PANDORA : continuous mining software repository and dataset generation
Nguyen, Hung; Lomio, Francesco; Pecorelli, Fabiano; Lenarduzzi, Valentina (2022-07-21)
H. Nguyen, F. Lomio, F. Pecorelli and V. Lenarduzzi, "PANDORA: Continuous Mining Software Repository and Dataset Generation," 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Honolulu, HI, USA, 2022, pp. 263-267, doi: 10.1109/SANER53432.2022.00041
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https://urn.fi/URN:NBN:fi-fe2023032333017
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Abstract
During the mining software repository activities, a huge amount of data gathered from different sources is analyzed. Different tools have been developed for collecting and aggregating data from repositories, but they do not easily allow researchers to develop new extractors, to integrate the data collected from other platforms, and in particular from platforms that delete the data periodically. Moreover, mining software repository studies are commonly performed on old versions of software projects and their results are not commonly periodically updated. As a result of the non-continuously updated studies, practitioners often do not trust results from empirical studies. In order to overcome the aforementioned issues, in this paper, we present Pandora, a tool that automatically and continuously mines data from different existing tools and online platforms and enables to run and continuously update the results of mining software repository studies. To evaluate the applicability of our tool, we currently analyzed 365 projects (developed in different languages), continuously collecting data from December 2020 to May 2021 and running an example study, investigating the build-stability of SonarQube rules.
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