# Infosys Whitepaper
Title: The future of enterprise test automation
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER RPA: THE FUTURE OF ENTERPRISE  TEST AUTOMATION

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 RPA: The future of enterprise test automation If one word defines the 21st century,  it  would be speed. Never before has progress  happened at such a breakneck pace.  We have moved from discrete jumps of  innovation to continuous improvement and  versioning. The cycle time to production  is at an all-time low. In this era of constant  innovation where the supreme need is to  stay ahead of the competition and drive  exceptional user experiences, product  quality deployed in production is paramount  to ensure that speed does not derail  the product as a whole. A robust testing  mechanism ensures quality while allowing  faster release and shorter time to market –  so essential for that competitive edge.   Today, USD 550bn is spent on testing and  validation annually. It is also the second  largest IT community in the world. That is a  significant investment and effort being put  into this space already, but is it delivering  results? In the past five or so years, there has  been a push from CXOs, based on  recommendations from industry experts  and analysts, to go for extreme automation.  Companies have been adopting multiple  tools, opensource technologies, and  building enterprise automation frameworks.  This attempt to achieve end-to-end  automation has created a mammoth  network of tool sets in the organization that  may or may not work well with each other.  This is how test automation was done  conventionally; it still  requires elaborate  effort to build test scripts, significant  recurring investment for subscription  and licensing, and training and knowhow  for multiple tools. By some estimates,  traditional testing can take up to 40% of  total development time – that is untenable  in the agile and DevOps modes companies  operate in today.  What if this ongoing effort can be  eliminated? What if the need for multiple  tools can be done away with? Enter Robotic  Process Automation (RPA) in testing. While  originally not built for testing, RPA tools  show great potential to make testing more  productive, more efficient, and help get  more features to the market faster – giving  them an edge over conventional tools (see  Fig 1).   The state of testing and validation in the enterprise  Product features Traditional automation tools RPA Tools Coding Knowledge •  Coding knowledge is essential to  develop automated scripts •  Programming knowledge and effort is  needed to build the framework, generic  reusable utilities and libraries •   These tools offer codeless automation. Developing automated  scripts requires some effort for configuration and workflow  design. However, coding is minimal compared to traditional  tools •   Generic reusable utilities are available as plug-and-play  components Maintenance Extensive maintenance effort required Minimal  test maintenance effort required Cognitive automation No support for cognitive automation RPA tools are popular for supporting cognitive automation by  leveraging AI Plugin support Limited plugins are available for different  technologies Plugins are available for all leading technologies Orchestration and load  distribution Load distribution during execution requires  additional effort to develop the utilities   and set up the infrastructure This feature is available in most RPA tools. For example, feature  of a popular RPA tool helps in load distribution during execution  without any additional effort aside from configuration Automation development  Productivity Test development productivity is low since  custom coding is required most of the time Test development productivity is high as most generic activities  are available as plug-and-play OCR for text recognition This feature is not available This feature is available in all RPA tools Advanced image  recognition This feature is not available. Either  additional scripting or a third-party tool is  needed to support this This feature is available in all RPA tools In-built screen and data  scarping wizards This feature is not available and requires  integration with other tools This feature is available in all RPA tools Fig. 1: Traditional automation tools vs. RPA External Document © 2020 Infosys Limited

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 External Document © 2018 Infosys Limited RPA – the next natural  evolution of testing  automation  In the last decade, automation has  evolved and matured with time along  with changing technologies. As discussed,  automation in testing is not new but  its effectiveness has been a challenge –  especially the associated expense and lack  of skill sets. RPA can cut through the maze  of tool sets within an enterprise, replacing  them with a single tool that can talk to  heterogenous technology environments.   From writing stubs to record and playback,  to modular and scriptless testing,  and now to bots, we are witnessing a  natural evolution of test automation. In  this 6th Gen testing brought about by  RPA orchestration, an army of bots will  drastically change the time, effort, and  energy required for testing and validation.  We are heading towards test automation  that requires no script, no touch,  works across heterogenous platforms,  creates extreme automation, and allows  integration with opensource and other  tools.<|endoftext|>According to Forrester¹, “RPA brings  production environment strengths to  the table.” This translates into production  level governance, a wide variety of use  cases, and orchestration of complex  processes via layers of automation.  RPA allows companies to democratize  automation very rapidly within the testing  organization.  RPA has an advantage over traditional tools  in that it can be deployed where they fail  to deliver results (see Fig 2). For instance,  when:  • the testing landscape is heterogenous  with complex data flows • there is a need for attended and  unattended process validation  • there is a need to validate digital system  needs An RPA solution can bring in a tremendous  amount of simplicity for building out bots  quickly and deploying them with the  least amount of technical know-how and  skills that even business stakeholders can  understand.<|endoftext|>External Document © 2020 Infosys Limited

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 However, the challenges in testing are not  limited to writing and executing test cases.  The automation needs to also handle the  periphery of testing activities – validating  that all components of the environment  are up and running and that test data is  available on time. This dependency on  the peripheral activities, and the teams  running them, could cost valuable time.  For instance, for a large banking client, this  dependency put a lot of pressure on the  testing team to finish a sprint in 5 days.  Using RPA, we were able to automate the  batch monitoring and batch rendering  process. We also automated synthetic test  data creation and data mining processes   reducing time to market by 40%.  To really provide value for testing and  validation, RPA needs to provide some very  testing specific capabilities such as:  A Cohesive Automation Platform:  Enabling enterprises to leverage the full  potential of automation with process  discovery, process automation (attended,  unattended, and UI based), combined with  process orchestration capabilities. This  should include a test automation interface  that can bridge the gap between test  management tools and automated test  cases. A workflow-driven test automation  approach can make the solution business- centric.  Native AI Capabilities:  A cognitive  engine can leverage various data sources  to deliver pervasive intelligence across  process design, management, and  execution.<|endoftext|>Security and Scalability: The solution  should allow running multiple bots on a  single virtual machine, have robust access  management with a credential vault built  into the product, and offer out-of-the-box  technology-specific adaptors Fig 2. How RPA tools address traditional testing challenges Challenge Areas Performance of RPA tools Test Data Management Data-driven testing is supported by many traditional tools. RPA can manage data  form files like Excel/JSON/XML/DB and use these for testing Testing in different environments End-to-end business processes navigate through various environments like  mainframe/web/DB/client server applications. RPA tools can easily integrate this  process across multiple systems. Thus, RPA tools simplify business orchestration and  end-to-end testing compared to other testing tools Traceability While RPA tools do not directly provide test script traceability, there are methods to  enable this functionality. For instance, user stories/requirements stored in JIRA can  be integrated with RPA automation scripts using Excel mappings to create a wrapper  that triggers execution Script versioning A batch process can be implemented in the RPA tool to address this CI-CD integration This is available in most of the RPA Tools Reporting and defect logging RFA tools have comprehensive dashboards that showcase defects that can be logged  in Excel or JIRA through a suitable wrapper Error handling This feature is available in all RPA tools External Document © 2020 Infosys Limited


Continue # Infosys Whitepaper

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 AssistEdge for Testing in  Action  One of our clients, a large investment  company based in Singapore realized  the benefits of RPA based testing  when it helped them save 60% of  testing efforts. They were running  legacy modernization as a program  using mainframe systems that are  notoriously difficult to automate  using traditional automation tools.  RPA with its AI and OCR capabilities  and the ability to traverse through  and handle any technology, was easily  able to automate 800+ test cases in  the mainframe.<|endoftext|>In another instance, a large banking  client was using package-based  applications that used multiple  technologies to build different  screens. It becomes difficult to  integrate multiple tools in this  scenario. With RPA, we were able to  automate the end-to-end workflow  for each application using just one  tool. This helped reduce the overall  maintenance effort by over 30%.  Another one of our clients was  facing a quality assurance (QA)  challenge where bots were being  deployed without testing. We  developed specific QA bots with  added exceptional handlers to check  whether the bot is actually handling  exceptions and if it fails then how it  comes back to the original state. By  validating the bots, we improved the  overall efficiency by 30%.<|endoftext|>External Document © 2020 Infosys Limited

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 Take advantage of the Edge  The COVID-19 pandemic has accelerated  the organizations’ need to be hyper- productive. Companies are realizing  that they have to transform to build the  capabilities that will prepare them for the  future. Companies are thinking of ways to  drive efficiency and effectiveness to a level  not seen before. There is a strong push for  automation to play a central role in making  that happen. This is also reflected in the  testing domain, where any opportunity for  improvement will be welcome.<|endoftext|>Realizing the need to drive testing  efficiencies and reduce manual effort,  organizations want to adopt RPA in testing.  We are at a tipping point where the  benefits of RPA adoption are clear, what is  needed is that first step towards replacing  existing frameworks.   Recognizing the potential of RPA in  testing, EdgeVerve and Infosys Validation  Solutions (IVS) have been helping clients  simplify and scale up test automation  with AssistEdge for testing. AssistEdge  brings the experience of handling tens  of thousands of processes with different  technologies and environment systems  to test automation, helping navigate  heterogenous environments with  ease. By building an army of bots for  functional, regression, and user acceptance  testing, it can help achieve 100% test  automation with incredible accuracy.  In addition to being faster to build and  deploy, AssistEdge reduces the overall  time to value and also the investment  needed for deploying and managing RPA  infrastructure. Infosys Validation Solutions’  (IVS) engineering-led QA capabilities  enable enterprises to effortlessly scale  up testing in real-time, delivering  unprecedented accuracy, flexibility, and  speed to market. With its vast experience,  IVS enables clients across industry verticals  to successfully implement an automated  testing strategy, allowing them to move  away from tedious and error prone manual  testing, thereby improving performance  and software quality while simultaneously  resulting in effort- and cost-savings.<|endoftext|>The journey to RPA-based test automation  has to be implemented. And those that  adopt faster will hold a competitive  advantage in faster realization of benefits.  The question is, are you willing to take the  leap?  Want to know more about the potential  of RPA in testing? Write to us at                    askus@infosys.com External Document © 2020 Infosys Limited

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 About AssistEdge AssistEdge offers a cohesive automation  platform that enables enterprises to scale  in their automation journey. It offers  enterprises with a comprehensive suite of  products enabling them to drive initiatives  around process discovery, intelligent  automation and digital workforce  orchestration. AssistEdge has helped  enterprises unlock value in the form of  reduced service time, faster sales cycles,  better resource allocation, accelerated  revenue recognition and improved  efficiency among others.<|endoftext|>About EdgeVerve EdgeVerve Systems Limited, a wholly  owned subsidiary of Infosys, is a  global leader in AI and Automation,  assisting clients thrive in their digital  transformation journey. Our mission is  to create a world where our technology  augments human intelligence and creates  possibilities for enterprises to thrive. Our  comprehensive product portfolio across  AI (Infosys Nia), Automation (AssistEdge)  and AI enabled Business Applications  (TradeEdge, FinXEdge, ProcureEdge) helps  businesses develop deeper connections  with stakeholders, power continuous  innovation and accelerate growth in  the digital world. Today EdgeVerve’s  products are used by global corporations  across financial services, insurance, retail,  consumer & packaged goods, life sciences,  manufacturing telecom and utilities. Visit  us to know how enterprises across the  world are thriving with the help of our  technology.  https://www.edgeverve.com/ About Infosys Infosys is a global leader in next- generation digital services and  consulting. We enable clients in 46  countries to navigate their digital  transformation. With over three decades  of experience in managing the systems  and workings of global enterprises, we  expertly steer our clients through their  digital journey. We do it by enabling the  enterprise with an AI-powered core that  helps prioritize the execution of change.  We also empower the business with agile  digital at scale to deliver unprecedented  levels of performance and customer  delight. Our always-on learning agenda  drives their continuous improvement  through building and transferring digital  skills, expertise, and ideas from our  innovation ecosystem.<|endoftext|>Visit www.infosys.com to see how Infosys  (NYSE: INFY) can help your enterprise  navigate your next.<|endoftext|>External Document © 2020 Infosys Limited

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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 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 Vasudeva Naidu  AVP – Delivery Head Infosys Validation Solutions Sateesh Seetharamiah  VP – Global Product Head – AssistEdge EdgeVerve References:  ¹”RPA And Test Automation Are More Friends Than Foes”, Forrester Research, Inc., May 15, 2020 https://www.infosys.com/services/it-services/validation-solution/white-papers/documents/rpa-tool-testing.pdf https://www.infosys.com/services/it-services/validation-solution/documents/automation-testing-assistedge.pdf https://www.ibeta.com/risks-of-not-testing-software-properly/#:~:text=The%20cost%20to%20fix%20bugs,profit%20loss%20during%20 software%20downtime.<|endoftext|>

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# Infosys POV
Title: Energy Transition: Hydrogen for Net Zero
Author: Infosys Consulting
Format: PDF 1.7

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 An Infosys Consulting Perspective By Sundara Sambasivam & Shivank Saxena Consulting@Infosys.com | InfosysConsultingInsights.com Energy Transition Hydrogen for  Net Zero

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 Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 2 Energy transition: Hydrogen for Net Zero The pressure to reduce carbon emissions to achieve the target of net zero emissions by 2050  is ever-increasing. There is no silver bullet, no ‘one-size-fits-all’ solution to address this  challenge. At this point in time, there are many different energy sources with varying levels of  investment that are being explored and tested to enable our transition towards net zero.<|endoftext|>Hydrogen (H2) is one of the most abundant elements found in nature. For decarbonization of  the industry, it is considered a key component; opening new frontiers and complementing  existing solutions. This series of papers aims to share some interesting perspectives on this  sector, the associated challenges, and why it could play a significant role in the decarbonisation  agenda. Current limitations in tech, scaling challenges, and feasibility concerns are just some of  the reasons it has not yet been harnessed fully. However, hydrogen has significant potential to  manage this challenging journey towards net zero.<|endoftext|>

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 Types of Hydrogen Both the production source and process used define the hydrogen type. Below is a list of diverse hydrogen types produced today based on production method and source (The hydrogen colour chart, 2022).<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 3

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 Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 4 MARKET OUTLOOK - Production & Economies  Production and demand outlook According to the 2021 Report of International Energy Agency on Hydrogen, only 0.49 Mt of  hydrogen was produced via electrolysis. Although this was only 0.5% of overall global  production, the outlook on green and blue hydrogen is promising. It has become an essential  element for any state policy on energy transition for net zero. By 2050, more than 80% of  production is estimated to be of green or blue hydrogen. Demand will primarily be driven by  power, transport, and industry where demand for green hydrogen has the potential to grow  200% by 2050.<|endoftext|>Figure 2: Global hydrogen production and demand outlook (Harnessing Green Hydrogen: Opportunities for Deep Decarbonization in India, 2022)

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 Economic outlook The current hydrogen production costs from different methods are listed in Figure 3 (Hydrogen  Strategy: Enabling a low-carbon Economy, 2020). Coal and other fossil fuel-based production  is inexpensive at around 2 USD/kg. Prices increase by 10 to 20% when using carbon capture  and storage (CCS). Electrolysis powered by renewable energy (RE) is the most expensive at  5 to 10 USD/kg and is not currently a competitive price. This needs to decrease to at least 2  USD/kg or lower in the next decade to directly compete with fossil fuels as an energy source.<|endoftext|>There are several elements that would play a critical role in driving the cost of the end-to-end  supply chain of production and distribution. These include higher levels of innovation through research and development (R&D) and the right investment through disruptive digital  technologies like artificial intelligence, the Internet of Things, blockchain smart contracts,  certificates, and digital twin.<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 5 Figure 3: Hydrogen production costs by source and method  (Hydrogen Strategy: Enabling a low-carbon economy, 2020)

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 Economic outlook Renewables and electrolyser costs drive green hydrogen prices and are both showing declining  trends. Electrolyser costs are expected to fall by 30% in the next ten years (Harnessing Green  Hydrogen, 2022). Industrial manufacturers like Siemens Energy and Linde have already started  setting up some of the world’s biggest electrolyser production facilities in line with the European  Union’s (EU) strategy (REPowerEU plan May 2022) for fuel diversification, which will need a 27  billion EUR direct investment in domestic electrolyser and distribution of hydrogen in the EU,  excluding the investment of solar and wind electricity (REPowerEU Plan, 2022).<|endoftext|>The US, on the other hand, has announced future investments of up to 9 billion USD from 2022  to 2026 through its ‘Infrastructure Investments and Jobs Act’ (García-Herrero et al, 2022). The  key difference is that US policy plans to use both blue and green hydrogen in the fuel mix, while  the EU views blue hydrogen as a temporary solution only. Based on policy support and market conditions, the industry will decide on a future roadmap.  Green credits and green hydrogen trading can turn many fossil fuel-dependent countries into  future energy suppliers. Various states and corporates are funding green and brown field  projects which have created finance opportunities for venture capital, underwriters, and  insurance firms.<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 6 Figure 4: Renewables and electrolyser cost outlook (Harnessing Green Hydrogen, 2022)

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 Economic outlook Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 7 Figure 5: Renewables and electrolyser cost outlook (Harnessing Green Hydrogen, 2022)

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 Figure 6: Hydrogen value chain opportunities Hydrogen Value Chain Opportunities Figure 6 outlines the end-to-end value chain from production and electrolyser plant setup, operations  in conjunction with RE parks, storage (long- and short-term), distribution (liquified or gaseous), and  consumption applications (power, transportation, and industries). It gives an overview on the current  usage of Hydrogen in industry applications. New emerging areas where significant opportunities exist  for growth are primarily transportation (heavy duty vehicles and shipping), long-term energy storage  (sub-surface), and green ammonia (production and energy carrier).  Hydrogen can contribute directly to decarbonising the biggest polluters like steel, refineries, and  ammonia production. Although Hydrogen has a clean burn, its production is not clean. Hydrogen  production from fossil fuels resulted in 900 Mt CO2 emissions in the year 2020 (Global Hydrogen  Review 2021, 2021). High demand for green and blue hydrogen and hydrogen-based fuels could  reduce up to 60 Gt of CO2 emissions between 2021 and 2050, accounting for a reduction of 6% of  total cumulative emissions (Hydrogen, 2022).<|endoftext|>Some of the biggest polluters in the transportation sector include long-haul freight, heavy-duty  vehicles, maritime, and jet fuel. Decarbonizing them is not easy. By 2050, green ammonia can meet 25% of shipping fuel demand to meet the International Maritime Organization’s goal of reducing CO2  emissions by 50% from 2008’s levels. Hydrogen fuel cells can gear up short distance rides such as  ferry journeys (Harnessing Green Hydrogen, 2022). With air travel growth, a significant carbon  footprint increase is expected in aviation which already has the highest carbon emission intensity.  Options like hydrogen fuel cells, hydrogen turbines, and hydrogen-based electrolytic synthetic fuel  exist to decarbonize aviation, but each option has its merits and demerits. Big corporations like Airbus  or start-ups like ZeroAvia have already presented their roadmaps for a hydrogen-based carrier in the  next decade.<|endoftext|>For building, hydrogen can be blended into existing gas networks for both residential and commercial  complexes. It can also be used by boilers and fuel cells. Its biggest promise is in long-term energy  storage. This will impart stability to renewables-based generation and grid operations. Today, new gas  turbines can also use hydrogen as a fuel component.<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 8 Opportunities for the industry

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 What’s next? In our next articles, we will discuss  the challenges of this emerging  sector, some exciting industry  projects underway around hydrogen,  support, and digital solutions  needed to help pave the way to net  zero.  Infosys Consulting


Continue # Infosys POV
 achieved its  net zero goals 30 years ahead of  time and is working to help our  partners in their energy transition  journey towards their own net zero  goals.<|endoftext|>Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 9

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 MEET THE EXPERTS Sundara Sambasivam Associate Partner - Services, Utilities, Resources and Energy Practice Sundara.Sambasivam@infosys.com “The  lines  betw een  digita l and  physi cal  retail  will  conti nue  to  blur” Sources • García-Herrero, A.,Tagliapietra, S. & Vorsatz, V. (2021), ‘Hydrogen development strategies: a global perspective’, Bruegel, August, [Online], Link:  [Accessed: 21 Nov 2022].<|endoftext|>• ‘Global Hydrogen Review 2021’, (2021), International Energy Agency: IEA, [Online], Link Accessed:16 Nov 2022].<|endoftext|>• ‘Harnessing Green Hydrogen: Opportunities for Deep Decarbonisation in India’, (2022), Niti Aayog & Rocky Mountain Institute (RMI), June,  [Online], Link [Accessed: 16 Nov 2022].<|endoftext|>• ‘Hydrogen’, (2021), International Energy Agency: IEA, [Online], Link [Accessed: 16 Nov 2022].<|endoftext|>• ‘Hydrogen’, (2022), International Energy Agency: IEA, [Online], Link [Accessed: 16 Nov 2022].<|endoftext|>• ‘Hydrogen Strategy: Enabling A Low-Carbon Economy’, (2020), U.S. Department of Energy, July, [Online], Link [Accessed: 16 Nov 2022].<|endoftext|>• ‘REPowerEU Plan’, (2022), European Commission, [Online], Link [Accessed: 16 Nov 2022].<|endoftext|>• ‘The hydrogen colour chart’, (2022), National Grid, [Online], Link [Accessed: 16 Nov 2022].<|endoftext|>Shivank Saxena Senior Consultant - Services, Utilities, Resources and Energy Practice Shivank01@infosys.com Energy transition: Hydrogen for Net Zero | © 2022 Infosys Consulting 10 Over 22 years of global experience, Sundar has led a number of business and digital transformation  and outcome-based efficiency turnaround programmes across the Energy and Utilities  (Transmission & Distribution). Sundar is excited to collaborate and help our clients to navigate the  journey of Energy Transition towards the net zero ambitions.<|endoftext|>Over 11 years of experience, Shivank has led digital transformation projects, enabling end-to-end systems’ delivery for clients across industries and sectors. He has ensured sustained value delivery on multiple engagements by building roadmaps and driving planning-to-execution for various business-led initiatives. He is passionate about supporting the industry to meet its net zero goals, and currently helps clients innovate to drive energy transition initiatives.<|endoftext|>

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 consulting@Infosys.com InfosysConsultingInsights.com LinkedIn: /company/infosysconsulting Twitter: @infosysconsltng About Infosys Consulting  Infosys Consulting is a global management consulting firm helping some of the world’s most recognizable brands  transform and innovate. Our consultants are industry experts that lead complex change agendas driven by disruptive  technology. With offices in 20 countries and backed by the power of the global Infosys brand, our teams help the C- suite navigate today’s digital landscape to win market share and create shareholder value for lasting competitive  advantage. To see our ideas in action, or to join a new type of consulting firm, visit us at  www.InfosysConsultingInsights.com.  For more information, contact consulting@infosys.com  © 2022 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 other such intellectual property rights  mentioned in this document. Except as expressly permitted, neither this document nor any part of it may be  reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printed,  photocopied, recorded or otherwise, without the prior permission of Infosys Limited and/or any named intellectual  property rights holders under this document.

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# Infosys Whitepaper
Title: Leveraging advanced validation techniques for retail size optimization
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER LEVERAGING ADVANCED VALIDATION  TECHNIQUES FOR RETAIL SIZE  OPTIMIZATION - Divya Mohan C, Chitra Sylaja

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 Introduction In today’s highly volatile business  environment, retailers that want to  remain profitable must be able to predict  customer demand and ensure availability  of the right products in the right store at  the right time. This is a challenging task  when merchandise such as apparel and  footwear are offered in a range of sizes.  To maximize revenue and profitability,  retailers need a strategy that allows them  to sell goods at full price while reducing  markdowns.<|endoftext|>External Document © 2018 Infosys Limited

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 How does size optimization  help retailers Size optimization transforms historical  sales and inventory data into size-demand  intelligence. This enables smart buying  and allocation at the size level to match  customer needs at each store. The size  profile optimization (SPO) application  provides optimized size curves for products  at the store level. SPO uses past sales  history to deliver the right sizes to the right  stores and reduces the imbalance between  consumer demand and inventory.  By leveraging a combination of back-end  data crunching technologies and front- end size profiling tools, SPO enables  allocators, buying coordinators and buyers  to leverage sales and inventory data and  create precise style/color size scales for  each store. This degree of specificity can  help buyers and allocators create size  profiles that recover lost sales, improve  the size balance across the retail chain and  launch new products and categories based  on previous successes.<|endoftext|>Key challenges in size  optimization Retailers find it challenging to procure and  maintain the right mix of merchandise in  the right size at the right store. The store- specific size profile plays an important role  in helping retailers make assortment and  allocation decisions. As the competition  evolves, new challenges arise and retailers  need suitable approaches to counter these  challenges.<|endoftext|>Ability to understand  consumer demand Customer demand varies across stores.  Understanding the size level demand at  each store for different products is critical  to meeting each unique customer demand.  This requirement can be accurately  captured and analyzed through granular  data that represents size profiles for every  store/product.<|endoftext|>Historical performance is not  true customer demand As size profiling is based mainly on sales  history, a cleansed history is necessary to  generate an accurate size profile. Typically,  historical data comprises of profitable sales,  lost sales and markdowns. To get relevant  size profiling data, it becomes necessary  to filter out inventory data related to  stock-outs, markdowns, margins, etc., from  the consolidated data. This ensures that  the right data set is used to design better  analytical models and prevent biases  arising from extreme data points.<|endoftext|>Analyze and process large  volume of data Gathering information at the granular level  of store and size can generate large data  volumes that are difficult to handle and  analyze. Retailers may find it challenging to  derive impactful business decisions from  such large data sets.<|endoftext|>Key influencers in size  optimization: Predicting  customer decisions Consumers are always looking for specific  merchandise; say for instance, a shoe that  has the name of a basketball legend. In  case the store does not have this particular  product, they should be able to offer the  customer a suitable alternative through  smart product grouping. Size profile  optimization can help retailers predict  similar products and position them  appropriately within the store.<|endoftext|>External Document © 2018 Infosys Limited

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 Region-specific demand for  products Certain products have high customer  demand in specific regions. Size profile  optimization can analyze historical data  to ensure adequate stock of high demand  products in these stores.<|endoftext|>Reducing imbalance between  consumer demand and  inventory Customer demand for merchandise  depends on factors such as holiday  seasons, upcoming special events such as a  marathon, etc.  Size optimization can predict the type of  merchandise that needs to be in stock  during these seasons. This ensures that the  inventory is never out-of-stock for such  products.<|endoftext|>Recovering lost sales  On the other hand, owing to inaccurate or  excessive merchandise allocation, some  stores are forced to conduct clearance  sales at the end of a season to reduce  their inventory. SPO can assist retailers in  allocating accurate size profiles, thereby  ensuring only the required inventory is  stocked to meet existing business demand.<|endoftext|>Allocate new products/ new stores based on similar  products/stores Size profile optimization goes beyond  allocating size profiles based on  historical sales and inventory data.  Retail merchandising mandates that the  allocation of a new article to any store is  profitable to the company. The SPO engine  can map a new article to similar existing  articles and create profiles based on these.  Similarly, SPO can map a new store to  similar existing stores and create relevant  profiles.<|endoftext|>Demand Transfer Walk Switch Point Product Type -  Apparel for Men Class Men - Top 3 2 1 Navigate maximum up to Class Level Style 123 Style-Color 123-143 Style-Color/Size S, M, L, XL, XXL SHORT SLEEVE Top Style 132 Style-Color 132-145 Style-Color/Size S, M, L, XL, XXL LONG SLEEVE  Top External Document © 2018 Infosys Limited

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 Functional testing Functional testing is performed to ensure  that size profiling is done according to  business expectations. There are four key  ways to conduct functional testing: 1. Validate pre-load data 2. Analyze data in SPO  3. Validate of the size profile engine based  on business rules 4. Validate business intelligence reports Validation of pre-load data Data from various sources such as point-of- sale (POS) terminal, digital stores, etc., are  loaded into the database. The raw data can  exist in any form (flat file or XML).  To verify that the right data is fed into the  database, different validation techniques  can be used. These include:  • Comparing data received from different  input source systems through XML/flat  file format with data available in the  intermediate database • Ensuring that data is loaded according to  business rules defined in the system • Ensuring the data loaded from the  source to intermediate databases  is according to the mapping sheet  specified in the requirement  Analysis of data in spo Retail merchants possess large amounts of  historical data accumulated over several  years that can be fed into the size profile  engine for profile generation.<|endoftext|>Testing teams should ensure that the  correct data and possible scenarios are  sampled and transferred to the size engine.  a Data from POS, Digital Stores Intermediate DB d b c SPO Why validation is critical for  size optimization? Previously, retailers were unaware of the  importance of size optimization. They  would randomly determine an average size  profile and apply it across all stores and,  occasionally, across geographic locations.  Retailers lacked the right approach to  leverage the large amount of historical  data readily available. This often resulted  in early season stock-outs in some stores  and markdowns for the same merchandise  in other stores. Additionally, retailers  struggled to make intelligent data-driven  decisions owing to the lack of automated  approaches and techniques to validate  data quality and integrity issues.<|endoftext|>Validation strategy  Validation involves processing a large  amount of data from various sources  according to the format specified by the  size profile engine. A validation strategy  can help retailers meet customer demand  by accurately projecting each store’s future  sales and inventory needs. It can support  the business goals of the retailer, i.e.,  reduce markdowns, increase full price sales  and drive higher revenue.  Besides validation, size profiling also  includes functional and performance  testing.<|endoftext|>External Document © 2018 Infosys Limited

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 Non-functional testing Any size profile optimization project  involves processing a large volume of  structured data. Performance testing  ensures that size profile engines perform  optimally and that size profiles are  generated within the stipulated time  limits to support business needs. For  best results, the test environment for  performance testing should be similar to  the product environment. Further, if the  performance service level agreement (SLA)  is not met, then the advantages of size  profile optimization are lost. Performance  can be monitored by different tools that  are available in the market. The typical  performance testing check-points are: • Ensure data movement in each stage is  completed according to the SLA • Monitor system performance on  maximum data load Validation of reports Once the size profiles are generated,  business users can compare the profiles  for different


Continue # Infosys Whitepaper
 products and allocate them  based on analytical reports drawn using  business intelligence report-generation  mechanisms.<|endoftext|>Analytical reports are generated based  on the business rule set. The testing team  validates the accuracy of the report data  with data from the data warehouse and  verifies the usefulness of information  displayed to the business.<|endoftext|>The reports generated by the size profile  engine provide the following key details:  • Allocation by store – How many articles  of a particular size have been allocated  to a particular store • Allocation percentage at various levels  such as class, style, style-color, concept,  etc • Effectiveness of size profile – Business  can measure the effectiveness of size  profiles in improving allocation to stores  Validation of the size profile  engine based on business  rules Once data is fed into the size profile  engine, it needs to be processed according  to business rules specified within the  system.   Business rules are set to analyze the  accuracy of size profiling. The size profile  engine can analyze and process data using  these validation techniques:  • In cases where the business rule should  exclude stock-out data and sales data  having a margin filter greater than 10%  for a particular set of merchandise, the  validation team verifies that the size  profile engine has not considered such  data for profile generation  • The validation team has to ensure that  relevant data is used to determine the  appropriate profile for the introduction  of a new article/ store. Often, the data  used may be incorrect owing to non- availability of relevant data for the new  article/ store To execute high-level validation for  business rules, the following validation  techniques can be used by validation  teams: • Compare data on new products with  data on existing/similar products  to verify that a similar size profile is  generated • Ensure that the correct sample of data  is selected for verifying all the business  rules • Monitor and verify that size profiles are  generated for every size of a particular  style/color of a product • Ensure that the total size profile  generated for a particular style/color of  an article is 100% External Document © 2018 Infosys Limited

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 Conclusion Size profile optimization helps retailers  effectively stock the right sizes in stores  based on various parameters, thereby  enabling them to maximize profit, reduce  markdowns and recover lost sales.  Historical sales and inventory data is  analyzed and transformed to drive critical  business decisions. Here, data quality and  data analysis play a vital role. By leveraging  the right validation strategy with  appropriate validation techniques, retailers  can ensure that all possible business  scenarios are considered and accurate data  is chosen for size optimization decisions.<|endoftext|>References http://www.sas.com/industry/retail/sas-size-optimization http://www.oracle.com/us/industries/retail/retail-size-profile-optimize-ds-078546.pdf 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: An Insight into Microservices Testing Strategies
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER AN INSIGHT INTO MICROSERVICES  TESTING STRATEGIES Arvind Sundar, Technical Test Lead Abstract The ever-changing business needs of the industry necessitate  that technologies adopt and align themselves to meet demands  and, in the process of doing so, give rise to newer techniques  and fundamental methods of architecture in software design. In  the context of software design, the evolution of “microservices”  is the result of such an activity and its impact percolates down  to the teams working on building and testing software in the  newer schemes of architecture. This white paper illustrates the  challenges that the testing world has to deal with and the effective  strategies that can be envisaged to overcome them while testing  for applications designed with a microservices architecture.  The paper can serve as a guide to anyone who wants an insight  into microservices and would like to know more about testing  methodologies that can be developed and successfully applied  while working within such a landscape.<|endoftext|>

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 Microservices attempt to streamline the  software architecture of an application  by breaking it down into smaller units  surrounding the business needs of  the application. The benefits that are  expected out of doing so include creating  systems that are more resilient, easily  scalable, flexible, and can be quickly and  independently developed by individual  sets of smaller teams.<|endoftext|>Formulating an effective testing strategy  for such a system is a daunting task. A  combination of testing methods along  with tools and frameworks that can  provide support at every layer of testing is  key; as is a good knowledge of how to go  about testing at each stage of the test life  cycle. More often than not, the traditional  methods of testing have proven to be  ineffective in an agile world where changes  are dynamic. The inclusion of independent  micro-units that have to be thoroughly  tested before their integration into the  larger application only increases the  complexity in testing. The risk of failure and  the cost of correction, post the integration  of the services, is immense. Hence, there is  a compelling need to have a successful test  strategy in place for testing applications  designed with such an architecture.<|endoftext|>Introduction External Document © 2018 Infosys Limited

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 The definition of what qualifies as a  microservice is quite varied and debatable  with some SOA (service-oriented  architecture) purists arguing that the  principles of microservices are the same  as that of SOA and hence, fundamentally,  they are one and the same. However,  there are others who disagree and view  microservices as being a new addition to  software architectural styles, although  there are similarities with SOA in the  concepts of design. Thus, a simpler and  easier approach to understand what  microservices architecture is about, would  be to understand its key features: • Self-contained and componentized  • Decentralized data management • Resilient to failures • Built around a single business need • Reasonably small (micro) The points above are not essentially the  must-haves for a service to be called a  microservice, but rather are ‘good-to-have.’  The list is not a closed one either, as it  can also include other features that are  common among implementations of a  microservices architecture. However, the  points provide a perspective of what can  be termed as a microservice. Now that we  know what defines a microservice, let us  look at the challenges it poses to testers.<|endoftext|>The distributed and independent nature  of microservices development poses  a plethora of challenges to the testing  team. Since microservices are typically  developed by small teams working on  multiple technologies and frameworks, and  are integrated over light-weight protocols  (usually ReST over HTTPs, though this is  not mandatory), the testing teams would  be inclined to use the Web API testing  tools that are built around SOA testing.  This, however, could prove to be a costly  mistake as the timely availability of all  services for testing is not guaranteed, given  that they are developed by different teams.  Furthermore, the individual services are  expected to be independent of each other  although they are interconnected with  one another. In such an environment, a  key factor in defining a good test strategy  would be to understand the right amount  of testing required at each point in the test  life cycle.<|endoftext|>Additionally, if these services integrate  with another service or API that is exposed  externally or is built to be exposed to the  outside world, as a service to consumers,  then a simple API testing tool would prove  to be ineffective. With microservices, unlike  SOA, there is no need to have a service  level aggregator like ESB (enterprise  service bus) and data storage is expected  to be managed by the individual unit.  This complicates the extraction of logs  during testing and data verification, which  is extremely important in ensuring there  are no surprises during integration. The  availability of a dedicated test environment  is also not guaranteed as the development  would be agile and not integrated.<|endoftext|>Microservices architecture Challenges in testing microservices External Document © 2018 Infosys Limited

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 In order to overcome the challenges  outlined above, it is imperative that the test  manager or lead in charge of defining the  test strategy appreciates the importance  of Mike Cohn’s Test Pyramidi and is able to  draw an inference of the amount of testing  required.  The pictorial view emphasizes the need to  have a bottom-up approach to testing. It  also draws attention to the number of tests  and in turn, the automation effort that  needs to be factored in at each stage. The  representation of the pyramid has been  slightly altered for the various phases in  microservice testing. These are: i. Unit testing   The scope of unit testing is internal to  the service and in terms of volume of  tests, they are the largest in number.  Unit tests should ideally be automated,  depending on the development  language and the framework used  within the service.<|endoftext|>ii. Contract testing   Contract testing is integral to  microservices testing and can be of  two types, as explained below. The  right method can be decided based on  the end purpose that the microservice  would cater to and how the interfaces  with the consumers would be defined.  a) Integration contract testing:   Testing is carried out using a test  double (mock or stub) that replicates  a service that is to be consumed.  The testing with the test double  is documented and this set needs  to be periodically verified with the  real service to ensure that there are  no changes to the service that is  exposed by the provider.  b) Consumer-driven contract testing:   In this case, consumers define the  way in which they would consume  the service via consumer contracts  that can be in a mutually agreed  schema and language. Here, the  provider of the service is entrusted  with copies of the individual  contracts from all the consumers.  The provider can then test the  service against these contracts to  ensure that there is no confusion in  the expectations, in case changes  are made to the service.<|endoftext|>iii. Integration testing   Integration testing is possible in case  there is an available test or staging  environment where the individual  microservices can be integrated before  they are deployed. Another type of  integration testing can be envisaged  if there is an interface to an externally  exposed service and the developer  of the service provides a testing or  sandbox version. The reliance on  integration tests for verification is  generally low in case a consumer- driven contract approach is followed.<|endoftext|>iv. End-to-end testing   It is usually advised that the top layer   of testing be a minimal set, since a  failure is not expected at this point.  Locating a point of failure from an  end-to-end testing of a microservices  architecture can be very difficult and  expensive to debug.<|endoftext|>Mike Cohn’s Testing Pyramid E2E UI Testing Scope of Testing Execution Time Number of Tests Integration  Testing Contrast Testing Unit Testing Approach to testing microservices and testing phases External Document © 2018 Infosys Limited

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 • For unit testing, it would be ideal to  use a framework like xUnit (NUnit or  JUnit). The change in data internal to  the application needs to be verified,  apart from checking the functional  logic. For example, if reserving an  item provides a reservation ID on  success in the response to a REST call,  the same needs to be verified within  the service for persistence during unit  testing.<|endoftext|>• The next phase of testing in  contract testing. In case there are  several dissimilar consumers of the


Continue # Infosys Whitepaper
 service within the application, it is  recommended to use a tool that can  enable consumer-driven contract  testing. Open source tools like Pact,  Pacto, or Janus can be used. This  has been discussed in further detail  in the last example and hence, in  the context of this example, we will  assume that there is only a single  consumer of the service. For such  a condition, a test stub or a mock  can be used for testing by way of  REST over HTTPS Item ID. date Reservation ID Application Mocked Service Unit Testing Scope Integration Testing Scope Select an Item Reserve an item Integration Contract Testing Scope integration contract testing. Data  being passed between the services  needs to be verified and validated  using tools like SOAPUI. For example,  an item number being passed  between the services that selects it to  the one that reserves it.  • E2E tests should ensure that  dependency between microservices  is tested at least in one flow, though  extensive testing is not necessary. For  example, an item being purchased  should trigger both the ‘select’ and  ‘reserve’ microservices.<|endoftext|>In order to get a clear understanding of how testing can be carried out in different scenarios, let us look at a few examples that can help  elucidate the context of testing and provide a deeper insight into the test strategies used in these cases.<|endoftext|>• Scenario 1:    Testing between microservices internal to an application or residing within the same application   This would be the most commonly encountered scenario, where there are small sets of teams working on redesigning an application by  breaking it down into microservices from a monolithic architecture.  In this example, we can consider an e-commerce application that has two services a) selecting an item and b) reserving an item, which  are modelled as individual services. We also assume there is a close interaction between these two services and the parameters are  defined using agreed schemas and standards.<|endoftext|>Testing scenarios and test strategy External Document © 2018 Infosys Limited

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 • Unit tests should ensure that the  service model is catering to the  requirements defined for interacting  with the external service, while  also ensuring that internal logic is  maintained. Since there is an external  dependency, there exists a need to  ensure that requirements are clearly  defined and hence, documenting  them remains key. TDD approach is  suggested where possible and any of  the popular frameworks discussed in  the previous example can be chosen   for this.<|endoftext|>• Contract testing can be used in this  case to test the expectations from  consumer microservices, that is, the  applications internal service, decoupling  it from the dependency on the external  web service to be available. In this  context, test doubles, created using  tools like Mockito or Mountebank,  can be used to define the PayPal API’s  implementation and tested. This is  essentially integration contract testing  and again needs to be verified with  a live instance of the external service  periodically, to ensure that there is no  change to the external service that has  been published and consumed by the  consumer.<|endoftext|>• Integration tests can be executed if  the third-party application developer  • Scenario 2:    Testing between internal microservices and a third-party service   Here, we look at a scenario where a service with an application consumes or interacts with an external API. In this example,  we have considered a retail application where paying for an item is modelled as a microservices and interacts with the  PayPal API that is exposed for authenticating the purchase.<|endoftext|>  Let us look at the testing strategy in each phase of the test cycle in this case: provides a sandbox (e.g. PayPal’s  Sandbox APIii ) for testing. Live testing  for integration is not recommended.  If there is no availability of a sandbox,  integration contract testing needs to be  exercised thoroughly for verification of  integration.<|endoftext|>• E2E tests should ensure that there  are no failures in other workflows  that might integrate with the internal  service. Also, a few monitoring tests can  be set up to ensure that there are no  surprises. In this example, selecting and  purchasing an item (including payment)  can be considered an E2E test that can  run at regular and pre-defined intervals  to spot any changes or breaks.<|endoftext|>PayPal (External) API PayPal Sandbox API Unit Testing Scope Application Pay for an Item Test Double Virtual Provider Firewall  Contract Testing Scope Integration Testing Scope REST over HTTPS External Document © 2018 Infosys Limited

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 • Unit tests should cover testing for  the various functions that the service  defines. Including a TDD development  can help here to ensure that the  requirements are clearly validated  during unit testing. Unit test should also  ensure that data persistence within the  service is taken care of and passed on  to other services that it might interact  with.<|endoftext|>• Contract testing – In this example,  consumers need to be set up by using  tools that help define contracts. Also,  the expectations from a consumer’s  perspective need to be understood.  The consumer should be well-defined  and in line with the expectations in the  live situation and contracts should be  collated and agreed upon.<|endoftext|>  Once the consumer contracts are  validated, a consumer-driven contract  approach to testing can be followed. It  is assumed that in this scenario, there  would be multiple consumers and  hence, individual consumer contracts  for each of them. For example, in the  above context, a local retailer and  an international retailer can have  • Scenario 3:    Testing for a microservice that is to be exposed to public domain   Consider an e-commerce application where retailers can check for availability of an item by invoking a Web API.<|endoftext|>different methods and parameters of  invocation. Both need to be tested  by setting up contracts accordingly.  It is also assumed that consumers  subscribe to the contract method of  notifying the provider on the way  they would consume the service and  the expectations they have from it via  consumer contracts.<|endoftext|>• E2E tests – minimal set of E2E tests  would be expected in this case, since  interactions with external third parties  are key here Unit Testing Scope Application   Consumer Driven Contract Testing Scope REST over HTTPS REST over HTTPS Customer Contract 1 Virtual Consumer 1 Customer Contract 2 Virtual Consumer 2 External Document © 2018 Infosys Limited

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 Improvements in software architecture has led to fundamental changes in the way applications are designed and tested. Teams  working on testing applications that are developed in the microservices architecture need to educate themselves on the behavior of  such services, as well as stay informed of the latest tools and strategies that can help deal with the challenges they could potentially  encounter. Furthermore, there should be a clear consensus on the test strategy and approach to testing. A consumer-driven contract  approach is suggested as it is a better way to mitigate risk when services are exposed to an assorted and disparate set of consumers and  as it further helps the provider in dealing with changes without impacting the consumer. Ensuring that the required amount of testing  is focused at the correct time, with the most suitable tools, would ensure that organizations are able to deal with testing in such an  environment and meet the demands of the customer.<|endoftext|>References :  ihttps://www.mountaingoatsoftware.com/blog/the-forgotten-layer-of-the-test-automation-pyramid       iihttps://www.sandbox.paypal.com In conclusion © 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: Modernizing enterprise systems in healthcare
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER MODERNIZING ENTERPRISE SYSTEMS  IN HEALTHCARE An end-to-end testing approach for greater  predictability and quality Abstract As digital technologies, smart wearables and remote monitoring  capabilities penetrate healthcare, traditional healthcare companies  are unable to keep up with end-user expectations. Under pressure  to adopt rapid transformation, these organizations are looking  for robust and end-to-end testing procedures. This paper explains  various end-to-end testing approaches within the four main  modernization techniques for healthcare companies. The analysis  presented here acts as a guideline for healthcare leaders to make  strategic and informed decisions on how to modernize their systems  based on the needs of their end-users.

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 Introduction Sustainability in healthcare is a looming  challenge, particularly as the fusion of  disruptive innovations such as digitization,  Internet-of-Things and smart wearables  enable remote and real-time health  tracking, diagnosis and management.  To succeed in such an environment,  healthcare organizations rely heavily on IT.  Thus, using the latest end-to-end testing  approaches becomes essential to:  •  Ensure that all applications operate  as a single entity with multi-module  interactions  •  Maintain performance/non-functional  scenarios within the desired limit •  Identify bottlenecks and dependencies  ahead of time so that the business can  take appropriate actions  Testing challenges in  healthcare modernization Business transformation in healthcare  is complex because of the challenges in  maintaining integrity between different  types of customer needs and health- related plans. Modernizing healthcare  software applications mandates enabling  multi-directional flow of information across  multiple systems, which can complicate  the entire healthcare workflow application.  Further, failures or errors in systems outside  the enterprise environment can adversely  affect the performance of applications with  which they are integrated.  To address such challenges, it is important  to determine the right method and types  of end-to-end testing. This will optimize  application performance by testing it  across all layers from the front-end to the  back-end along with its interfaces and  endpoints.<|endoftext|>Typically, most healthcare organizations  use multi-tier structures with multiple  end-users, making end-to-end testing  very complex. Launching a new product in  such a multi-directional business scenario  requires extensive user testing. Thus, to  enable end-to-end (E2E) testing, health  insurance companies must first understand  what customers expect from their  healthcare providers and identify how they  can meet these expectations in shorter  timelines.<|endoftext|>Fig 1: A typical multi-tier healthcare business Customer  Profle Cloud Member Rule Firewall Web server Service Rule Oracle Big Data Web service Find  Doctor  Hospital Claim  Web   Page Payment  Application Provider  Web Page Business Team XML XML XML XML XML SOA XML SOAPUI Pictorial Diagram of Multi-tier  Healthcare Business API Call Database Update SOA Call Service Call Consumers External Document © 2018 Infosys Limited

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 Infosys solution  End-to-end testing approaches for different modernization techniques Infosys leverages four modernization techniques to help healthcare organizations enable end-to-end testing. These techniques are: 1. Re-engineering technique Use-case: Best-suited in cases where  companies need to digitize healthcare  product marketing to different constituents  of a state through online retail.  This modernization technique is useful  when venturing into new markets or when  retiring obsolete technologies due to  high maintenance costs. It leverages the  following end-to-end testing approaches: •  Simulation testing: User-centric  interaction testing is performed  based on different behavior events  like usability testing, cross browser  compatibility and mobile testing •  Compliance testing: This testing is  needed for security protocols and  financial boundary testing as per  mandates defined by state and central  governments •  Blend testing: This combines functional  and structural testing into a single  approach and is essential for any  healthcare digitization transformation  strategy Universal automation: This  is a new approach that automates  the acceptance of changed features  in applications through browser  recognition, payment gateways, etc.<|endoftext|>•  Risk-based testing: This focuses on  testing a few components or critical  defects that are identified as high-risk  functions, have significant complexity in  business operations and can impact key  features  •  Continuous automation testing:  Based on continuous integration and  continuous delivery, this testing is  for real as well as virtual features that  are proposed for projects in minor  transition •  Recognition accuracy testing: This tests  non-textual data like images, pictorial  figures, feelings, fingerprinting, etc.,  using a virtual augmented framework  Benefits – This testing approach provides  higher returns on investment by nearly 80- 90% in short spans within 5-7 iterations. It  also improves co-ordination, accuracy and  reusability of data in ensuing runs, thus  providing a robust and reusable testing  option through cutting-edge technology.<|endoftext|>Risks – Diversified technology exposure  is critical to support such big bang  transformation and limited technical  knowledge may result in uncovering fewer  quality issues. Further, rebuilding the  enterprise framework can be costly.<|endoftext|>2. Replacing or Retiring  technique Use case: Best-suited when one needs to  remove contract and legal documentation  from healthcare insurance and hospitals to  a separate online portal.<|endoftext|>This modernization technique is used  when there is a need for more control and  accuracy. Migratory functions are clustered  as units and can be renovated easily  without disturbing other applications.  Here, end-to-end testing focuses on  components that undergo gradual  replacement or are retired as described  below: •  Plug-and-play testing: This is usually  executed when testing teams employ  different types of tools for automation  scripting or when different types of  technologies are involved in testing •  Web service-based testing: This  is a mechanism or medium of  communication by which two or more  applications exchange data, irrespective  of the underlying architecture and  technology •  Neutrality testing: This is typically used  when the existing platform is replaced  with a new one without altering the  final business outcomes or end-user  experiences  •  Parallel testing: This analyzes several  applications or sub-elements of one  application simultaneously and in the  same instance using agile or waterfall  models in order to reduce test time •  Assembly testing: This reveals precise  interactions among modules as per user  requirements. It is used when functions    are grouped into a logical entity and  alliances are needed •  Usability testing: Usability testing  covers learnability, memorability,  adeptness, and customer satisfaction  indices to determine how easy to use  the application is for end-users  Benefits – This modernization approach  provides more structure and control to  end-to-end testing with15-20% effort  reduction. It ensures effective application  testing with the option of reverting to  native state on-demand when needed.  Further, it requires only 5-7% effort for  automation changes during build.  Risks – Project overrun can occur without  proper supervision. Additionally, it requires  repeated testing of the same regression  suite even for small deployments.  External Document © 2018 Infosys Limited

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 3. Re-fronting technique Use case: Best-suited when adding  encryption logic protocol is required for  sensitive claim-related information passing  through a web service.<|endoftext|>This approach is used when end-users  want to use the same data efficiently  and quickly without investing in  expensive infrastructure set-up. It covers  virtualization, non-functional testing and  regression testing as described below: •  Virtualization testing: This simulates  multiple users to check the performance  of the new technology while it interacts  with existing applications •  Non-functional testing: Certain features  like technology compatibility, platform  integrity, exception handling, help  analysis, impact exploration, and  application availability falls under the  purview of non-functional testing •  Regression testing: Regression re-run  approach is used when there is a slight  change in functionality but the overall  system behavior has not changed Benefits – This approach simplifies  localized defect resolution. Here, end- to-end testing is more stable as changes  are limited and specific. Further, the cost  of running E2E test cases is lower as the  regression suite can be easily automated.<|endoftext|>Risks – Frequent patch changes can lower  productivity and increase maintenance  cost. Further, repeated testing of the same  emergency bug fix can reduce long-term  RoI.  4.Re-platforming technique Use case: Best-suited when upgrading  billing/payments databases to recent


Continue # Infosys Whitepaper
 versions is needed due to license renewals.<|endoftext|>Re-platforming of application  modernization is primarily done in  areas where businesses aim to minimize  maintenance costs with cost effective  technology. This modernization technique  uses migration, acceptance, intrusive, and  volume testing approaches as described  below:  •  Migration testing: This is used when  ensuring data integrity is the most  important factor during technology  upgrades  •  Acceptance testing: Acceptance testing  ensures that applications being moved  to a new platform have the same  recognition intensities as before •  Intrusive testing: Also used as negative  testing, this approach determines the  effect of hosting unexpected variables  into the system or overall application •  Volume testing: This evaluates the  stability of applications by ingesting a  huge number of records Benefits – This approach simplifies end- to-end testing as predicted business  outcomes are achieved. It lowers testing  cost, thus reducing total cost of operations  and time-to-market and does not require  additional infrastructure or specialized  licensing tools. Further, it increases testing  penetration by reusing scenarios, data and  execution strategies.<|endoftext|>Risks – Re-platforming may warrant  additional testing of critical business flows  to ensure functional defects are caught  early to avoid cost impact. Also conducting  testing in the new platform requires proper  training.<|endoftext|>External Document © 2018 Infosys Limited

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 Modernization techniques for end-to-end testing approaches Testing Approach Re-engineer Remediate or  replace Re-front Re-platform Simulation testing Yes Yes No No Compliance testing Yes Yes No No Blend testing Yes Yes No No Universal testing Yes Yes Yes No Risk-based testing Yes Yes No No Plug-and-play testing No Yes Yes Yes Web service-based testing Yes Yes Yes Yes Agile testing No Yes Maybe No Parallel testing No Yes Yes No Virtualization testing No No Yes Yes Usability testing Yes Yes No No Recognition testing Yes No Yes Maybe Regression testing No No Yes Yes Migration testing Yes No Maybe Yes Assembly testing Yes Yes Yes No Volume testing Yes No No Yes Intrusive testing Yes No No No Acceptance testing Yes Maybe No Yes Comparative analysis of various testing approaches The following table depicts a matrix of end-to-end test approaches along with modernization techniques in healthcare. The matrix illustrates  which E2E testing method is best-suited to the four different modernization techniques. While ‘yes’ and ‘no’ represent absolute outcomes, it  is important to note that ‘maybe’ results depend on how critical the business needs are and whether the approach is actually cost-effective  when considering the overall business operations.<|endoftext|>Table 1: Comparison of different end-to-end test approaches and modenization techniques External Document © 2018 Infosys Limited

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 Case study Business need – A healthcare  company with three verticals for  policyholders, doctors and claims wanted  to remodel their business portfolio  to adopt new technologies and meet  customer demand. While the policy  procuring system and the consumer  premium collection system were digitized,  the company decided to re-engineer the  claims processing system from DB2 to a big  data-based system. As the claims vertical  interacted with doctors and hospital web  portals, they also wanted to gradually  transform the portals component-wise  in order to give doctors sufficient time  to acquaint themselves with the new  digitized system.<|endoftext|>Solution approach – To support the  company’s hybrid transformation project,  we used an end-to-end testing strategy  that leveraged complementary test  approaches from different modernization  techniques across the three verticals, as  described below:  •  For policyholders: The customer  procurement system was treated with a  combination of re-front modernization  and big bang transformation. Blend  testing was used with continuous  automation followed by web service  testing and assembly testing •  For providers (doctors/hospitals): Here,  we used a combination of assembly,  regression rerun and agile testing to  ensure gradual changes since agile  testing methodology is best-suited  for scenarios where constituents are  deployed slowly over a period of time •  For claims: Claims is a crucial vertical.  Thus, skeleton scripts, virtualization and  migration testing methods were used  for their stability and lower risk when  migrating from DB2 to big data As each vertical of the company has different business needs, different types of modernization were needed to suit various end-users.<|endoftext|>Fig 2: End-to-end testing approach for the three verticals External Document © 2018 Infosys Limited

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 The road ahead In future, more consumers will embrace digitization and the uber connectedness of wearables and mobile devices that can track the user’s  health through in-built monitoring systems. Thus, as a higher number of service operators orchestrate multiple domains, we can expect to  see greater challenges ahead for end-to-end testing. This makes it imperative to leverage DevOps and analytics-based testing capabilities  along with modernization approaches.<|endoftext|>Conclusion  Disruptive technologies are creating avenues  for healthcare providers to issue virtual  treatment, advice and services. However, this  requires some degree of IT modernization  for which end-to-end testing is crucial. There  are various approaches that can be used  to enable re-engineering, replacing, re- fronting, and re-platforming modernization  techniques. Each testing approach has  its benefits and risks and must be chosen  based on the end-user expectations. Thus,  it is important for business leaders to be  aware of these in order to make the right  decision for their IT modernization journey.  The right approach can offer significant cost  advantages, accelerate time-to-market and  ensure seamless end-user experience.
Authors Dipayan Bhattacharya  Project Manager Infosys Validation Solutions  Amit Kumar Nanda  Group Project Manager  Infosys Validation Solutions External Document © 2018 Infosys Limited

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 References Egui Zhu1, M., Anneliese Lilienthal1, M., Shluzas, L. A., Masiello, I., & Zary, N. (2015). Design of Mobile Augmented Reality in Health Care Education: A Theory- Driven Framework. JMIR Medical Education, 1-17.<|endoftext|>Flahiff, J. (2011, June 6). Integrating Agile into a Waterfall World. The InfoQ Podcast.<|endoftext|>Hewlett-Packard Development Company. (2012). Survival guide for testing modern applications. Hewlett-Packard Development Company.<|endoftext|>Infosys. (n.d.). https://www.infosys.com/it-services/validation-solutions/white-papers/documents/end-test-automation.pdf.<|endoftext|>Infosys. (n.d.). https://www.infosys.com/it-services/validation-solutions/white-papers/documents/qa-strategy-succeed.pdf.<|endoftext|>Karan Maini. (n.d.). Legacy Modernization. Retrieved from https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved= 0ahUKEwjSs5HssZnTAhUFSY8KHeKeDBoQFggiMAI&url=http%3A%2F%2Fdoczz.net%2Fdoc%2F5515301%2Flegacy-modernization&usg=AFQjCNFDRMLgm slUWqaqhyCqR7XahprSBQ&bvm=bv.152174688,d.c2I NetReach Technologies. (2010). Legacy Application Modernization Balancing Risk vs. Reward.<|endoftext|>Quality Thoughts. (2016, Oct 10). Quality Thoughts. Retrieved from http://www.qualitythought.in/courses/webservices-testing/ Selenium Labs. (2016, Oct 10). Web Services SOA Testing. Retrieved from http://www.seleniumlabs.in/web-services-soa-testing-in-bangalore.html Slide share. (2016, Oct 10). Webservices Testing. Retrieved from http://www.slideshare.net/AmitChanna/webservices-testing-a-changing-landscape Transvive. (2011). Migration Strategies & Methodologies. Toronto: Transvive.<|endoftext|>(n.d.). Retrieved from http://healthinsurancemedical.50webs.com/article1.html (n.d.). Retrieved from https://www.google.co.in/search?hl=en-IN&biw=1280&bih=866&tbm=isch&q=3+pillars+of+financial+security&oq=&gs_ l=#


Continue # Infosys Whitepaper
imgrc=uQhgXTjoI03D8M%3A (n.d.). Retrieved from https://www.google.co.in/search?q=computer+server+clipart&sa=G&hl=en-IN&biw=1280&bih=866&tbm=isch&imgil=W9Nhy A0FXtloxM%253A%253B5cEEUU2VgnYvcM%253Bhttp25253A%25252F%25252Fpublicdomainvectors.org%25252Fen%25252Fpowerpoint-clip-art- server&source=iu&pf=m&fir (n.d.). Retrieved from http://www.google.co.in/imgres?imgurl=http://sacramentoappraisalblog.com/wp-content/uploads/2016/08/real-estate-market- future-sacramento-appraisal-blog-mage-purchased-and-used-with-permission-by-123rf-1.jpg&imgrefurl=http://paper.li/AngelaRunsAmuck/130652 (n.d.). Retrieved from https://www.google.co.in/search?q=virtual+reality+in+business&hl=en-IN&biw=1280&bih=866&source=lnms&tbm=isch&sa=X&ved =0ahUKEwjP9MWOnM3QAhXIRY8KHbjjBCwQ_AUICCgB#imgrc=VyCt_MfLFMQo0M%3A © 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 POV
Title: Next-gen Process Mining powers Oil & Gas transformation
Author: Infosys Consulting
Format: PDF 1.7

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 An Infosys Consulting Perspective By Sachin Padhye, Naveen Kamakoti, Shruti Jayaraman and Sohini De Consulting@Infosys.com | InfosysConsultingInsights.com Next-gen Process  Mining powers Oil &  Gas transformation

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 Oil & Gas transformation | © 2023 Infosys Consulting 2 Process Mining technology: A key enabler to transform Oil & Gas Technology continues to be a reliant and indispensable enabler to transform operations of Oil and  Gas companies. As a growing trend, the broader intent of incorporating technology is the use of  operational data to support analytics and fact-based decision-making. However, increasing  complexity of core and supplementary processes, coupled with limited agility of legacy and  monolithic IT systems adds a constant challenge to continuous process improvement.<|endoftext|>The agility of business processes and operations depends on the ability to capture real-time data  and perform large scale analyses to generate actionable insights on-demand and help steer and  nudge key metrics and key performance indicators (KPIs).  One such technology framework with ever-growing adoption is Process Mining, especially due  to the evolution from process-discovery-based limited applications to centralized platforms for  integrated process automation.<|endoftext|>

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 3 Process Mining uses detailed data from business processes Process Mining is the practice of using data from various sources to analyze, baseline  and improve business processes.  The concept of Process Mining is built on the pillars of analysis techniques using artificial  intelligence (AI) and machine learning (ML). It is an approach to analyze, optimize, and  improve complex operational processes.  Powered by event data logs and data science tools, Process Mining helps identify  process variations and bottlenecks and gathers quantitative insights in process flows. It  also helps address performance and compliance-related issues in processes.  The following high-level steps are involved in a typical process mining lifecycle journey: Step Description Tools used 1. Data collection Collect data from various sources, such  as event logs, databases, operational  data stores.<|endoftext|>Data extraction tools, such as ETL  tools, log parsers, or database  connectors.<|endoftext|>2. Data pre- processing Clean, filter, and normalize data to  ensure consistency and accuracy.<|endoftext|>Data cleaning and preparation  tools, such as Python, or R scripts.<|endoftext|>3. Process  discovery Create a process model based on the  collected data.<|endoftext|>Process Mining tools, such as  Disco, ProM, or Celonis.<|endoftext|>4. Conformance  checking Compare the process model with the  collected data to identify deviations,  errors, or inefficiencies in the process.<|endoftext|>Conformance checking tools, such  as Disco, ProM, or Celonis.<|endoftext|>5. Process  enhancement Optimize the process model to improve  efficiency, reduce costs, and enhance  quality.<|endoftext|>Process simulation and optimization  tools, such as Arena, Simul8, or  ProModel.<|endoftext|>6. Process  monitoring Continuously track and analyze process  data to identify potential issues,  bottlenecks, or opportunities for  improvement.<|endoftext|>Process monitoring tools, such as  Celonis, Splunk, ELK, or Graylog.<|endoftext|>7. Process  visualization Create graphical representations of the  process model and process data to help  stakeholders understand the process and  identify areas for improvement.<|endoftext|>Data visualization tools, such as  Celonis, Tableau, Power BI, or  QlikView.<|endoftext|>Oil & Gas transformation | © 2023 Infosys Consulting

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 4 Evolution of Process Mining From being a niche technology used in research-oriented projects to a completely  integrated cross-functional collaboration platform, Process Mining has evolved and  matured for broad adoption. Here is a summary of this evolution: Oil & Gas transformation | © 2023 Infosys Consulting Process  discovery Process  conformance User-centered  process Integrated process  automation Time period First Generation Second Generation Third Generation Fourth Generation 1988 – 2004 2004 – 2011 2011 – 2016 2016 – Present Summary Focus on discovery  of process models  from event logs Introduction of  conformance checking  and process  enhancements Shift towards more user- centered and interactive  approaches Expansion of process mining  beyond event logs to include  other types of data and  processes Use of process  discovery algorithms  and process tree  visualizations Integration of multiple  perspectives and data  sources Focus on business  process management  and improvement Integration of process mining  with other technologies such  as AI, IoT, and blockchain Limited support for  large and complex  processes Focus on quality  control, compliance,  and audit trails Integration of social,  organizational, and  environmental factors Increased focus on  automation, robotics, and  digital transformation Challenges in  handling noise,  concurrency, and  infrequent behavior Use of data mining and  machine learning  techniques Increased emphasis on  big data, cloud  computing, and  distributed systems Development of new  techniques such as predictive  process monitoring and  prescriptive analytics People Primarily academic  researchers and  process experts Involvement of  business stakeholders  and end-users in  process mining projects Involvement of a wider  range of stakeholders  including end-users, IT  staff, and top  management Involvement of a wide range  of stakeholders including  business users, IT staff, data  scientists, and process  experts Minimal involvement  of business  stakeholders and  end-users Increasing emphasis on  collaboration and  communication Greater emphasis on  user needs and user  experience Greater emphasis on cross- functional collaboration and  co-creation Process Emphasis on  process modeling  and analysis Shift towards process  improvement and  optimization Increased integration of  process mining with  business strategy and  management practices Integration of process mining  with digital transformation and  innovation initiatives Limited focus on  process  improvement and  optimization Greater attention to  business objectives  and value creation Greater emphasis on  continuous improvement  and innovation Systems Basic computing  tools and algorithms Development of more  sophisticated  algorithms and  methods Greater use of cloud  computing, big data, and  advanced analytics Integration of AI, IoT,  Blockchain, and Advanced  Analytics Primarily desktop- based software Increased use of  enterprise-level  software systems Integration with other  digital technologies such  as social media and  mobile devices Use of event logs  and basic data  mining techniques Integration of multiple  data sources and  formats Greater use of process  automation and robotic  process automation  (RPA)

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 5 Process Mining impacts multiple areas in Oil & Gas The Oil and Gas industry is complex and dynamic with significant data generated across  all areas.  For Oil and Gas companies, Process Mining can be particularly important because of the  complex and highly regulated nature of their operations. Here are some specific ways in  which Process Mining can benefit Oil and Gas companies: Oil & Gas transformation | © 2023 Infosys Consulting Value levers Impact of Process Mining Operational  efficiency Process Mining can help identify inefficiencies in processes,  such as bottlenecks or unnecessary steps, and suggest ways  to streamline them. This can lead to cost savings and better  use of resources.<|endoftext|>Regulatory  compliance Oil and Gas companies are subject to numerous regulations  and standards, such as those related to environmental  protection and worker safety. Process Mining can help  ensure that these regulations are being followed and identify  areas where improvements are needed.<|endoftext|>Operational safety Safety is a top priority for Oil and Gas companies, and  Process Mining can help identify potential hazards and risks.<|endoftext|>By analyzing data from sensors, equipment, and other  sources, companies can identify patterns that may indicate  an increased risk of accidents or equipment failure.<|endoftext|>Optimized  maintenance Process Mining can help companies optimize maintenance  schedules by analyzing data from equipment and other  sources to identify when maintenance is needed. This can  help prevent unplanned downtime and reduce maintenance  costs.<|endoftext|>Customer  satisfaction Oil and Gas companies may interact with customers in  various ways, such as through fuel delivery or service  stations. Process Mining can help companies understand  how customers interact with their services and identify ways  to improve the customer experience.<|endoftext|>

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 6 Pre-requisites for Process Mining Certain conditions need to be met before leveraging Process Mining effectively. These  can broadly be grouped under People, Process and Technology.<|endoftext|>Oil & Gas transformation | © 2023 Infosys Consulting Who can help manage the  organizational changes that may  result from Process Mining  initiatives.  BUSINESS ANALYSTS With domain knowledge of the  processes to be analyzed.  With expertise in data  management and system  integration.  With expertise in data analysis  and statistical modelling.  Or subject matter experts who can  provide feedback on the accuracy  and relevance of Process Mining  results.  DATA SCIENTISTS IT PROFESSIONALS