Fault coverage-based test suite optimization method for regression testing: learning from mistakes-based approach

被引:4
|
作者
Agrawal, Arun Prakash [1 ]
Choudhary, Ankur [1 ]
Kaur, Arvinder [2 ]
Pandey, Hari Mohan [3 ]
机构
[1] Amity Univ Uttar Pradesh, Amity Sch Engn & Technol, Noida, India
[2] Guru Gobind Singh Indraprastha Univ, USIC & T, New Delhi, India
[3] Edge Hill Univ, Ormskirk, England
来源
NEURAL COMPUTING & APPLICATIONS | 2020年 / 32卷 / 12期
关键词
Regression testing; Software maintenance; Heuristics; Greedy; Additional Greedy; HGS; Enhanced HGS; TEST-CASE PRIORITIZATION; TEST SELECTION; ALGORITHMS; SOFTWARE;
D O I
10.1007/s00521-019-04098-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a novel method referred as fault coverage-based test suite optimization (FCBTSO) for regression test suite optimization. FCBTSO is proposed based on Harrolds-Gupta-Soffa (HGS) test suite reduction method, and it follows the phenomenon: "learning from mistakes". We conducted computational experiments on 12 versions of benchmarked programs retrieved from software artefact infrastructure repository and dummy fault matrix test. The performance of the proposed FCBTSO is measured against the traditional test suite reduction methods (Greedy method, Additional Greedy, HGS, and Enhanced HGS) by following the performance measures: fault coverage, execution time and reduced optimized test suite size. Rigorous statistical tests are conducted to determine the performance significance, which indicates that FCBTSO outperforms other approaches implemented with respect to the execution time that includes the execution time of the proposed approach to find the optimized test suite and the execution time of test cases in the optimized test suite.
引用
收藏
页码:7769 / 7784
页数:16
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