Abstract
Users of Open Source Software (OSS) projects discuss a diverse range of topics online. The content of a post often corresponds to one or more context-sensitive content types, e.g. a suggestion for a solution, a request for further clarification or indication that a proposed solution did not work. The detection of content types can provide several benefits for software developers. For instance, content types can be used as indicators that summarise the content of the messages. These indicators can be exploited as part of a developer-centric knowledge mining platform allowing developers and project managers to create action alerts concerning new bugs found outside of a bug tracker or they can be combined with other metrics to assess the quality of an OSS project. We present a multi-label classifier, able to classify messages exchanged on communication means about OSS, and detailed evaluation results. We experimented with two state-of-the-art multi-label classification approaches HOMER (Hierarchy Of Multilabel classifiER) and RAkEL (RAndom k-labELsets) as these met the technical requirements of the CROSSMINER project. A manually-annotated threaded corpus of posts form newsgroups discussions, bug tracking systems and forums related to Eclipse projects was also used. The results are promising and indicate the potential to attract novel and deeper research for this task.
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Notes
- 1.
Labels that have not been assigned to any dataset instances, e.g. 8.5, have been omitted.
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This research work is part of the CROSSMINER Project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 732223.
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Campbell, D., Cabrera-Diego, L.A., Korkontzelos, Y. (2020). What is the Message About? Automatic Multi-label Classification of Open Source Repository Messages into Content Types. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_49
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