Continue # Infosys Whitepaper 
choice using a tool-agnostic  library.<|endoftext|>•  Analyze past data and patterns to  dynamically decide what tests must be  run for different teams and products for  subsequent application builds.<|endoftext|>•  Correlate production log data with past  code change data to determine the risk  levels of failure in different application  modules, thereby optimizing the  DevOps process.<|endoftext|>External Document © 2020 Infosys Limited {{   img-description: man working with two computers to code a software, in the style of dark brown and light azure, soft edges and blurred details, handheld, poured, studyplace, furaffinity, coded patterns  }} 

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 Fig 2: How reinforcement algorithms in testing identify critical flows in an application B.  Automation test suite maintenance Use case 4: Identifying the most critical  paths in an AUT When faced with short regression cycles  or ad hoc testing requests, QA teams are  expected to cover only the most important  scenarios. They rely on system knowledge  or instinct to identify critical scenarios,  which is neither logical nor prudent  from a test coverage perspective. Thus,  the inability to logically and accurately  identify important test scenarios from the  automation test suite is a major challenge,  especially when short timelines are  involved.<|endoftext|>Solution 4: Use deep learning to  determine critical application flows If the AUT is ready for validation (even  when no test cases are available in any  form), a tester can use a reinforcement  learning-based system to identify the  critical paths in an AUT.  External Document © 2020 Infosys Limited {{   img-description: person and woman pointing on computers while using software programs, in the style of code-based creations, object portraiture specialist, light amber and navy, iso 200, flowing brushwork, embroidery, polished craftsmanship (bottom full width) }} 

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 {{   img-description: person and woman pointing on computers while using software programs, in the style of code-based creations, object portraiture specialist, light amber and navy, iso 200, flowing brushwork, embroidery, polished craftsmanship  }} Use case 5: Reworking the test  regression suite due to frequent  changes in AUT  If a testing team conducts only selective  updates and does not update the  automation test suite completely, then  the whole regression suite becomes  unwieldly. An AI-based solution to  maintain the automation and regression  test suite is useful in the face of ambiguous  requirements, frequent changes in AUTs  The five use cases and solutions discussed above can be readily implemented to immediately enhance an enterprise’s test suite automation  process, no matter their stage of test maturity.<|endoftext|>and short testing cycles, all of which leave  little scope for timely test suite updates.<|endoftext|>Solution 5: Deploy a self-healing  solution for easier test suite  maintenance Maintaining the automation test suite  to keep up with changing requirements,  releases and application modifications  requires substantial effort and time. A  self-healing/self-adjusting automation test  suite maintenance solution follows a series  of steps to address this challenge. These  steps are identifying changes between  releases in an AUT, assessing impact,  automatically updating test scripts, and  publishing regular reports. As shown in  Fig 3, such a solution can identify changes  in an AUT for the current release, pinpoint  the impacted test scripts and recommend  changes to be implemented in the  automation test suite.<|endoftext|>AUTtest suite External Document © 2020 Infosys Limited 

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 Conclusion Complete automation, i.e., an end-to-end  test automation solution that requires  minimal or no human intervention, is the  goal of QA organizations. To do this, QA  teams should stop viewing automation  test suites as static entities and start  considering these as dynamic ones with  a constant influx of changes and design  solutions. New technologies like AI/ML  can help QA organizations adopt end- to-end automation models. For instance,  AI can drive core testing whether this  involves maintaining test scripts, creating  automation test suites, optimizing  test cases, or converting test cases to  automated ones. AI can also help identify  components for reusability and self-healing  when required, thereby slashing cost, time  and effort. As agile and DevOps become  a mandate for software development, QA  teams must move beyond manual testing  and traditional automation strategies  towards AI/ML-based testing in order to  proactively improve software quality and  support self-healing test automation.<|endoftext|>External Document © 2020 Infosys Limited {{   img-description: group of men and women in an office, raising their hands, in the style of spatial play, mix of masculine and feminine elements, smilecore, object-oriented, strong emotional impact, working-class empathy, curved mirrors  }} 

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 About the 
Authors © 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 Suman Boddu, Senior Technical Manager, Infosys  Akanksha Rajendra Singh, Consultant, Infosys    


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