In this talk, we will discuss leveraging Machine Learning practices in Software Testing with several practical examples and a case study that I used in my project to do Bug Triage. Let's embrace the future together!
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:
Results & Conclusion:
We see how ML helps in all stages. I summarize the application areas with algorithms and discuss 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. I introduce the applications in different testing stages, making it easy for the audience to find what they want.
Mesut在工業自動化、物聯網平台、SaaS/PaaS和雲服務、國防工業、自主移動機器人以及嵌入式和軟件應用方面擁有15年以上的經驗。除了精通CMMI和Scrum & PMP經驗外，他還在跨國項目中擔任過各種角色。他在CI/CD管道中具有測試自動化和持續測試的專業知識。此外，他還一直在促進測試流程並建立項目的測試策略和生命週期。他是一位經常參加國際會議的演講者，最佳演講獎得主，也是各種程序委員會的成員。他在100多個國際會議上發表過演講（6大洲，25個以上的國家：其中10個以上是親自參加的）。