The Future is Today: Leveraging AI in Software Testing

Mesut will talk about leveraging Machine Learning practices in Software Testing with several practical examples and share a case study that he used in his project to do Bug Triage. 

Testing is very cumbersome. Agile is open to changes, which means expected behaviors can change over time. Besides, due to implementation changes, tests may be broken. And most importantly, time is very precious and limited. Manual efforts should be minimized to improve coverage and reserve more time for exploratory activities with limited resources.

Manual effort can be reduced and testing can be done in a more convenient and consistent way by applying ML. Stages in which ML is applicable are:

• Test definition.
• Automatic code generation.
• Execution: exploratory testing.
• Maintenance and grouping.
• Review test code.
• Heal broken test code.
• Prioritize test cases.
• Bug Triage.

We see how ML helps in all stages. The speaker summarizes the application areas with algorithms and discusses AI applications' advantages and potential risks in software testing. To sum up, this talk targets an important problem – AI-based applications of software testing. AI is one of the hottest topics in the software world nowadays. Especially, mining valuable information from bugs can be made use of by managers to guide feature priorities. Mesut introduces the applications in different testing stages, making it easy for the audience to find what they want.

After the talk, we will have seen how Machine Learning can be used to:

• generate test cases automatically;
• review test code;
• heal broken test code;
• prioritize test cases;
• perform exploratory testing;
• manage bugs.