data Privacy Suite, Data for Digital ICETS, on data privacy, data  augmentation, and CX strategy.<|endoftext|>

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# Infosys Whitepaper
Title: Test data management in software testing life cycle- Business need and benefits in functional, performance, and automation testing
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER TEST DATA MANAGEMENT IN  SOFTWARE TESTING LIFE CYCLE- BUSINESS NEED AND BENEFITS IN  FUNCTIONAL, PERFORMANCE,   AND AUTOMATION TESTING Praveen Bagare (Infosys) and Ruslan  Desyatnikov (Citibank)

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 Abstract The testing industry today is looking  for ways and means to optimize  testing effort and costs.  One potential  area of optimization is test data  management. Testing completeness  and coverage depends mainly on the  quality of test data. It stands to reason  that without high quality data testing  assurance is unattainable. A test plan  with several comprehensive scenarios  cannot be executed unless appropriate  data is available to run the scenarios.<|endoftext|>The best data is found in production  since these are actual entries the  application uses. While using  production data, it is always prudent  to create a sub-set of the data. This  reduces the effort involved in test  planning and execution and helps  achieve optimization.  However, live  data is not always easily available for  testing. Depending on the business,  privacy and legal concerns may be  associated with using live data. Often  the data is not complete and therefore  cannot be used for testing.  It is best to  avoid the use of raw production data  to safeguard business and steer clear  of expensive lawsuits.  The challenge of TDM lies in obtaining  the right data effectively. Before  proceeding on this path, we need  to find answers to some pertinent  questions: Will there be a positive  return on the investment? Where  do we start implementing Test  Data Management (TDM)? Should  we start with functional testing or  non-functional testing?  Can test  automation help?   The practice of not including TDM  steps in the Testing Life Cycle often  leads to ignorance towards TDM on  part of the testing team. This paper  attempts to explain why testers in  the functional, non-functional and  automation test arenas need the  TDM service. We also discuss the test  data challenges faced by testers and  describe the unparalleled benefits of a  successful TDM implementation.<|endoftext|>TDM is fast gaining importance in the  testing industry. Behind this increasing  interest in TDM are major financial losses  caused by production defects, which could  have been detected by testing with the  proper test data. Some years ago, test  data was limited to a few rows of data in  the database or a few sample input files.  Since then, the testing landscape has come  a long way. Now financial and banking  institutions rely on powerful test data sets  and unique combinations that have high  coverage and drive the testing, including  negative testing. TDM introduces the  structured engineering approach to test  data requirements of all possible business  scenarios.  Large financial and banking institutions  also leverage TDM for regulatory  compliance. This is a critical area for these  institutions due to the hefty penalties  associated with non-compliance. Penalties  for regulatory non-compliance can run into  hundreds of thousands of dollars or more.  Data masking (obfuscating) of sensitive  information and synthetic data creation  are some of the key TDM services that can  assure compliance.<|endoftext|>Significance of Test Data Management External Document © 2018 Infosys Limited

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 Test data is any information that is used  as an input to perform a test. It can  be static or transactional. Static data  containing names, countries, currencies,  etc., are not sensitive, whereas data  pertaining to Social Security Number  (SSN), credit card information or medical  history may be sensitive in nature.  In addition to the static data, testing  teams need the right combination of  transaction data sets/conditions to test  business features and scenarios.  TDM is the process of fulfilling the test  data needs of testing teams by ensuring  that test data of the right quality is  provisioned in suitable quantity, correct  format and proper environment, at the  appropriate time. It ensures that the  provisioned data includes all the major  flavors of data, is referentially intact and  is of the right size. The provisioned data  must not be too large in quantity like  production data or too small to fulfill all the  testing needs. This data can be provisioned  by either synthetic data creation or  production extraction and masking or by  sourcing from lookup tables.<|endoftext|>TDM can be implemented efficiently with  the aid of well-defined processes, manual  methods and proprietary utilities. It can  also be put into practice using well-evolved  TDM tools such as Datamaker, Optim or  others available in the market.<|endoftext|>A TDM strategy can be built based on the  type of data requirements in the project.  This strategy can be in the form of: •  Construction of SQL queries that  extract data from multiple tables in the  databases • Creation of flat files based on:    Mapping rules      Simple modification or desensitizing  of production data or files     An intelligent combination of all of  these What is Test Data  Management? External Document © 2018 Infosys Limited

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 Some of the most common challenges  faced by testing teams while sourcing test  data are: •  Test data coverage is often incomplete  and the team may not have the required  knowledge.<|endoftext|>•  Clear data requirements with volume  specifications are often not gathered  and documented during the test  requirements phase.<|endoftext|>•  Testing teams may not have access  to the data sources (upstream and  downstream).<|endoftext|>•  Data is generally requested from the  development team which is slow to  respond due to other priority tasks.<|endoftext|>•  Data is usually available in large chunks  from production dumps and  can  be sensitive in nature, have limited  coverage or may be unsuitable for the  business scenarios to be tested.<|endoftext|>•  Large volumes of data may be needed  in a short span of time and appropriate  tools may not be at the testing team’s  disposal.<|endoftext|>•  Same data may be used by multiple  testing teams, in the same environment,  resulting in data corruption.<|endoftext|>•  Review and reuse of data is rarely  realized and leveraged.<|endoftext|>•  Testers may not have the knowledge of  alternate data creation solutions using a  TDM tool.<|endoftext|>•  Logical data relationships may be hidden  at the code level and hence testers may  not extract or mask all the referential data.<|endoftext|>•  Data dependencies or combinations to  test certain business scenarios can add  to the difficulties in sourcing test data.<|endoftext|>•  Testers often spend a significant amount  of time communicating with architects,  database administrators, and business  analysts to gather test data instead  of focusing on the actual testing and  validation work.<|endoftext|>•  A large amount of time is spent in  gathering test data.<|endoftext|>•  Most of the data creation happens  during the course of execution based on  learning.<|endoftext|>•  If the data related to defects is not found  during testing, it can cause a major risk  to production.<|endoftext|>The Challenges of Test Data  Sourcing External Document © 2018 Infosys Limited

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 TDM Offers Efficient Solutions and  Valuable Benefits  An effective TDM implementation can address most of the challenges mentioned above. Some of the key benefits  that a business can gain by leveraging the TDM services are: Superior quality Minimum time Reduced cost Less resources •  Optimal data coverage is  achieved by the TDM team  through intelligent tools and  techniques based on data  analysis strategies •  The TDM service employs a  dedicated data provisioning  team with agreed service-level  agreements (SLAs) ensuring  prompt data delivery •  Condensed test design and  data preparation effort helps  achieve cost savings •  Database or file access provided  to the TDM team facilitates data  privacy and reuse  •  Test data requirements from  the TDM team enable the  testing team to capture these  effectively during the test  planning phase. Version- controlled data requirements  and test data ensure complete  traceability and easier  replication of results •  Compact test design and  execution cycles can be  achieved for reduced time to  market •  Minimized test data storage  space leads to reduction of  overall infrastructure cost •  Professionals with specialized  skills, sharp focus on Test  Data and access to industry  standard tools contribute to  the success of TDM •  Detailed analysis and review  of data requirements ensure  early identification of issues  and resolution of queries •  Automated processes lead to  less rework and reduced result  replication time •  The TDM team also wears  the system architect’s


Continue # Infosys Whitepaper
 hat,  thus understanding data  flow across systems and  provisioning the right data •  Synthetic data can be created  from the ground up for new  applications •  TDM tools such as Datamaker  can speed up scenario  identification and creation of  the corresponding data sets •  Errors and data corruption  can be reduced by including  defined TDM processes in  the Testing Life Cycle and by  adopting TDM tools •  Clear data security policies  increase data safety and  recoverability •  Well-defined process and  controls for data storage,  archival and retrieval support  future testing requirements External Document © 2018 Infosys Limited

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 Coverage:  Exposure to all the possible  scenarios or test cases needs to be the  key driving factor for data provisioning in  functional testing. The data provisioned for  this testing must cover: •  Positive scenarios (valid values that  should make the test case pass)  •  Negative scenarios (invalid values that  should result in appropriate error handling) •  Boundary conditions (data values at the  extremities of the possible values) •  All functional flows defined in the  requirement (data for each flow) Low volumes: Single data sets for each of  the scenarios are sufficient for the need.  Repetitive test data for similar test cases  may not be required and can prove to be  a waste of time. This can help reduce the  execution time significantly.<|endoftext|>High reuse: Some test data such as accounts,  client IDs, country codes, etc., can be reused  across test cases to keep the test data pool  optimized. Static and basic transaction data  for an application can be base-lined so as  to be restored or retrieved for maintenance  release testing at regular intervals, depending  on the release frequency.<|endoftext|>Tools or utilities: As utilities have the  capacity to create large volumes of the  same type of data, they may not be very  useful in functional data preparation. If  the tool is capable of creating a spectrum  of data to meet all the data requirements  and the data can be reused across releases,  then it is beneficial to use the utility or tool  for TDM implementation.<|endoftext|>Optimal Environment Size Data in the testing environment should be a  smart subset (a partial extract of the source  data based on filtering criteria) of production  or synthetic data. It should comprise data for  all testing needs and be of minimal size to  avoid hardware and software cost overhead.  Sub-setting referentially intact data in the  right volume into testing environments is  the solution to overcome the problem of  oversized, inefficient and expensive testing  environments. This is the key to reduce the  test execution window and minimize data- related defects with the same root cause.<|endoftext|>Data Requirements Gathering  Process  During test case scripting, the test data  requirements at the test-case level should  be documented and marked as reusable  or non-reusable. This ensures that the test  data required for testing is clear and well  documented. A simple summation of the same  type of test data provides the requirement of  test data that needs to be provisioned.<|endoftext|>A sample test data requirement gathering,  as per this method, has been demonstrated  in Appendix I.  Feasibility Check TDM is suitable for functional testing  projects that: •  Spend over 15% of the testing effort on  data preparation or data rework •  Use regression test cases which are run  repeatedly across releases (so that the test  data methodology identified can be reused)  •  Indicate that a high data coverage TDM  solution can be identified and implemented TDM in Functional Testing Most challenges mentioned earlier are part of the day-to-day struggle of functional  testing. The most commonly seen challenges are: low coverage, high dependency, access  limitations, oversized testing environment, and extended test data sourcing timelines.  Successful implementation of TDM in functional testing projects can alleviate most of  these issues and assure the completeness of testing from the business perspective.<|endoftext|>In functional testing, TDM is governed by numerous factors highlighted below.<|endoftext|>External Document © 2018 Infosys Limited

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 Test data preparation in performance  testing is most impacted by issues  like large volume requirements  with significant coverage, high data  preparation time, limited environment  availability and short execution windows.  The TDM team with their tools and  techniques can provide solutions for  bulk data generation with quick refresh  cycles ensuring on-time high quality  data provisioning to address these steep  demands.<|endoftext|>Test data for performance testing is  characterized by:.<|endoftext|>High volumes of data: Performance  testing always needs large volumes of test  data by virtue of the way it works. Multiple  users are simultaneously loaded onto an  application to run a flow of test executions.  Hence multiple data sets are required in  parallel, in large numbers, based on the  workload model.  Quick consumption of test data: Since  the load or stress on the application  is induced by multiple users, the data  provisioned for them is consumed rapidly.  This leads to quick exhaustion of large  volumes of the test data.<|endoftext|>Very short data provisioning cycle:  Since the data is consumed very quickly  in performance testing, a new cycle  of execution requires the data to be  replenished before start. This implies that  the TDM strategy must make sure that  the test data can be recreated, extracted  again or provisioned in a short period  of time so as to avoid the impact of the  unavailability of data on the testing cycle.<|endoftext|>Workload distribution: Performance  testing in today’s time does not use one  type of data repeatedly. If the use cases  comprise multiple types of data, with  a corresponding percentage of each  occurrence, a similar workload model is  built for the testing environment. Thus  smart data needs to be provided for such a  workload model which covers the multiple  types. This multiplies the complexity of  data creation.<|endoftext|>Feasibility Check TDM is definitely suitable for most of  the performance testing projects. The  strategy needs to be well designed to  make sure that the challenges listed  above are addressed appropriately and a  satisfactory return on investment (ROI) is  achieved soon.<|endoftext|>TDM in Performance Testing  External Document © 2018 Infosys Limited

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 Some of the biggest challenges in  automation testing are associated  with creating test data. Included in this  challenge is the inability to create data  quickly from the front-end, fast burning up  of large volumes of data during test runs,  limited access to dynamic data and partial  availability of the environment.  Implementing a well-designed TDM strategy  can support multiple iterations of dynamic  data in short intervals of time by synthetically  creating or extracting data, using TDM tools.<|endoftext|>Test data for automation testing is driven  by factors such as: Automation of test data creation: The  data required for automation testing  is usually created by using one of the  automated processes, either from user  interface (UI) front-end or via create or edit  data operations in the database. These  methods are time-consuming and may  require the automation team to acquire  application as well as domain knowledge.<|endoftext|>Fast consumption of test data: Similar to  performance testing, automation testing  also consumes data at a very quick pace.  Hence the data provisioning strategy must  accommodate fast data creation with a  relatively short life cycle.<|endoftext|>High coverage: As with functional testing  automation testing also requires test data  for each of the automated scenarios. The  data requirement may be restricted to the  regression test pack and yet covers a large  spectrum of data.<|endoftext|>Feasibility Check TDM as process implementation is certainly  recommended for an automation project.  The automation team must provide the  data requirement in clear terms and  the TDM team can ensure provisioning  of this data before the test run begins.  Automation tools have abilities to create,  mock and edit data but the TDM team’s  expertise and tools can add significant  value to the proposition.<|endoftext|>TDM in Automation Testing External Document © 2018 Infosys Limited

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 Conclusion In functional testing, increasing data  coverage plays a significant role in  providing the TDM value-add. The sheer  volume of test data that is repeatedly  used in the regression suites make it  an important focal area from the ROI  viewpoint. The right TDM tools can help  provision a spectrum of data and ensure  continuous ROI in each cycle.<|endoftext|>TDM implementation in performance  testing projects can deliver quick benefits  and the improvement can be significantly  highlighted as large volumes of similar  data can be created swiftly and efficiently.  Automation testing can benefit from TDM  implementation. Tools such as Quick Test  Professional (QTP) can create data via user  interface but need significant functional  knowledge and are slow in nature. TDM  solutions can save time and cost by  keeping the data ready.  Robust data  creation methods and tools can be used to


Continue # Infosys Whitepaper
  achieve these goals.<|endoftext|>Based on our learning, interactions and  experience gleaned from the TDM world,  we can confidently affirm that functional,  automation and performance testing  can leverage TDM implementation to  overcome their respective challenges  and achieve optimization.  Each type  of testing presents its own unique  challenges and benefits, but there is  a common theme – TDM is a major  enhancement and addition to the tools  and techniques available to the testing  team.  This practice can help realize gains  at the bottom-line with cost reductions,  improved turnaround time and fewer  data related defects in test and in  production.<|endoftext|>External Document © 2018 Infosys Limited

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 We have identified the following set  of metrics to be captured before and  after implementing TDM practices to  measure the ROI and benefits: Test data coverage Percentage of test data  related defects Test data management  effort (percentage and  time in hours) We wish you success in your TDM  implementation.<|endoftext|>External Document © 2018 Infosys Limited

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 An illustration of ‘Sample Test Data Requirement Gathering’ The last three columns depicted in the table below should be a part of the test case documentation and must be updated during test case  authoring.<|endoftext|>Data requirement summary for the above illustration: • Two ‘NAM Bank Account Number’ ‘with balance > $100,000’ • Two ‘ASIA Bank Account Number’ • One client ID Reference http://www.gxsblogs.com/wp-content/blogs.dir/1/files/SEPA-Penalties-Table1.png?8f0a21 Appendix I No.<|endoftext|>Test Case Test Data Requirement Reusable? Remarks 1 Test Case 01 NAM Bank Account Number Y with balance > $1,000 2 Test Case 02 Client ID N - 3 Test Case 03 NAM Bank Account Number Y with balance > $100,000 4 Test Case 04 ASIA Bank Account Number Y Account open date > 01 Jan 2013 5 Test Case 05 ASIA Bank Account Number Y - External Document © 2018 Infosys Limited

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 © 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected

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# Infosys Whitepaper
Title: Test Factory Setup for SAP Applications
Author: Infosys Limited
Format: PDF 1.7

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 PERSPECTIVE TEST FACTORY SETUP FOR SAP  APPLICATIONS - Barry Cooper-Brown, Diageo Chandur Ludhani and Sailesh Chandrasekaran,  Infosys Abstract Emergence of IT enabled business growth is compelling  organizations to give testing and testing related strategies the much  needed importance. By now, it has been reaffirmed that the cost of a  single development defect can snowball to many times the original  cost, if not discovered until the QA phase of testing and eventually  showing up in the Production Environment.<|endoftext|>However, when it comes to SAP there are unique testing challenges  to deal with. The challenges of SAP testing will present you with  both tradeoffs that need to be considered and the choices that need  to be made about the kind of testing that is needed for your QA  organization. The following point-of-view has been written based on  the engagement between a major drinks manufacturer and Infosys,  describing a successful approach of setting up a Test Factory to  manage testing of SAP applications.<|endoftext|>

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 Challenges in managing changes in SAP application which indirectly gets mapped  to reaping benefits for all its stakeholders  namely the customers, employees,  shareholders, etc. Often these investments  do not bear fruits and are in turn viewed  as a cost. For example, a delay in readying  the application for regulatory changes,  could lead to serious consequences for the  organisation in the region within which  the changes were mandated. Another  such example could be with encountering  incidents in the live environment or facing  downtime with particular applications  leading to severe business disruption.   Organisations are constantly on the  lookout for innovative ways to help adapt  quickly to these changes in the SAP  business processes. Their ability to do so  also facilitates: • Improvement of  delivery confidence  with every change deployed • Reduction in cost of every change  implemented  • Ability to contract the overall lead time  required for such activities thereby  allowing more frequent releases An organisation’s inability to do so, leads  to a host of challenges to the employees  who interface with SAP for their day-to-day  activities: • Long time for deploying the SAP  changes, means more business  application downtime • Leakage of defects to production–  hampering their day to day operations   • High number of defects detected  during User Acceptance Testing (UAT)  resulting in a delay of final application  go-live.  The question therefore is whether  there is a single and efficient solution  available to organisations in managing  their SAP related QA/Testing operations,  inexpensively and efficiently? The concept of Test Factory and the  offering under the business tag of NEM  (New Engagement Models) is gaining  traction across the globe. The Test Factory,   also an alias for Managed Test Service or  Testing Centre of Excellence, acts as an  independent function in the SDLC; whilst  supplanting the existing set of processes  with benefits of a more agile, efficient and  repeatable set of processes.<|endoftext|>In our next section, we explore the  business needs of SAP enabled  organisations to deploy a Test Factory  model setup for their QA organisation.<|endoftext|>Today’s unrelenting economic and financial  pressures, coupled with far too many  internal and external factors (beyond  the control of any organisation’s circle of  influence), are compelling organisations to  change and tailor their business processes  accordingly. These changes, most often  than not, necessitate a change in the very  supporting business applications itself  (example, SAP, Oracle, etc.)   Acquisition of new business, opening  of new facilities, introduction of new  business line, consolidation of service  lines, Regulatory Changes like changes  in tax laws, changes in reporting needs,  etc., incorporating these changes to SAP is  fairly complex. Any update, even if major,  moderate or minor, needs to be completely  analysed from several dimensions  keeping in mind that these are all large  business applications. The result is that  organisations end up managing projects  involving a high number of interrelated  or moving parts. Further, SAP applications  span geographies and often need heavy  customisations to suit local requirements  (like government, language, etc.) and have  multiple vendors running the applications.  Every investment that an organisation  makes in its IT systems is channelled  towards ensuring a smooth running  External Document © 2018 Infosys Limited

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 Understanding the Business Need • Inconsistent in application test quality  across teams and  • Lack of usage of appropriate testing  tools 2. Governance Challenges Organisations running SAP are  continuously engaged in taking up  large change programmes or rolling out  applications to newer regions. A large  change programme or rollout requires the  QA function to constantly generate status  reports and deal with various risks and  issues.<|endoftext|>Absence of defined processes, metrics  to track progress, risk management and  inability to consolidate reports frequently,  leads to a governance challenge with  respect to managing decentralised  QA teams. Another common problem  encountered with decentralised QA teams  is with the large amount of time consumed  in assimilating and consolidating  information for status reporting from  various regional teams and resolution  of risks and issues. Further, under the  decentralised structure, teams lack  adoption of uniform processes and hence  there are bound to be differences in the  content and structure of status reporting  and the way risks are identified and   dealt with.  3. High Cost of Testing /  Maintenance Sound testing processes and deep  business knowledge are pre-requisites  to testing of SAP applications. We have  also learnt earlier that a large amount of  effort is spent in running the QA function  for a SAP enabled organisation. Majority  of organisations dedicate a large number  of resources for testing of SAP releases.  In addition to this, SAP testing involves  testing some portions of business  functionalities repeatedly and often  decentralised teams lack the benefits  associated with the reusability aspect.  This can be cited as an additional reason  for the inflated costs of SAP testing in a  decentralised model.<|endoftext|>Project based QA teams primarily look at  testing from a very narrow project point of  view and often miss the holistic implication  of the changes from a complete business  landscape perspective. This leads to high  efforts from the business users during  UAT and large number of defects getting  identified in the later stages of testing. In  addition to this, the non-functional testing  aspects such as Performance, Security etc.,  are also overlooked in the initial phases of  testing leading to high amount of re-work  and maintenance costs downstream.<|endoftext|>4. Lower Delivery Confidence and  Higher Time-to-Market  There is very low confidence on delivery  of release considering the testing in the  earlier phases is not really focussed on  business knowledge. This results in a  high percentage of defect identification  in later stages of SDLC. In the absence of  benchmark metrics, there is no opportunity  to measure the test execution productivity,  often leading to increased durations of  testing cycles.<|endoftext|>While these may sound like age old  problems and issues, these are indeed the  common issues across organisations. These  pitfalls are the reasons why organisations  find themselves grappling with an  expensive and a non-yielding QA function.<|endoftext|>Listed below are some of the common  pitfalls encountered by SAP enabled  organisations in running their QA functions - 1. Decentralised Testing This model of testing is usually prevalent  in organisations which have undergone  mergers or made acquisitions. Testing in  such organisations is carried out using a  decentralised model where no common  testing processes and methodologies  exist. Each Line of business (LOB) has its  own processes and differences exist even  within units of the same LOB. In most cases,  testing is managed by the development  team itself, being aligned directly to each  project.<|endoftext|>This model does not provide clear  delineation between the build and test  functions. If there are any inefficiencies  or delays in build, then the same is  compensated for in the testing phase by  either compressing the testing timelines  or by moving forward with inadequate  coverage of business scenarios. Individual  teams often adopt the approach of testing  with a self defined set of testing processes  and scope limited to their project. The  result is the lack of co-ordination when it  comes to delivering together with other  ongoing projects. This not only results in  severe delays in the programme go-live,  but also leads to a severe compromise of  the quality and quantity of testing that  is necessary.<|endoftext|>Most common limitations of this approach: • Lack of defined uniform testing  processes and global governance  based on metrics • Duplication of test effort  • Non – conformance of testing timelines External Document © 2018 Infosys Limited

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 So, What is a Test Factory? Having


Continue # Infosys Whitepaper
 looked at what is ailing SAP based  organisations, setting up a Test Factory can  be the most definitive solution available  in the market currently. Test Factory is  a centralised testing model that brings  together people and infrastructure into  a shared services function adopting  standardising processes, effective usage of  tools, high reuse and optimising resource  utilisation in order to generate required  benefits for the organisation.<|endoftext|>Let us broadly explore the solution that a  Test Factory can provide –  a. Test Factory acts as an independent  function in the SDLC and resolves the  very first ailment by having a clear  delineation between the Build and Test  functions of an organisation.<|endoftext|>b. Test Factory is setup as a centralised  QA function which brings in uniform  process adoption and an enterprise  wide QA approach with easier  governance. Test Factory is also  setup with the attributes of a more  agile, efficient and repeatable set of  processes.  Test Factory can also be operated in the  new engagement model (NEM) format  which helps in measuring the business  value linked to the services offered,  example; pricing is based on the work  performed instead of traditional Time &  Material models.<|endoftext|>Implementing a Test Factory The entire process of implementing a Test  Factory involves 3 major phases – Solution  Definition Solution  Design Solution  Implementation 3 Solution Definition Phase “Building the Case for Organisational Buy- in” One of the most essential starting points  of the entire Test Factory setup involves  assessing the existing organisational  test processes, determining the maturity  level of the processes and deriving the  gaps observed. The solution definition  phase involves arranging for one-on-one  or group interview sessions with various  stakeholders, in the existing ecosystem,  and understanding the various pros and  cons of the existing processes. Alternatively  or additionally, a questionnaire pertaining  to the respective areas of the stakeholders  can be used to help document the same.  For assessing the maturity, organisations  are spoilt for choices with widely known  Test Maturity models such as the TMMi,  TMAP, TPI or the ITMM (Infosys Test  Maturity Model).<|endoftext|>The ITMM is a well-blended model which  builds upon the standard Test Maturity  models and also adds further dimensions  to its fabric in being able to evolve  constantly to the changing business  context.  The assessment results show the current  level of maturity of the organisation’s  processes. It is of utmost importance at  this stage to bring together the leadership  team of the organisation and showcase  the various process improvements and  benefits of moving the organisation to the  higher levels of maturity. On the basis of  the agreed level of maturity to be targeted,  the ITMM model allows for a continuous  improvement process to be imbibed into  the organisation. Once an agreement  is reached, a roadmap is devised on  how the solution is to be designed and  implemented.  Solution Design Phase “Structuring A Winning Solution” The Solution design phase is a core  component in the Test Factory setup  process and involves designing processes  on three dimensions of the ITMM model – • Test Engineering Dimension, covering  the focus areas of Requirements  Gathering, Test Strategy, Testing tools,  Test Data and Environment • Test Management dimension, covering  the focus areas of Estimation, Test  Planning, Communications, Defect  Management and Knowledge  Management  • Test Governance dimension, covering  the focus areas of Test Methodology,  Test policy, Organisational structure  and Test Metrics The most important key areas to focus  would be to design the processes for -  • Test Methodology  Defining various types of testing to  External Document © 2018 Infosys Limited

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 be performed, estimation techniques,  entry and exit criteria for each testing  phase, testing environment set-up,  operating model and various input and  output artifacts • Test Governance  Defining the governance structure  and chalking out clear test roles and  responsibilities • Test Factory Structure  Defining the various communication  paths within and outside the Test  Factory • Metrics, KPIs and SLAs  Defining the various testing related  metrics and ensuring agreement on the  various SLAs and KPIs for each role and  processes in the Test Factory • Knowledge Management Framework  Defining a centralised service to allow  effortless and effective sharing of  knowledge between teams, across  knowledge assets   In addition, teams may create, test data  management processes, a catalogue of  the testing services, non-functional test  services methodology, Guidelines for  various Testing tools and Testing Policies.  The solution design phase is a highly  collaborative process in which the design  and delivery teams play equal roles.  It involves both, fine tuning some of  the current processes and completely  revamping the rest. The implementation  of the solution in the right manner, and  with the right amount of calibration, can  bring about bountiful benefits to the  organisation in having a sound testing  process.<|endoftext|>Solution Implementation Phase “Walk the Talk” Depending on the level of maturity  that organisations choose to attain, this  phase needs a good deal of time to be  invested. The time taken to nurture the  processes and imbibe them could range  anywhere from six months up to two years,  depending on the organisational buy-in  and focus in implementing the same.   Having a good amount of time on hand,  organisations also have the option of  choosing to implement the processes in  either a staggered manner or with a big  bang approach. In general, it is advisable  that a staggered approach be chosen.<|endoftext|>In a staggered approach, the  implementation team collaborates with  the champion or manager of the Test  factory prioritising the areas lacking basic  maturity and identifying a pilot release in  which the updated processes can be put  to test. This allows the implementation  team to lay out checkpoints where any  anomalies can be corrected. At the end  of the pilot implementation, a survey can  be conducted with the stakeholders in  determining the success and failures in the  implementation. The lessons learnt at the  pilot implementation stage are a crucial  input to the next phase of implementation.<|endoftext|>It is important to look at some of the  frequently encountered challenges  associated with the solution  implementation phase -  • Aversion to change  This often is a sticky issue with teams  unwilling to adapt to new processes  as it involves moving away from the  comfort zone • Poorly adapted processes and  communications  This is an indication that the impacted  teams are not aware of new processes  and are not well trained • Handing over testing to Test Factory  Traditional approach of testing by  business users due to lack of business  knowledge by testing team is one  of the most challenging change  management aspects to deal with It is therefore a task for both the  implementation team and the Leadership  team of the organisation in addressing  these challenges / change management.  The task of the implementation team lies  in devising a thorough training plan for  the various teams involved, designing user  manuals and guidelines for any reference  required to the new processes.<|endoftext|>On the other hand, the task of the  leadership team is to put together a strong  communication plan, listing the benefits  that accrue to, both the impacted teams  and the business benefit in adapting  the changes. In certain situations,  grievance redressal efforts and holding  communication forums is a good way to  engage with the teams.<|endoftext|>External Document © 2018 Infosys Limited

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 Figure: Test Maturity Model External Document © 2018 Infosys Limited

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 Benefits of a Test Factory We have managed to explore the story  of the Test Factory setup with the sound  process framework forming the base, but  the real icing on the cake is to see the  benefits that accrue post setting up of the  Test Factory.<|endoftext|>Test Factory brings about a set of both  Qualitative and Quantitative benefits.  Some of the Qualitative and hard hitting  benefits include  • Ensures high levels of repeatability,  predictability and test coverage • Delivers on business requirements  driven end-to-end testing  This helps in identification of critical  defects and requirement gaps which  would have been typically identified  only during UAT phase • Definition of key metrics  Assists in tracking enabling effective  governance at every stage of the  programme  • A reusable set of artifacts and test  design, helping crash test planning and  design timelines • Reduce the UAT phase saving on  delivery timelines • Understanding of core business  processes & building the core  regression library • Effective usage of tools enabling  complete traceability from  requirements to test cases and defects  during various phases of the project • Accelerated test automation helping in  reduction of cycle time and execution  costs • Providing platform to have more  frequent releases annually – This will  benefit the business and end users in  moving away from the erstwhile lower  frequency, which meant having to wait  long for the release of the important  Business/IT changes • One


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-stop shop for various testing  needs – Performance Testing, Security  Testing, UAT Support etc.<|endoftext|>Quantitative benefits include  • An estimated cost saving of 50% owing  to reuse of the test design artifacts • An expected 40% reduction in  execution cost with automation of test  build and  • Near zero defect leakage from System  Integration testing to UAT and Go-Live,  ensuring faster time to market and less  Production downtime.  Conclusion Setting up a Test Factory can give organisations a unique insight into how they can successfully tailor and reinvent the traditional onsite- offshore model for ensuring effective and comprehensive application testing. In addition to the cost benefits, with the adoption of the Test  Factory approach, organisations emerge with an integrated and comprehensive SLA driven QA organisation with tightly knitted processes.  One of the most enterprising advantages with a Test Factory setup is the ability to add further services associated with Testing and without  having to largely tweak the underlying processes framework. This helps the organisation in quickly on-boarding and implementing testing  skills and processes required for the supporting of new business initiatives oriented towards upcoming areas like Cloud, Mobility, Social  Commerce, etc.   The Test Factory model is a welcome addition to the plate of offerings by Service firms and is definitely a force to reckon with in the  foreseeable future.<|endoftext|>External Document © 2018 Infosys Limited

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 About the
Authors Barry Cooper-Brown     Test Manager, Diageo Barry is Test Manager with Diageo plc and is based in London. He has over 20 years of experience working with SAP across many disciplines  from Basis to programme delivery. He has worked for over 14 years within the FMCG sector and has specialised in delivery of projects and  programmes of SAP solutions. Currently Barry is the Test Factory Manager for Diageo responsible for the implementation and running of a  Global Test Organisation across Multiple regions and SAP solutions.<|endoftext|>Chandur Ludhani  Principal Consultant, Infosys Chandur is a principal consultant with the Retail, CPG and Life Sciences unit of Infosys and has over 15 years experience. He has experience  on ERP Product development, Testing, Implementation and Support. As part of the testing engagements, he has helped clients in setting up  the processes related to various types of testing services - Manual testing, Automation etc. leading to Testing Centers of excellence.<|endoftext|>Sailesh Chandrasekaran     Senior Consultant, Infosys Sailesh is a senior consultant and has over 6 years of experience working for clients in Retail, Banking and Financial Services industry. He  helps clients in assessing the maturity of their test organizations, improving their testing processes and transforming them into Centers of  Excellence.<|endoftext|>© 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected

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# Infosys Whitepaper
Title: 5G testing holds the key to empower healthcare industry
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER 5G TESTING HOLDS THE KEY TO  EMPOWER HEALTHCARE INDUSTRY Abstract COVID-19 has unleashed uncertainty on organizations, limiting their  visibility and ability to strategize for the future. Technology, though,  continues to evolve and has played a major role in helping deal with this  crisis. When combined with AI and IoT, 5G becomes a potent technology  across industries and domains, bringing unprecedented empowerment  and superior customer service. This paper explores the impact of 5G on  the healthcare industry. It also examines why 5G testing is important when  supporting healthcare services and functions.<|endoftext|>

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 External Document © 2020 Infosys Limited About 5G  Mobile communication has evolved rapidly with changing technologies. 5G represents the latest generation of cellular mobile  communication, characterized by ultra-reliable low latency communication (URLLC), enhanced mobile broadband (eMBB) and massive  machine type communication (mMTC). These capabilities enable data to be transferred at very high speeds with extremely low latency.  As the Internet of Things ecosystem widens, 5G has the ability to support the capture of enormous volumes of data as well as provide  computing power to process the same across billions of devices. This will lead to superior customer experience with unprecedented insights  and capabilities, resulting in a new digital world.  Fig 1: Defining characteristics of 5G Testing 5G in healthcare  5G will become the backbone of  telemedicine or remote healthcare in  the future. Remote healthcare involves  frequent but distant monitoring of  patients using a range of devices that  capture vital parameters. This data must  be continuously transmitted to doctors  for real-time monitoring and assistance.  It also includes video consultations  for diagnosis and precision medicine  prescription, especially for people in care  homes. On a larger scale 5G can improve  overall healthcare capabilities through  innovations like robotic-assisted laser  surgery where doctors can use machines to  perform complex procedures with greater  precision and flexibility.<|endoftext|>Remote healthcare requires devices  as well as apps that enable real-time  patient monitoring. AR/VR can provide an  immersive user experience while artificial  intelligence and machine learning (AI/ML)  provide descriptive, prescriptive and, more  importantly, predictive diagnostics. Each  of these technologies are interconnected.  They often converge to create use cases  that provide next-gen healthcare services.  To ensure their effectiveness, it is critical to  check that these technologies are tested  and certified. Thus, 5G testing is important  to ensure that it can support these  technologies. Let us examine some of the  use cases of 5G testing in the healthcare  industry.  Use case 1: Healthcare  devices Healthcare devices include wearables used  by patients to monitor vital parameters  like heart rate, speech analysis, body mass  index, facial expressions, and more. Real- time video consultations involve using  video devices for consultations, remote- assisted surgeries and training.  Some of the key considerations for testing  these two types of devices are:  • High reliability to ensure uninterrupted  service • Minimal to zero latency for critical  medical procedures like remote-assisted  surgeries z eMBB – enhanced mobile broadband mMTC – massive machine-type communica�on URLLC – ultra-reliable low-latency communica�on 5G drivers 1 million devices per Km2 10 Gbps peak data rate 1 ms latency 10X battery life for low power devices 5G One network – multiple Industries Mobility – 500 km/h high-speed railway 99.999% reliable 99.999% available

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 External Document © 2020 Infosys Limited • Ensuring that sensors monitoring  vital parameters send real-time alerts  to the patient’s/doctor’s handheld  devices • Interoperability testing for many  devices from different vendors  • Compatibility testing to ensure  that the devices can integrate  with applications that are built to  support real-time service, high-speed  performance and superior user  experience Use case 2: Healthcare apps  Medical apps can help deliver a range of  healthcare services covering: • Mental health – These aid in tracking  psychological/behavioral patterns,  substance addiction and emotional  well-being • Patient medication – These apps  provide medication reminders,  maintain medication history,  document online prescriptions, and  more  • Telemedicine – These apps aid  diagnostics, real-time consultations,  and monitoring of patient progress, to  name a few  • Wellness – These apps help maintain  fitness and exercise regimes, diet  prescriptions, monitoring of food  intake, and meditation sessions Some of the key focus areas for testing  healthcare apps are: • User interface and experience (UI/UX)  testing to ensure enhanced customer  experience • Non-functional requirements (NFR)  testing for performance and security  of apps that provide real-time patient  data including large imaging files/ videos on a 5G network  • Crowd testing of apps for varied user  experience and localization • Device compatibility to support a vast  number of devices that will run on 5G Use case 3: AR/VR  AR/VR aims at creating real-time  immersive experiences that can empower  telemedicine and remote patient  monitoring. It requires very high data rate  transmissions and low latencies to deliver  healthcare services covering: • Simulation of different conditions using  sound, vision or 3D image rendering  that can be transmitted from connected  ambulances to operating rooms for  advanced medical care • Real-time training for medical students • Treatment of patients with various  phobias • Early detection of diseases • Various types of therapies to support  physical and mental wellbeing Some of the key focus areas for testing  include: • Checking that the networks can support  real-time immersive experience through: • High speed data transfer rates • Ultra-low-level latency with no lag in  the experience • High bandwidth • Checking that a range of hardware  devices will work with a user’s  smartphone to create a good mobile VR  experience Use case 4: AI/ML It is evident that, in the near future, AI/ML  will play a significant role across healthcare  functions and services by helping diagnose  illnesses earlier and prescribing the right  treatment to patients. 5G will be critical in  enabling healthcare functions that involve  analyzing massive volumes of data. These  healthcare functions include: • AI-based imaging diagnostics that  provide doctors with insights about  diseases and severity • ML-enabled digital biomarkers that  analyze and enable early and accurate  detection of Alzheimer’s and dementia • Assisting in clinical trials and research to  observe patient responses to new drugs  and their behavior patterns • Collecting large volumes of critical  patient data from healthcare apps  and devices to predict the occurrence  of potential diseases through ML  algorithms Some of the key focus areas for testing  include: • Testing for cognitive features like speech  recognition, image recognition, OCR, etc.<|endoftext|>• Implementing robotic process  automation (RPA) for common functions  like recurring medication prescriptions,  appointments and periodic medical  reports.  • Big data testing for both structured and  unstructured data. As large volumes of  data are transmitted and received from  every device, app and equipment, it will  need to be ingested and stored to derive  insights that aid in next-gen predictive  and preventive healthcare  Testing on cloud In addition to the above test focus  areas, cloud testing will be a common  test function for the entire healthcare  ecosystem.<|endoftext|>Cloud can support the core network  through network functions virtualization  (NFV). It can also enable software-defined  networking (SDN), a must-have capability  for 5G. Moreover, cloud will also host the  various healthcare devices and equipment  in the healthcare ecosystem. Thus, cloud  testing will be critical to ensure smooth  performance including functional, non- functional and network testing.<|endoftext|>These use cases provide an overview  of the impact of 5G in healthcare with  a focus on hospitalization, preventive  healthcare, device monitoring, and patient  wellbeing. There are other areas such as  pharmaceuticals, insurance, compliance,  etc., that will also leverage 5G to deliver  their services, creating a connected  healthcare ecosystem.<|endoftext|>

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 © 2020 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may


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 be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com  Infosys.com | NYSE: INFY Stay Connected References https://www.cnet.com/news/covid-19-has-pushed-health-care-online-and-5g-will-make-it-better/ https://www.uschamber.com/series/above-the-fold/how-innovation-accelerating-meet-coronavirus-challenges https://www.pwc.co.uk/communications/assets/5g-healthcare.pdf https://www.ericsson.com/en/blog/2018/6/why-compatibility-and-5g-interoperability-are-crucial-for-success https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029126/ https://www.scnsoft.com/healthcare/mobile/patient-apps https://pharmaphorum.com/digital/four-ways-ai-and-machine-learning-will-transform-healthcare-in-2020/ Conclusion 5G is emerging as the backbone of future  healthcare, catering to a wide range of  healthcare services from ambulances to  operating theatres, apps to diagnostic  equipment, physical illnesses to mental  wellbeing, and more. Technologies like  cloud, AR/VR and AI/ML will play a key  role in driving a technological revolution  within healthcare. As technologies and  equipment for monitoring and diagnostics  become more sophisticated, 5G will act  as the high-speed expressway,  allowing devices to exchange  data at much faster speeds. 5G  will support a variety of remote  healthcare use cases such as early  disease detection, at-home patient  monitoring, precision surgeries, and  distant medical training, to name a  few. Thus, 5G testing will be crucial to  ensure that it unlocks the potential of  modern technologies in healthcare.<|endoftext|>About the
Author Sumanth Dakshinamurthy  Principal Consultant, Infosys Validation Solutions

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