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Proceedings Year : 2023

Fingerprinting and Building Large Reproducible Datasets

Abstract

Obtaining a relevant dataset is central to conducting empirical studies in software engineering. However, in the context of mining software repositories, the lack of appropriate tooling for large scale mining tasks hinders the creation of new datasets. Moreover, limitations related to data sources that change over time (e.g., code bases) and the lack of documentation of extraction processes make it difficult to reproduce datasets over time. This threatens the quality and reproducibility of empirical studies. In this paper, we propose a tool-supported approach facilitating the creation of large tailored datasets while ensuring their reproducibility. We leveraged all the sources feeding the Software Heritage append-only archive which are accessible through a unified programming interface to outline a reproducible and generic extraction process. We propose a way to define a unique fingerprint to characterize a dataset which, when provided to the extraction process, ensures that the same dataset will be extracted. We demonstrate the feasibility of our approach by implementing a prototype. We show how it can help reduce the limitations researchers face when creating or reproducing datasets.
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Dates and versions

hal-04132604 , version 1 (19-06-2023)

Identifiers

Cite

Romain Lefeuvre, Jessie Galasso, Benoit Combemale, Houari Sahraoui, Stefano Zacchiroli. Fingerprinting and Building Large Reproducible Datasets. ACM REP '23: Proceedings of the 2023 ACM Conference on Reproducibility and Replicability, 2023, ⟨10.5281/zenodo.7989955⟩. ⟨hal-04132604⟩
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