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On the Effectiveness of Manual and Automatic Unit Test Generation: Ten Years Later

Authors: Serra, Domenico; Grano, Giovanni; Palomba, Fabio; Ferrucci, Filomena; Gall, Harald C.; Bacchelli, Alberto;

On the Effectiveness of Manual and Automatic Unit Test Generation: Ten Years Later

Abstract

Good unit tests play a paramount role when it comes to foster and evaluate software quality. However, writing effective tests is an extremely costly and time consuming practice. To reduce such a burden for developers, researchers devised ingenious techniques to automatically generate test suite for existing code bases. Nevertheless, how automatically generated test cases fare against manually written ones is an open research question. In 2008, Bacchelli et al. conducted an initial case study comparing automatic and manually generated test suites. Since in the last ten years we have witnessed a huge amount of work on novel approaches and tools for automatic test generation, in this paper we revise their study using current tools as well as complementing their research method by evaluating these tools' ability in finding regressions.

Preprint of the publication appeared in the proceedings of the 16th International Conference on Mining Software Repositories (MSR 2019), Montréal, Canada, 2019.

Keywords

1712 Software, 10009 Department of Informatics, Automatic Test Case Generation, Empirical Studies, Software Testing, 1706 Computer Science Applications, 000 Computer science, knowledge & systems, Automatic Test Case Generation; Empirical Studies; Software Testing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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