Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE International Conference on Data Mining |
| Subtitle of host publication | ICDM |
| Editors | Guihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-10 |
| Number of pages | 10 |
| ISBN (electronic) | 9798350307887 |
| ISBN (print) | 979-8-3503-0789-4 |
| Publication status | Published - 2023 |
| Event | 23rd IEEE International Conference on Data Mining, ICDM 2023 - Shanghai, China Duration: 1 Dec 2023 → 4 Dec 2023 |
Abstract
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.
Keywords
- configuration sampling, reinforcement learning, software testing
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
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2023 IEEE International Conference on Data Mining: ICDM. ed. / Guihai Chen; Latifur Khan; Xiaofeng Gao; Meikang Qiu; Witold Pedrycz; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1-10.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Deep Reinforcement Learning Approach to Configuration Sampling Problem
AU - Abolfazli, Amir
AU - Spiegelberg, Jakob
AU - Palmer, Gregory
AU - Anand, Avishek
N1 - Funding Information: The authors gratefully acknowledge that the proposed research is a result of the research project QuBRA granted by the BMBF via funding code 13N16052.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - configuration sampling
KW - reinforcement learning
KW - software testing
UR - http://www.scopus.com/inward/record.url?scp=85185398160&partnerID=8YFLogxK
U2 - 10.1109/ICDM58522.2023.00009
DO - 10.1109/ICDM58522.2023.00009
M3 - Conference contribution
AN - SCOPUS:85185398160
SN - 979-8-3503-0789-4
SP - 1
EP - 10
BT - 2023 IEEE International Conference on Data Mining
A2 - Chen, Guihai
A2 - Khan, Latifur
A2 - Gao, Xiaofeng
A2 - Qiu, Meikang
A2 - Pedrycz, Witold
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Data Mining, ICDM 2023
Y2 - 1 December 2023 through 4 December 2023
ER -