Speaker: Prof. Meir Kalech
Title: AI for Software Quality Assurance
Modern software systems are highly complex and often have multiple dependencies on external parts such as other processes or services. This poses new challenges and exacerbate existing challenges in different aspects of software Quality Assurance (QA) including testing, debugging and repair. The goal of this talk is to present a novel AI paradigm for software QA (AI4QA). In particular, we discuss the new challenges that software QA poses, especially those that are due to the rapidly growing number of code revisions. These challenges do not allow the use of traditional manual QA methods and thus there is a need to automate the QA processes. We briefly review state-of-the-art AI techniques to address these challenges and present the AI4QA paradigm which integrates a range of AI techniques to guide human and computer QA efforts in a cost-effective manner: A quality assessment AI4QA uses machine-learning techniques to predict where coding errors are likely to occur. Then a test generation AI agent considers the error predictions to direct automated test generation. Then a test execution AI
agent executes tests, that are passed to the root-cause analysis AI agent, which applies automatic debugging algorithms. The candidate root causes are passed to a code repair AI agent that tries to create a patch for correcting the isolated error.
Meir Kalech completed his Ph.D. at the Computer Science Department of Bar-Ilan University in 2006. In 2008 he became a faculty member of the Department of Software and Information System Engineering at Ben-Gurion University of the Negev. Kalech's research interests lie in artificial intelligence and specifically in anomaly detection and diagnosis. Kalech established the Anomaly Detection and Diagnosis Lab (AiDnD) which integrates two main AI approaches: model-based and data driven. He is a recognized expert in model-based diagnosis (MBD) and has published dozens of papers in leading journals and refereed conferences. In the past seven years Kalech promotes research that implements these approaches for software engineering tasks such as debugging and testing. Kalech's lab promotes cooperation research with the government and leading corporations such as General Motors, Mekorot and IBM. Among the research of Kalech exist anomaly detection of Supervisory Control And Data Acquisition (SCADA) systems, Automated debugging, survival analysis and troubleshooting. Kalech has served as a senior program committee in leading AI conferences such as AAAI, IJCAI, AAMAS and the International Workshop on Principles of Diagnosis.