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Sogeti’s Strategy for AI & RPA in Testing

 

We recently caught up with Sogeti, a subsidiary of Capgemini, to discuss the use of AI and RPA in software testing.

In testing, AI use cases are focusing on making sense of the data generated by testing and from ITSM and production tools. For RPA, adoption of RPA workflows and chatbots in automating testing services has to date been minimal.

Continued investment in Cognitive QA through AI use cases and UX

Earlier this year, we commented on the 2017 launch by Sogeti of its Cognitive QA IP. Sogeti had developed several AI use cases in areas including test case optimization and prioritization, and defect prediction. Sogeti continues to invest in Cognitive QA to gain further visibility around test execution. Recent use cases include:

  • Improving test coverage through mapping test cases with test requirements, and defects with test cases
  • Predicting performance defects based on a release analysis, and identifying the code that is impacting performance
  • Conducting what-if analyses to assess the impact of a risk-based approach to defect levels and test coverage
  • Managing test projects by gaining productivity data, e.g. how long it takes to identify a defect, to share it with the development team, and to fix it.

Sogeti, like most of its peers, also continues to invest in sentiment analysis capabilities. The principle of sentiment analysis tools in the context of testing is to cluster data across several keywords, e.g. functional defect, UX defect. Sogeti is working on translating its sentiment analysis into actionable feedback to developers.

The company is finding that these AI use cases are a good door-opener with clients and open new broader discussions on test & development and data quality: with the increased adoption of agile methodologies and DevOps open source tools, the fragmentation of tools used in the SDLC is impacting the quality and comprehensiveness of data.

Bringing UX to AI use cases

While we were expecting Sogeti to maintain its focus on AI use cases, we had not expected that Sogeti is also focusing on the UX. The first step in this journey was straightforward, with Cognitive QA being accessible on mobile devices and going beyond a responsive website approach, e.g. creating automated alerts for not meeting SLAs, and automatically setting up emergency meetings.

Sogeti is also bringing virtual agents into Cognitive QA. It offers access to the IP through both voice and chatbot interfaces. With this feature, test managers can access information, e.g. number of cases to be tested for the next release of an application, which one, and what level of prioritization. The solution handles interaction through the virtual agents of Microsoft (Cortana) and Skype, IBM (Watson Virtual Agent), AWS (Alexa), and Google Home. Sogeti has deployed this virtual agent approach with two clients, with implementation time taking between two to three months.

Another aspect of Sogeti’s investment outside of a pure AI use case approach is its project health approach. Cognitive QA integrates with financial applications/ERPs. The intention is to provide a view on the financial performance of a given testing project and integrate with HR systems to help source testing personnel across units.

Deploying RPA workflows to automate testing services

The other side of automation is RPA. We have mentioned several times the similarities between RPA tools and test execution tools, and the fact that they share the same UI approach (see the blog RPA Can Benefit from Testing Services’ Best Practices & Experience for more information). The world of testing execution software and RPA workflow tools is converging with several testing ISVs now launching their RPA software products. Several of Sogeti’s clients are using their RPA licenses to automate testing. The frontier between testing execution and RPA is about to become porous.

We have not historically seen extensive use of RPA tools to automate manual testing activities. To a large extent, the wealth of testing software tools has been comprehensive enough to avoid further investment in software product licenses. Sogeti is indicating that this is now changing: with several clients it is using RPA to automate activities related to test data management, test environment provisioning, or real-time reporting. This is all about a business case: these clients are making use of their existing RPA software licenses rather than buying additional specialized software from the likes of CA. To that end, Sogeti has been building its RPA testing-focused capabilities internally, and has ~100 test automation engineers now certified on UiPath and Blue Prism.

AI and RPA are only one of the current priorities for testing services

The future of testing services goes beyond the use of AI and RPA: there is much more. One major push currently is adopting agile methodologies and DevOps tools, and re-purposing TCoEs to become more automated and more technical. And there is also UX testing, which itself is a massive topic, and requiring investment in automation. The reinvention of testing services continues.

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