Software is constantly changing as developers add new features or make changes. This directly impacts the effectiveness of the test suite associated with that software, especially when the new modifications are in an area where no test case exists. This article addresses the issue of developing a high-quality test suite to repeatedly cover a given point in a program, with the ultimate goal of exposing faults affecting the given program point. Our approach, IFRIT, uses Deep Reinforcement Learning to generate diverse inputs while keeping a high level of reachability of the desired program point. IFRIT achieves better results than state-of-the-art and baseline tools, improving reachability, diversity and fault detection.
Tue 5 AprDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
15:45 - 16:45
|IFRIT: Focused Testing through Deep Reinforcement Learning|
|Robustness assessment and improvement of a neural network for blood oxygen pressure estimation|
Paolo Arcaini National Institute of Informatics , Andrea Bombarda University of Bergamo, Silvia Bonfanti University of Bergamo, Angelo Gargantini University of Bergamo, Daniele Gamba AISent S.r.l., Rita Pedercini AISent S.r.l.Pre-print
|Evaluating Features for Machine Learning Detection of Order- and Non-Order-Dependent Flaky Tests|
|Discussion and Q&A|