Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion
Adaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the ne...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9020057/ |
| _version_ | 1818736456690040832 |
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| author | Jinfu Chen Qihao Bao T. H. Tse Tsong Yueh Chen Jiaxiang Xi Chengying Mao Minjie Yu Rubing Huang |
| author_facet | Jinfu Chen Qihao Bao T. H. Tse Tsong Yueh Chen Jiaxiang Xi Chengying Mao Minjie Yu Rubing Huang |
| author_sort | Jinfu Chen |
| collection | DOAJ |
| description | Adaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the neighborhoods (or zones) of previously executed test cases. RRT has higher effectiveness than RT in terms of failure detection but incurs a higher time cost. In this paper, we aim to further reduce the time costs for RRT and improve the effectiveness for RT and ART methods. We propose a proactive technique known as “RRT by largest available zone” (RRT-LAZ). Like RRT, RRT-LAZ first defines an exclusion zone around every executed test case in order to determine the available zones. Unlike the original RRT, RRT-LAZ then compares all the available zones to proactively pick the largest one, from which the next test case is randomly generated. Both simulation analyses and empirical studies have been employed to investigate the efficiency and effectiveness of RRT-LAZ in relation to RT and related ART algorithms. The results show that RRT-LAZ has significantly lower time costs than RRT. Furthermore, RRT-LAZ is more effective than RT and related ART methods for block failure patterns in low-dimensional input spaces. In general, since RRT-LAZ employs a proactive technique instead of a passive one in generating next cases, it is much more cost-effective than RRT. RRT-LAZ is also more cost-effective than RT and other ART methods that we have studied. |
| first_indexed | 2024-12-18T00:37:27Z |
| format | Article |
| id | doaj.art-7c2921c1693c49348cd713da4edd835b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:37:27Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7c2921c1693c49348cd713da4edd835b2022-12-21T21:26:58ZengIEEEIEEE Access2169-35362020-01-018524755248810.1109/ACCESS.2020.29777779020057Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by ExclusionJinfu Chen0https://orcid.org/0000-0002-3124-5452Qihao Bao1T. H. Tse2Tsong Yueh Chen3Jiaxiang Xi4Chengying Mao5https://orcid.org/0000-0001-8178-1205Minjie Yu6Rubing Huang7https://orcid.org/0000-0002-1769-6126School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, ChinaDepartment of Computer Science, The University of Hong Kong, Hong KongDepartment of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC, AustraliaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, ChinaSchool of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, ChinaAdaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the neighborhoods (or zones) of previously executed test cases. RRT has higher effectiveness than RT in terms of failure detection but incurs a higher time cost. In this paper, we aim to further reduce the time costs for RRT and improve the effectiveness for RT and ART methods. We propose a proactive technique known as “RRT by largest available zone” (RRT-LAZ). Like RRT, RRT-LAZ first defines an exclusion zone around every executed test case in order to determine the available zones. Unlike the original RRT, RRT-LAZ then compares all the available zones to proactively pick the largest one, from which the next test case is randomly generated. Both simulation analyses and empirical studies have been employed to investigate the efficiency and effectiveness of RRT-LAZ in relation to RT and related ART algorithms. The results show that RRT-LAZ has significantly lower time costs than RRT. Furthermore, RRT-LAZ is more effective than RT and related ART methods for block failure patterns in low-dimensional input spaces. In general, since RRT-LAZ employs a proactive technique instead of a passive one in generating next cases, it is much more cost-effective than RRT. RRT-LAZ is also more cost-effective than RT and other ART methods that we have studied.https://ieeexplore.ieee.org/document/9020057/Software testingrandom testingadaptive random testingrestricted random testingexclusion zonelargest available zone |
| spellingShingle | Jinfu Chen Qihao Bao T. H. Tse Tsong Yueh Chen Jiaxiang Xi Chengying Mao Minjie Yu Rubing Huang Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion IEEE Access Software testing random testing adaptive random testing restricted random testing exclusion zone largest available zone |
| title | Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion |
| title_full | Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion |
| title_fullStr | Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion |
| title_full_unstemmed | Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion |
| title_short | Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion |
| title_sort | exploiting the largest available zone a proactive approach to adaptive random testing by exclusion |
| topic | Software testing random testing adaptive random testing restricted random testing exclusion zone largest available zone |
| url | https://ieeexplore.ieee.org/document/9020057/ |
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