Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2021 IEEE/ACM 43rd International Conference on Software Engineering, ICSE 2021 |
| Publisher | IEEE Computer Society |
| Pages | 201-212 |
| Number of pages | 12 |
| ISBN (electronic) | 9780738113197 |
| ISBN (print) | 978-1-6654-0296-5 |
| Publication status | Published - May 2021 |
| Event | 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021 - Virtual, Online, Spain Duration: 22 May 2021 → 30 May 2021 |
Publication series
| Name | Proceedings - International Conference on Software Engineering |
|---|---|
| ISSN (Print) | 1558-1225 |
| ISSN (electronic) | 0270-5257 |
Abstract
Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.
Keywords
- Automated Algorithm Optimization, Constrained Covering Array Generation
ASJC Scopus subject areas
- Computer Science(all)
- Software
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Proceedings - 2021 IEEE/ACM 43rd International Conference on Software Engineering, ICSE 2021. IEEE Computer Society, 2021. p. 201-212 (Proceedings - International Conference on Software Engineering).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - AutoCCAG
T2 - 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021
AU - Luo, Chuan
AU - Lin, Jinkun
AU - Cai, Shaowei
AU - Chen, Xin
AU - He, Bing
AU - Qiao, Bo
AU - Zhao, Pu
AU - Lin, Qingwei
AU - Zhang, Hongyu
AU - Wu, Wei
AU - Rajmohan, Saravanakumar
AU - Zhang, Dongmei
PY - 2021/5
Y1 - 2021/5
N2 - Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.
AB - Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.
KW - Automated Algorithm Optimization
KW - Constrained Covering Array Generation
UR - http://www.scopus.com/inward/record.url?scp=85115669653&partnerID=8YFLogxK
U2 - 10.1109/ICSE43902.2021.00030
DO - 10.1109/ICSE43902.2021.00030
M3 - Conference contribution
AN - SCOPUS:85115669653
SN - 978-1-6654-0296-5
T3 - Proceedings - International Conference on Software Engineering
SP - 201
EP - 212
BT - Proceedings - 2021 IEEE/ACM 43rd International Conference on Software Engineering, ICSE 2021
PB - IEEE Computer Society
Y2 - 22 May 2021 through 30 May 2021
ER -