Continue # Infosys Whitepaper 
 about  smartphones or tablets, rather it is perva- sive with handheld devices in retail stores,  point of service (POS) systems, mobile  payment devices, Wi-FI hotspot, etc. The  list goes on! In the QA world, these pose  unique challenges. For example, these  devices and applications need to perform  at speed and in various network conditions  while using different browser, operating  systems, and many more such conditions.  Club this mobile challenge with applica- tions and data hosted in a cloud environ- ment such as Microsoft Azure, Amazon  Web Services, to name few and you have  magnified the testing teams challenges by  manifold.<|endoftext|>Since most organizations are not really  equipped with mobile test labs, these are  some areas where they can tie up with  various vendors to perform mobile testing.  Another trend that helps overcome these  challenge is the adoption of newer meth- odologies such as agile Scrum, test-driven  development, behavior-driven develop- ment, and DevOps. However, most of these  methodologies demand progressive auto- mation or model-based testing concepts  where testers may need to be reskilled to  wear multiple hats.<|endoftext|>Risk-based testing (RBT) /  algorithm and combinatorial  testing  RBT is not a new concept and we all apply  it in almost every project, in one way or the  other. However, depending on the nature  of the project or applications, RBT can be  tricky and risky. QA and testing teams need  tools that can generate various permu- tations and combination to optimally  test and reduce the cost. For instance, in  mobile testing, you may come across many  operating systems and browsers, hence,  many permutations and combinations are  possible.<|endoftext|>Combinatorial testing is another tech- niques that has gained fresh momentum  in recent years and organizations can now  use tools to derive an optimal set of com- binations when attempting to test a huge  number of possible scenarios.<|endoftext|>Infrastructure testing The recent Galaxy Note 7 debacle costed  Samsung millions. And this is not a stray  incident. In fact, the list is endless, making  it important to thoroughly test the infra- structure. Many organizations now have  dedicated infrastructure testing teams  working in the shared service model. It is  recommended to review the infrastructure  testing needs and ensure that the services  are well aligned with the IT Infrastructure  teams who provision the internal and  external hardware needs such as VDI, Win- dows patches, databases, etc.  

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 External Document © 2018 Infosys Limited Automated testing As per the latest QA trends, automation  testing is now a necessary testing type  as against being optional, 5-7 years back.  Many leaders still question the value of  automation – what is the ROI? How is  automation directly benefiting, etc.? In my  view, the key is to do it right. For instance,  • Allocate automation funding for  applications and not seek funding from  projects to develop new automation  scripts and maintain the automation  framework  • Automate regression testing and not  functional testing • Establish application-specific regression  baseline   • Perform impact analysis using  predictive analytics as described earlier  and plan for automated testing at the  release level instead of on project basis  or simply, funded  • Track automation ROI and coverage  metrics and showcase the value of  automation as compared to manual  regression • Adopt and enable existing automation  to take up new methodology and  technology as it is related with Agile  Scrum, Test and Behavior driven  development, and DevOps model (as  discussed in the new-age testing saga  section) Test environment,  data and security The test environment, test data, and the  overall IT security challenges are the most  agreed upon and accepted challenges.  However, many organizations find it very  difficult to build multiple QA environments  and replicate their production processes.  While conducting a process maturity  assessment, I was surprised to hear that,  a bank spent more than USD1 million but  failed to implement a production-like  environment.  Part of the reason is a lack of planning and  budgeting itself, key to effective testing  environment. And if budgeted but not  approved, such initiatives take back seat  and the organization continues to solve  reactively instead of proactively.   In my experience, successful organizations  typically promote centralization of these  two functions and form test environment  and test data management teams. These  teams are responsible for ensuring the  right data in the right environment at the  right time. Both of these functions can  benefit from developing an operating  and engagement model that would allow  them to get funding, manage service  requests, and obtain the necessary access  to applications, jobs and data.  For security testing, there are many tools  available in market these days. But it is  recommended to look for tools that can  integrate with application development  and testing tools as well as support cloud  and mobile infrastructure.  

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 External Document © 2018 Infosys Limited Metrics, dashboard and  analytics Metrics and dashboard concepts are  not new but how the data is collected,  retrieved, processed, displayed, and finally,  analyzed to make informed decision has  surely changed. There are many tools now  in the market that can integrate with many  technology platforms and drill down the  capability in a very interactive manner.  Some tools that are gaining popularity  are Tableau, Quick View, etc. Many  organizations develop in-house tools or  leverage SharePoint as their metrics tool.  Whatever the choice of tool, below are  some key consideration that QA managers  and leaders would find beneficial: • Capture and communicate the key  performance indicators (KPIs) to senior  management on production and QA  defects, engagement feedback, cost  avoidance, and application level defect  density  • Define project level vs. aggregated view  of the metrics  • For multiple departments or lines of  business, apply consistent database  scheme   • Define standard folder structure in the  available QA or test management tools • Develop integration for analytics tool • Define key metrics to track and ensure  data accuracy and quality • Ensure automatic generation and  analytical capability to assist in decision  making • Develop QA specific predictive analytics,  for example, production and QA  defects data can be used to predict  potential areas for rigorous functional or  regression testing, an upcoming trend  

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 © 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected Conclusion In summary, it’s  more beneficial to know  “what” is happening at the macro level  rather than “why” it is happening at  the micro level.  While it’s important to  measure perfectly but due to huge amount  of QA data such as count of test cases  prepared, executed, effort consumed etc.,  it’s more beneficial to understand the  trend at high level and not the detailed  statistics. This will help define the Quality  in the context of an individual organization  as opposed to industry standard QA  definition. Such definition can then guide  organization specific QA metrics to collect,  new age testing types and methodology to  adopt as well as any consideration towards  automation, improvement in QA processes  and any supporting elements such as test  data and test environments build up.  
Author Mayank Jain  Principal Consultant   


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