Overlap-aware rapid type analysis for constructing one-to-one matched call graphs in regression test selection

Mingwan Kim, Jongwook Jeong, Neunghoe Kim, Hoh Peter In

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)423-432
Number of pages10
JournalIET Software
Issue number4
Publication statusPublished - 2020 Aug 1

Bibliographical note

Funding Information:
This research was supported by the MIST (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (2015-0-00936) supervised by the IITP (Institute for Information & Communications Technology Promotion) and Samsung Electronics Co. Ltd.

Publisher Copyright:
© The Institution of Engineering and Technology 2020

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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