Are unit and integration test definitions still valid for modern Java projects? An empirical study on open-source projects

Trautsch, Fabian; Herbold, Steffen; Grabowski, Jens

doi:10.1016/j.jss.2019.110421

Cited by 18 publications

(13 citation statements)

References 16 publications

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“…Our approach targets traceability for unit tests we excluded some obvious integration tests from our evaluation as discussed in Section 7.1. As Trautsch et al (2020) concludes, there is no longer a clear distinction between unit testing and integration testing in modern software testing, and the JUnit framework is often used for both. Interestingly Orellana et al (2017) use naming conventions to distinguish unit and integration tests and Trautsch et al (2020) use coverage information for the same purpose, thus utilising techniques which are similar to those we evaluate.…”

Section: Unit Vs Integration Testingmentioning

confidence: 99%

TCTracer: Establishing test-to-code traceability links using dynamic and static techniques

White

Krinke

2022

Empir Software Eng

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Test-to-code traceability links model the relationships between test artefacts and code artefacts. When utilised during the development process, these links help developers to keep test code in sync with tested code, reducing the rate of test failures and missed faults. Test-to-code traceability links can also help developers to maintain an accurate mental model of the system, reducing the risk of architectural degradation when making changes. However, establishing and maintaining these links manually places an extra burden on developers and is error-prone. This paper presents TCTracer, an approach and implementation for the automatic establishment of test-to-code traceability links. Unlike existing work, TCTracer operates at both the method level and the class level, allowing us to establish links between tests and functions, as well as between test classes and tested classes. We improve over existing techniques by combining an ensemble of new and existing techniques that utilise both dynamic and static information and exploiting a synergistic flow of information between the method and class levels. An evaluation of TCTracer using five large, well-studied open source systems demonstrates that, on average, we can establish test-to-function links with a mean average precision (MAP) of 85% and test-class-to-class links with an MAP of 92%.

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Section: Unit Vs Integration Testingmentioning

confidence: 99%

TCTracer: Establishing test-to-code traceability links using dynamic and static techniques

White

Krinke

2022

Empir Software Eng

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“…Zhang et al used Major and PIT, two well-known mutation testing tools to generate mutants. Researchers have studied both tools extensively [46][47][48][49][50][51][52][53][54]. Major and PIT are also used in industry.…”

Section: Background: Predictive Mutation Testingmentioning

confidence: 99%

An ensemble‐based predictive mutation testing approach that considers impact of unreached mutants

Aghamohammadi

Mirian-Hosseinabadi

2021

Software Testing Verif & Rel

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Predictive mutation testing (PMT) is a technique to predict whether a mutant is killed, using machine learning approaches. Researchers have proposed various methods for PMT over the years. However, the impact of unreached mutants on PMT is not fully addressed. A mutant is unreached if the statement on which the mutant is generated is not executed by any test cases. We aim at showing that unreached mutants can inflate PMT results. Moreover, we propose an alternative approach to PMT, suggesting a different interpretation for PMT. To this end, we replicated the previous PMT research. We empirically evaluated the suggested approach on 654 Java projects provided by prior literature. Our results indicate that the performance of PMT drastically decreases in terms of area under a receiver operating characteristic curve (AUC) from 0.833 to 0.517. Furthermore, PMT performs worse than random guesses on 27% of the projects. The proposed approach improves the PMT results, achieving the average AUC value of 0.613. As a result, we recommend researchers to remove unreached mutants when reporting the results.

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“…We already used data collected with SmartSHARK for multiple publications, e.g., on differences between unit and integration tests [13], the impact on static analysis [11,12], the mining of project activity patterns [5], or the detailed analysis of issues with defect prediction data [4]. As part of the latter, we developed the Python script Mynbou 10 , that can collect release level data for defect prediction.…”

Section: Analyzing Data With Smartsharkmentioning

confidence: 99%

The SmartSHARK ecosystem for software repository mining

Trautsch

Herbold

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings

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Are unit and integration test definitions still valid for modern Java projects? An empirical study on open-source projects

Cited by 18 publications

References 16 publications

TCTracer: Establishing test-to-code traceability links using dynamic and static techniques

TCTracer: Establishing test-to-code traceability links using dynamic and static techniques

An ensemble‐based predictive mutation testing approach that considers impact of unreached mutants

The SmartSHARK ecosystem for software repository mining

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