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ICST 2022
Mon 4 - Fri 8 April 2022
Tue 5 Apr 2022 15:45 - 16:00 at Margaret Hamilton - ICST AI I Chair(s): Raihana Ferdous

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 Apr

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:45 - 16:45
ICST AI IResearch Papers / Industry at Margaret Hamilton
Chair(s): Raihana Ferdous Fondazione Bruno Kessler
IFRIT: Focused Testing through Deep Reinforcement Learning
Research Papers
Andrea Romdhana DIBRIS - University of Genoa, FBK - Security & Trust unit, Mariano Ceccato University of Verona, Alessio Merlo DIBRIS - University of Genoa, Paolo Tonella USI Lugano
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.
Evaluating Features for Machine Learning Detection of Order- and Non-Order-Dependent Flaky Tests
Research Papers
Owain Parry The University of Sheffield, Gregory Kapfhammer Allegheny College, Michael Hilton Carnegie Mellon University, USA, Phil McMinn University of Sheffield
Live Q&A
Discussion and Q&A
Research Papers