A Deep Reinforcement Learning Approach to Configuration Sampling Problem

A Deep Reinforcement Learning Approach to Configuration Sampling Problem

Abolfazli, Amir (L3S Research Center)
Spiegelberg, Jakob (Volkswagen AG)
Anand, A. (TU Delft Web Information Systems)
Palmer, Gregory (L3S Research Center)

O’Conner, Lisa (editor)

2023

Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the challenge of tractably finding potentially faulty configurations given exponentially large configurations. To overcome this problem, prior works focused on sampling strategies to significantly reduce the number of generated configurations, guaranteeing a high t-wise coverage. In this work, we address the configuration sampling problem by proposing a deep reinforcement learning (DRL) based sampler that efficiently finds the trade-off between exploration and exploitation, allowing for the efficient identification of a minimal subset of configurations that covers all t-wise feature interactions while minimizing redundancy. We also present the CS-Gym, an environment for the configuration sampling. We benchmark our results against heuristic-based sampling methods on eight different feature models of software product lines and show that our method outperforms all sampling methods in terms of sample size. Our findings indicate that the achieved improvement has major implications for cost reduction, as the reduction in sample size results in fewer configurations that need to be tested.

Deep learning
Costs
Software algorithms
Reinforcement learning
Sampling methods
Software systems
Software product lines
reinforcement learning
configuration sampling
software testing

http://resolver.tudelft.nl/uuid:e0a3c3b1-99aa-47de-956f-19e2172f5b0b

https://doi.org/10.1109/ICDM58522.2023.00009

IEEE, Piscataway

2024-08-05

979-8-3503-0789-4

Proceedings of the 2023 IEEE International Conference on Data Mining (ICDM)

2023 IEEE International Conference on Data Mining (ICDM), 2023-12-01 → 2023-12-04, Shanghai, China

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 Amir Abolfazli, Jakob Spiegelberg, A. Anand, Gregory Palmer