NDLI: TCA: An Efficient Two-Mode Meta-Heuristic Algorithm for Combinatorial Test Generation (T)

Content Provider	IEEE Xplore Digital Library
Author	Jinkun Lin Chuan Luo Shaowei Cai Kaile Su Dan Hao Lu Zhang
Copyright Year	2015
Description	Author affiliation: State Key Lab. of Comput. Sci., Inst. of Software, Beijing, China (Shaowei Cai) \|\| Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing, China (Jinkun Lin; Chuan Luo; Dan Hao; Lu Zhang) \|\| Dept. of Comput. Sci., Jinan Univ., Guangzhou, China (Kaile Su)
Abstract	Covering arrays (CAs) are often used as test suites for combinatorial interaction testing to discover interaction faults of real-world systems. Most real-world systems involve constraints, so improving algorithms for covering array generation (CAG) with constraints is beneficial. Two popular methods for constrained CAG are greedy construction and meta-heuristic search. Recently, a meta-heuristic framework called two-mode local search has shown great success in solving classic NPhard problems. We are interested whether this method is also powerful in solving the constrained CAG problem. This work proposes a two-mode meta-heuristic framework for constrained CAG efficiently and presents a new meta-heuristic algorithm called TCA. Experiments show that TCA significantly outperforms state-of-the-art solvers on 3-way constrained CAG. Further experiments demonstrate that TCA also performs much better than its competitors on 2-way constrained CAG.
Starting Page	494
Ending Page	505
File Size	243080
Page Count	12
File Format	PDF
e-ISBN	9781509000258
DOI	10.1109/ASE.2015.61
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software Heuristic algorithms Search problems Testing Algorithm design and analysis Software algorithms Computer science
Content Type	Text
Resource Type	Article

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