It is recognized in the literature that finding representative data to conduct regression testing research is non-trivial. In our experience within this field, existing datasets are often affected by issues that limit their applicability. Indeed, these datasets often lack fine-grained coverage information, reference software repositories that are not available anymore, or do not allow researchers to readily build and run the software projects, e.g., to obtain additional information. As a step towards better replicability and data-availability in regression testing research, we introduce ReCover, a dataset of 114 pairs of subsequent versions from 28 open source Java projects from GitHub. In particular, ReCover is intended as a consolidation and enrichment of recent dedicated regression testing datasets proposed in the literature, to overcome some of the above described issues, and to make them ready to use with a broader number of regression testing techniques. To this end, we developed a custom mining tool, that we make available as well, to automatically process two recent, massive regression testing datasets, retaining pairs of software versions for which we were able to (1) retrieve the full source code; (2) build the software in a general-purpose Java/Maven environment (which we provide as a Docker container for ease of replication); and (3) compute fine-grained test coverage metrics. ReCover can be readily employed in regression testing studies, as it bundles in a single package full, buildable source code and detailed coverage reports for all the projects. We envision that its use could foster regression testing research, improving replicability and long-term data availability.

ReCover: A Curated Dataset for Regression Testing Research / Altiero, Francesco; Corazza, Anna; DI MARTINO, Sergio; Peron, Adriano; Starace, LUIGI LIBERO LUCIO. - (2022), pp. 196-200. (Intervento presentato al convegno IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) tenutosi a Pittsburgh, PA, USA nel 23-24 May 2022) [10.1145/3524842.3528490].

ReCover: A Curated Dataset for Regression Testing Research

Altiero Francesco
;
Corazza Anna;Di Martino Sergio;Peron Adriano;Starace Luigi Libero Lucio
2022

Abstract

It is recognized in the literature that finding representative data to conduct regression testing research is non-trivial. In our experience within this field, existing datasets are often affected by issues that limit their applicability. Indeed, these datasets often lack fine-grained coverage information, reference software repositories that are not available anymore, or do not allow researchers to readily build and run the software projects, e.g., to obtain additional information. As a step towards better replicability and data-availability in regression testing research, we introduce ReCover, a dataset of 114 pairs of subsequent versions from 28 open source Java projects from GitHub. In particular, ReCover is intended as a consolidation and enrichment of recent dedicated regression testing datasets proposed in the literature, to overcome some of the above described issues, and to make them ready to use with a broader number of regression testing techniques. To this end, we developed a custom mining tool, that we make available as well, to automatically process two recent, massive regression testing datasets, retaining pairs of software versions for which we were able to (1) retrieve the full source code; (2) build the software in a general-purpose Java/Maven environment (which we provide as a Docker container for ease of replication); and (3) compute fine-grained test coverage metrics. ReCover can be readily employed in regression testing studies, as it bundles in a single package full, buildable source code and detailed coverage reports for all the projects. We envision that its use could foster regression testing research, improving replicability and long-term data availability.
2022
ReCover: A Curated Dataset for Regression Testing Research / Altiero, Francesco; Corazza, Anna; DI MARTINO, Sergio; Peron, Adriano; Starace, LUIGI LIBERO LUCIO. - (2022), pp. 196-200. (Intervento presentato al convegno IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) tenutosi a Pittsburgh, PA, USA nel 23-24 May 2022) [10.1145/3524842.3528490].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/894583
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