References of "Vivanti, Mattia"
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See detailA Detailed Investigation of the Effectiveness of Whole Test Suite Generation
Rojas, José Miguel; Vivanti, Mattia; Arcuri, Andrea UL et al

in Empirical Software Engineering (2016)

A common application of search-based software testing is to generate test cases for all goals defined by a coverage criterion (e.g., lines, branches, mutants). Rather than generating one test case at a ... [more ▼]

A common application of search-based software testing is to generate test cases for all goals defined by a coverage criterion (e.g., lines, branches, mutants). Rather than generating one test case at a time for each of these goals individually, whole test suite generation optimizes entire test suites towards satisfying all goals at the same time. There is evidence that the overall coverage achieved with this approach is superior to that of targeting individual coverage goals. Nevertheless, there remains some uncertainty on (a) whether the results generalize beyond branch coverage, (b) whether the whole test suite approach might be inferior to a more focused search for some particular coverage goals, and (c) whether generating whole test suites could be optimized by only targeting coverage goals not already covered. In this paper, we perform an in-depth analysis to study these questions. An empirical study on 100 Java classes using three different coverage criteria reveals that indeed there are some testing goals that are only covered by the traditional approach, although their number is only very small in comparison with those which are exclusively covered by the whole test suite approach. We find that keeping an archive of already covered goal with corresponding tests and focusing the search on uncovered goals overcomes this small drawback on larger classes, leading to an improved overall effectiveness of whole test suite generation. [less ▲]

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See detailCombining Multiple Coverage Criteria in Search-Based Unit Test Generation
Rojas, Miguel; Campos, Jose; Vivanti, Mattia et al

in Symposium on Search-Based Software Engineering (SSBSE) (2015)

Automated test generation techniques typically aim at max- imising coverage of well-established structural criteria such as statement or branch coverage. In practice, generating tests only for one speci c ... [more ▼]

Automated test generation techniques typically aim at max- imising coverage of well-established structural criteria such as statement or branch coverage. In practice, generating tests only for one speci c criterion may not be su cient when testing object oriented classes, as standard structural coverage criteria do not fully capture the properties developers may desire of their unit test suites. For example, covering a large number of statements could be easily achieved by just calling the main method of a class; yet, a good unit test suite would consist of smaller unit tests invoking individual methods, and checking return values and states with test assertions. There are several di erent properties that test suites should exhibit, and a search-based test generator could easily be extended with additional tness functions to capture these properties. However, does search-based testing scale to combinations of multiple cri- teria, and what is the e ect on the size and coverage of the resulting test suites? To answer these questions, we extended the EvoSuite unit test generation tool to support combinations of multiple test criteria, de ned and implemented several di erent criteria, and applied combinations of criteria to a sample of 650 open source Java classes. Our experiments suggest that optimising for several criteria at the same time is feasible without increasing computational costs: When combining nine di erent criteria, we observed an average decrease of only 0.4% for the constituent coverage criteria, while the test suites may grow up to 70%. [less ▲]

Detailed reference viewed: 114 (8 UL)