In this tutorial, we see how natural language processing and machine learning can help us address the open challenges of software testing. We overview the open challenges of testing autonomous and self-adaptive software systems, discuss the leading-edge technologies that can address the core issues, and see the latest progresses and future prospective of natural language processing and machine learning to cope with core problems.

Automating test case and oracle generation are still largely open issues. Autonomous and self-adaptive systems, like self-driving cars, smart cities, and smart buildings, raise new issues that further toughen the already challenging scenarios. In the tutorial we understand the growing importance of field testing to address failures that emerge in production, the role of dynamic analysis and deep learning in revealing failure-prone scenarios, the need of symbolic fuzzing to explore unexpected scenarios, and the potentiality of reinforcement learning and natural language processing to generate test cases and oracles. We see in details state-of-the-art approaches that exploit natural language processing to automatically generate executable test oracles, as well as semantic matching, deep and reinforcement learning to automatically generate test cases and reveal failure-prone scenarios in production.

The tutorial is designed for both researchers, whose research roadmap focuses on software testing and applications of natural language processing and machine learning to software engineering, and practitioners, who see important professional opportunities from autonomous and self-adaptive systems. It is particularly well suited to PhD students and postdoctoral researchers who aim to address new challenges with novel technologies. The tutorial is self-contained, and is designed for a software engineering audience, who many not have a specific background in natural language processing and machine learning.

Mauro Pezzè is a professor of software engineering at USI - Università della Svizzera Italiana, Lugano, and SIT Schaffhausen Institute of Technology, Schaffhausen, Switzerland. Mauro Pezzè coordinates the STAR - Software Testing and Analysis research Lab, a joint research team at USI and SIT. Mauro Pezzè is editor in chief of ACM TOSEM Transactions on Software Engineering and Methodology, and served in the editorial board of IEEE TSE Transactions on Software Engineering and STVR, the International Journal of Software Testing, Analysis and Verification. He served as program chair of ICSE, the International Conference on Software Engineering, in 2012, and program and general chair of ISSTA, the ACM International Symposium on Software Testing and Analysis, in 2006 and 2013, respectively. He is the co-author of an influential book ‘Software Testing and Analysis, Process, Principle and Techniques, and is known for his work on software testing, program analysis, self-healing and self-adaptive software systems.