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The future of testing built on Predictive Analytics

Luke Barfield, Head of Testing and QA, P2 Consulting

22.11.18

The use of Artificial Intelligence (AI) in testing is still in its infancy. Organisations are keen to apply smart analytics for critical decision making and to optimise testing activities to achieve maximum quality with lower costs (in terms of both time and money). But are yet to find a complete answer. Some testing professionals are rightly sceptical of a future where all testing will be carried out by machines, but to ignore the potential for AI to supplement testing activities (rather than replacing it) would be foolish.

At P2 Consulting we’re specifically excited about the applications of AI for Predictive Analytics and my colleagues Richard Rikards and Adam Skinner have already written about its application in our Programme Management Office (PMO) practice .

Within our Testing & QA practice, I believe that Predictive Analytics is a must for organisations over the coming 2-3 years. Initially focusing on helping to make the key decisions in the testing process, such as which tests to run and how many tests should run for a release so that testing can be really targeted to maximise the use of limited resources without taking excessive risks to production quality.

Testing today…

There are already solutions available today that can identify business risk factors, automatically select and prioritise test cases, inform test case design, and even automatically assign tasks to the most productive test professionals to reduce costs and time to market. However, the success of these solutions is dependent on the data an organisation collects for its testing process and its ability to mine this data.

The obvious candidate for data mining is the application lifecycle management (ALM) and / or test management tools. Metrics such as test coverage, business risk coverage, execution and defect data can all provide data-points from current and past projects. But to further enrich the predictive capability we should look to increase the dataset further with details of code check-ins, code quality, unit test coverage, production data, log files, etc. Using this larger, more diverse dataset we can then apply machine learning techniques to predict the areas of risk, where defects are most likely to be found and the effort required to sufficiently test a product release.

We may also be able to enhance a tester’s spidey senses by analysing large datasets (i.e. log files) and providing accurate signal to noise detection therefore alerting the tester to the presence of a software bug where previously they would have missed the signs, working alone.

Organisations need to define a strategy to apply this to their testing. It needs to define the outcomes and success criteria; identify the datapoints required and a process to ensure that they are collected. It also needs to address the changing skillsets required from the Test & QA professionals such as: development / programming skills, natural language processing, data science, mathematics, algorithmic knowledge, and machine learning. It can present a maturity assessment of their current capabilities and provide a roadmap and success criteria to measure progress against objectives.

To learn more about how we can help your business with Predictive Analytics, please contact us today:

Luke Barfield, Head of Testing and QA
luke.barfield@p2consulting.com
+44 (0) 7837 259780