Work

Metamorphic relations via relaxations: an approach to obtain oracles for action-policy testing

Year: 2022

Type: conference-paper

Abstract: Testing is a promising way to gain trust in a learned action policy π, in particular if π is a neural network. A “bug” in this context constitutes undesirable or fatal policy behavior, e.g., satisfying a failure condition. But how do we distinguish whether such behavior is due to bad policy decisions, or whether it is actually unavoidable under the given circumstances? This requires knowledge about optimal solutions, which defeats the scalability of testing. Related problems occur in software testing when the correct program output is not known.

Source: ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

DOI: 10.1145/3533767.3534392

Authors: Hasan Ferit Eniser, Timo P. Gros, Valentin Wüstholz, Jörg Hoffmann, Maria Christakis

Institutions: Max Planck Institute for Software Systems, Saarland University, ConsenSys (United States), German Research Centre for Artificial Intelligence

Language: English

Topic: Software Testing and Debugging Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Deutsche Forschungsgemeinschaft

Awards: G3032849772, TRR 248: Grundlagen verständlicher SoftwareSysteme für eine nachvollziehbare cyberphysische Welt