Towards High-strength Combinatorial Interaction Testing for Highly Configurable Software Systems
This program is tentative and subject to change.
Highly configurable software systems are crucial in practice to satisfy the rising demand for software customization, and combinatorial interaction testing (CIT) is an important methodology for testing such systems. Constrained covering array generation (CCAG), as the core problem in CIT, is to construct a t-wise covering array (CA) of minimum size, where t represents the testing strength. Extensive studies have demonstrated that high-strength CIT (e.g., 4-wise and 5-wise CIT) has stronger fault detection capability than low-strength CIT (i.e., 2-wise and 3-wise CIT), and there exist certain critical faults that can be disclosed through high-strength CIT. Although existing CCAG algorithm has exhibited effectiveness in solving the low-strength CCAG problem, they suffer the severe high-strength challenge when solving 4-wise and 5-wise CCAG, which urgently calls for effective solutions to solving 4-wise and 5-wise CCAG problems. To alleviate the high-strength challenge, we propose a novel and effective local search algorithm dubbed HSCA. Particularly, HSCA incorporates three new and powerful techniques, i.e., multi-round CA generation mechanism, dynamic priority assigning technique, and variable grouping strategy, to improve its performance. Extensive experiments on 35 real-world and synthetic instances demonstrate that HSCA can generate significantly smaller 4-wise and 5-wise CAs than existing state-of-the-art CCAG algorithms. More encouragingly, among all 35 instances, HSCA successfully builds 4-wise and 5-wise CAs for 35 and 29 instances, respectively, including 11 and 15 instances where existing CCAG algorithms fail. Our results indicate that HSCA can effectively mitigate the high-strength challenge.
This program is tentative and subject to change.
Thu 1 MayDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | |||
14:00 15mTalk | Increasing the Effectiveness of Automatically Generated Tests by Improving Class ObservabilityAward Winner Research Track Geraldine Galindo-Gutierrez Centro de Investigación en Ciencias Exactas e Ingenierías, Universidad Católica Boliviana, Juan Pablo Sandoval Alcocer Pontificia Universidad Católica de Chile, Nicolas Jimenez-Fuentes Pontificia Universidad Católica de Chile, Alexandre Bergel University of Chile, Gordon Fraser University of Passau | ||
14:15 15mTalk | Invivo Fuzzing by Amplifying Actual Executions Research Track | ||
14:30 15mTalk | Towards High-strength Combinatorial Interaction Testing for Highly Configurable Software Systems Research Track Chuan Luo Beihang University, Shuangyu Lyu Beihang University, Wei Wu Central South University; Xiangjiang Laboratory, Hongyu Zhang Chongqing University, Dianhui Chu Harbin Institute of Technology, Chunming Hu Beihang University | ||
14:45 15mTalk | WDD: Weighted Delta Debugging Research Track Xintong Zhou University of Waterloo, Zhenyang Xu University of Waterloo, Mengxiao Zhang University of Waterloo, Yongqiang Tian Hong Kong University of Science and Technology, Chengnian Sun University of Waterloo | ||
15:00 15mTalk | TopSeed: Learning Seed Selection Strategies for Symbolic Execution from Scratch Research Track | ||
15:15 15mTalk | Hunting bugs: Towards an automated approach to identifying which change caused a bug through regression testing Journal-first Papers Michel Maes Bermejo Universidad Rey Juan Carlos, Alexander Serebrenik Eindhoven University of Technology, Micael Gallego Universidad Rey Juan Carlos, Francisco Gortázar Universidad Rey Juan Carlos, Gregorio Robles Universidad Rey Juan Carlos, Jesus M. Gonzalez-Barahona Universidad Rey Juan Carlos |