Generating Class-Level Integration Tests Using Call Site Information

P. Derakhshanfar, Xavier Devroey, A. Panichella, A.E. Zaidman, A. van Deursen

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Abstract

Search-based approaches have been used in the literature to automate the process of creating unit test cases. However, related work has shown that generated tests with high code coverage could be ineffective, i.e., they may not detect all faults or kill all injected mutants. In this paper, we propose <sc>Cling</sc> , an integration-level test case generation approach that exploits how a pair of classes, the caller and the callee, interact with each other through method calls. In particular, <sc>Cling</sc> generates integration-level test cases that maximize the Coupled Branches Criterion (CBC). Coupled branches are pairs of branches containing a branch of the caller and a branch of the callee such that an integration test that exercises the former also exercises the latter. CBC is a novel integration-level coverage criterion, measuring the degree to which a test suite exercises the interactions between a caller and its callee classes. We implemented <sc>Cling</sc> and evaluated the approach on 140 pairs of classes from five different open-source Java projects. Our results show that (1) <sc>Cling</sc> generates test suites with high CBC coverage, thanks to the definition of the test suite generation as a many-objectives problem where each couple of branches is an independent objective; (2) such generated suites trigger different class interactions and can kill on average 7.7&#x0025; (with a maximum of 50&#x0025;) of mutants that are not detected by tests generated randomly or at the unit level; (3) <sc>Cling</sc> can detect integration faults coming from wrong assumptions about the usage of the callee class (25 for our subject systems) that remain undetected when using automatically generated random and unit-level test suites.

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalIEEE Transactions on Software Engineering
DOIs
Publication statusE-pub ahead of print - 2022

Keywords

  • search-based software engineering
  • Class Integration testing
  • coverage criteria
  • evolutionary algorithms
  • Many-objective optimization

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