ICST 2024
Mon 27 - Fri 31 May 2024 Canada

This program is tentative and subject to change.

Mon 27 May 2024 15:00 - 15:30 at Room 4 - Session 3

Testing and verification of reinforcement learning policies are becoming ever more important. One of the open questions for testing such policies is how to determine test adequacy. Neuron activation has been proposed both as a metric for determining test adequacy, as well as for steering the test-case generation. However, recent studies have shown that increasing neuron coverage is not necessarily beneficial and might even be harmful. In this paper, we add an additional take on the evaluation of neuron coverage as a metric. We present different approaches to selecting test cases based on a Markov decision process, which is generated via model learning. We evaluate and compare the efficiency as well as the neuron activation achieved by each of the test suites. The approach is demonstrated on an RL agent playing Super Mario Bros. The results show that an intelligent selection of test cases leads to higher failure detection by the test cases, but does not imply high neuron coverage.

This program is tentative and subject to change.

Mon 27 May

Displayed time zone: Eastern Time (US & Canada) change

14:00 - 15:30
Session 3A-MOST at Room 4
14:00
30m
Full-paper
Active Model Learning for Software Interrogation and Diagnosis
A-MOST
Adam Porter University of Maryland, alan Karr
14:30
30m
Short-paper
Active Model Learning of Git Version Control System
A-MOST
Edi Muskardin , Tamim Burgstaller , Martin Tappler TU Wien, Austria, Bernhard Aichernig Graz University of Technology
15:00
30m
Full-paper
Bridging the Gap Between Models in RL: Test Models vs. Neural Networks
A-MOST
Martin Tappler TU Wien, Austria, Florian Lorber Silicon Austria Labs