Search Based Test Generation: from Programmed Systems to Systems that Learn

Search Based Test Generation: from Programmed Systems to Systems that Learn
Speaker: Prof. Paolo Tonella (Università della Svizzera Italiana)

Search based algorithms are flexible tools to find approximate solutions to arbitrary optimization problems, modelled as one or more fitness functions to be minimized or maximized. Among them, genetic algorithms have been extensively investigated with the aim of automatically generating the test inputs necessary to meet the desired test adequacy level. After summarising some structural test adequacy criteria, I will present the usage of genetic algorithms for the generation of unit test cases that cover all the branches of the class under test. Then, I will focus on alternative problem formulations that try to address the problem of infeasible and difficult test targets. In particular, I will describe a novel many objective sorting algorithm that addresses such problem by means of many-objective optimization based on a custom ranking function. In the last part of my talk, I will present the recently launched ERC project Precrime (https://www.pre-crime.eu). The project deals with systems whose behaviour is learned from data (e.g., via deep or reinforcement learning) rather than being programmed by logical rules. Ensuring the dependability of such systems is a major challenge for software testing. I will argue and provide preliminary evidence on the possibility to apply successfully search based algorithms for test generation in such new domain.

Short bio:
Paolo Tonella is Full Professor at the Faculty of Informatics and at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He is Honorary Professor at University College London, UK and he is Affiliated Fellow of Fondazione Bruno Kessler, Trento, Italy, where he has been Head of Software Engineering until mid 2018. Paolo Tonella holds an ERC Advanced grant as Principal Investigator of the project PRECRIME. Paolo Tonella wrote over 150 peer reviewed conference papers and over 50 journal papers. His H-index (according to Google scholar) is 54. He is/was in the editorial board of the ACM Transactions on Software Engineering and Methodology, of the IEEE Transactions on Software Engineering, of Empirical Software Engineering, Springer, and of the Journal of Software: Evolution and Process, Wiley. His current research interests are in software testing, in particular approaches to ensure the dependability of machine learning based systems, automated testing of web applications, and test oracle inference and improvement.