Overlap-aware rapid type analysis for constructing one-to-one matched call graphs in regression test selection
- Authors
- Kim, Mingwan; Jeong, Jongwook; Kim, Neunghoe; In, Hoh Peter
- Issue Date
- 8월-2020
- Publisher
- INST ENGINEERING TECHNOLOGY-IET
- Keywords
- Java; program testing; software maintenance; regression analysis; overlap-aware rapid type analysis; regression test selection; regression testing; important but costly activity; affected test cases; call graphs; ORTA; redundant cost; matching selected 46; fewer test cases
- Citation
- IET SOFTWARE, v.14, no.4, pp.423 - 432
- Indexed
- SCIE
SCOPUS
- Journal Title
- IET SOFTWARE
- Volume
- 14
- Number
- 4
- Start Page
- 423
- End Page
- 432
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/53901
- DOI
- 10.1049/iet-sen.2018.5442
- ISSN
- 1751-8806
- Abstract
- Regression testing is an important but costly activity for verifying a programme with the changed code. Regression test selection (RTS) aims to reduce this cost by selecting only the test cases affected by the changes. Among the several ways of selecting such affected test cases, call graphs have been statically constructed to select the test cases at the method-level granularity. However, RTS techniques will reduce the cost of regression testing less than expected unless the call graphs are efficiently one-to-one matched with the test cases. In this study, the authors propose overlap-aware rapid type analysis (ORTA). ORTA is designed to minimise the redundant cost of creating the matched call graphs using rapid type analysis (RTA). The one-to-one matching and ORTA were evaluated on 1487 commits selected from 30 Java projects. RTA-based RTS with the one-to-one matching selected 46.90% fewer test cases with 2.76% longer end-to-end time of regression testing than without the one-to-one matching. The time increased with the one-to-one matching was reduced by 22.58% when ORTA substituted for RTA. ORTA achieved the cost reduction while removing 82.77% of the duplicate edges that RTA created on 993 commits.
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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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