UTBot Java at the SBST2022 tool competition

Ivanov, Dmitry; Menshutin, Alexey; Fokin, D. A.; Kamenev, Yury; Pospielov, Sergii; Куликов, Е А; Stroganov, Nikita

doi:10.1145/3526072.3527529

Cited by 4 publications

(2 citation statements)

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“…As illustrated in Table 1, several tools have entered the competition over the years. Figure 4 presents the averaged instruction and branch coverage, and averaged mutation score of the different tools per year, collected from the reports of the past editions [17–48,49–54]. As can be seen from the Figure, EvoSuite has the best averaged structural coverage and mutation scores over the years.…”

Section: Impact Of Jugementioning

confidence: 99%

“…Consequently, JUGE has been improved and evolved over the years to integrate the latest advances from academia to enhance the comparison and best practices from industry to achieve high automation. Several tools have entered the competition [27–54] and matured over the years by fixing bugs evidenced by the evaluations using the JUGE infrastructure, but also by confronting the various approaches to different benchmarks to discover areas for improvement and future research directions. The current implementation is openly available on GitHub and on Zenodo for long‐term storage [12].…”

Section: Introductionmentioning

confidence: 99%

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JUGE: An infrastructure for benchmarking Java unit test generators

Devroey

Gambi

Galeotti

et al. 2022

Software Testing Verif & Rel

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Summary Researchers and practitioners have designed and implemented various automated test case generators to support effective software testing. Such generators exist for various languages (e.g., Java, C#, or Python) and various platforms (e.g., desktop, web, or mobile applications). The generators exhibit varying effectiveness and efficiency, depending on the testing goals they aim to satisfy (e.g., unit‐testing of libraries versus system‐testing of entire applications) and the underlying techniques they implement. In this context, practitioners need to be able to compare different generators to identify the most suited one for their requirements, while researchers seek to identify future research directions. This can be achieved by systematically executing large‐scale evaluations of different generators. However, executing such empirical evaluations is not trivial and requires substantial effort to select appropriate benchmarks, setup the evaluation infrastructure, and collect and analyse the results. In this Software Note, we present our JUnit Generation Benchmarking Infrastructure (JUGE) supporting generators (search‐based, random‐based, symbolic execution, etc.) seeking to automate the production of unit tests for various purposes (validation, regression testing, fault localization, etc.). The primary goal is to reduce the overall benchmarking effort, ease the comparison of several generators, and enhance the knowledge transfer between academia and industry by standardizing the evaluation and comparison process. Since 2013, several editions of a unit testing tool competition, co‐located with the Search‐Based Software Testing Workshop, have taken place where JUGE was used and evolved. As a result, an increasing amount of tools (over 10) from academia and industry have been evaluated on JUGE, matured over the years, and allowed the identification of future research directions. Based on the experience gained from the competitions, we discuss the expected impact of JUGE in improving the knowledge transfer on tools and approaches for test generation between academia and industry. Indeed, the JUGE infrastructure demonstrated an implementation design that is flexible enough to enable the integration of additional unit test generation tools, which is practical for developers and allows researchers to experiment with new and advanced unit testing tools and approaches.

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Section: Impact Of Jugementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%