We have for several years collected a vast majority of data through the software development life cycle. It encompasses an enormous amount of unstructured data spreading over project or delivery phases: planning, requirement, development, testing and operation. At the same time Business demands improvement within test efficiency and effectiveness.
The raise of AI technology embracing Data Analytics and Machine Learning has recently been warmly discussed and widely adopted in several fields and services: financial, marketing, commercial, etc… AI technology is a huge scientific research field and practices. It might somehow be considered too hard to comprehend and operationalize. The same challenge is applied when it comes to adoptation of AI to Software Development and Testing.
In this talk, we would like to present our experiences in adopting Machine Learning algorithms to predict the risk score associated to Requirement based on accessible historical data on test execution, defects and production incidents. Such insights into risk score of the Requirement to be developed will undoubtedly contribute in effective test planning and prioritization.
Målgruppe: Testleder, tester, data scientist, IT-leder, QA ansvarlig, osv…