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Analysis of institutional authors

Maes-Bermejo, MichelCorresponding AuthorGallego, MicaelAuthorGortazar, FranciscoAuthorRobles, GregorioAuthorBarahona, Jesus Maria GonzalezAuthor
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Hunting bugs: Towards an automated approach to identifying which change caused a bug through regression testing

Publicated to:Empirical Software Engineering. 29 (3): 66- - 2024-05-01 29(3), DOI: 10.1007/s10664-024-10479-z

Authors: Maes-Bermejo, Michel; Serebrenik, Alexander; Gallego, Micael; Gortazar, Francisco; Robles, Gregorio; Barahona, Jesus Maria Gonzalez

Affiliations

Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands - Author
Univ Rey Juan Carlos, Dept Comp Sci, Madrid, Spain - Author
Univ Rey Juan Carlos, Dept Telematic & Computat Syst Engn, Madrid, Spain - Author

Abstract

Context Finding code changes that introduced bugs is important both for practitioners and researchers, but doing it precisely is a manual, effort-intensive process. The perfect test method is a theoretical construct aimed at detecting Bug-Introducing Changes (BIC) through a theoretical perfect test. This perfect test always fails if the bug is present, and passes otherwise.Objective To explore a possible automatic operationalization of the perfect test method.Method To use regression tests as substitutes for the perfect test. For this, we transplant the regression tests to past snapshots of the code, and use them to identify the BIC, on a well-known collection of bugs from the Defects4J dataset.Results From 809 bugs in the dataset, when running our operationalization of the perfect test method, for 95 of them the BIC was identified precisely and in the remaining 4 cases, a list of candidates including the BIC was provided.Conclusions We demonstrate that the operationalization of the perfect test method through regression tests is feasible and can be completely automated in practice when tests can be transplanted and run in past snapshots of the code. Given that implementing regression tests when a bug is fixed is considered a good practice, when developers follow it, they can detect effortlessly bug-introducing changes by using our operationalization of the perfect test method.

Keywords
Bug originsBug-introducing changesFirst-failing changeRegression testinRegression testingSoftware testingSzz algorithm

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Empirical Software Engineering due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 32/132, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Software Engineering.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-04-29:

  • WoS: 1
  • Scopus: 1
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-29:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 5.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 5 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 6.65.
  • The number of mentions on the social network X (formerly Twitter): 6 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Netherlands.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Maes Bermejo, Michel) and Last Author (González Barahona, Jesús María).

the author responsible for correspondence tasks has been Maes Bermejo, Michel.