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Learning actionable analytics from multiple software projects

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Abstract

The current generation of software analytics tools are mostly prediction algorithms (e.g. support vector machines, naive bayes, logistic regression, etc). While prediction is useful, after prediction comes planning about what actions to take in order to improve quality. This research seeks methods that generate demonstrably useful guidance on “what to do” within the context of a specific software project. Specifically, we propose XTREE (for within-project planning) and BELLTREE (for cross-project planning) to generating plans that can improve software quality. Each such plan has the property that, if followed, it reduces the expected number of future defect reports. To find this expected number, planning was first applied to data from release x. Next, we looked for change in release x + 1 that conformed to our plans. This procedure was applied using a range of planners from the literature, as well as XTREE. In 10 open-source JAVA systems, several hundreds of defects were reduced in sections of the code that conformed to XTREE’s plans. Further, when compared to other planners, XTREE’s plans were found to be easier to implement (since they were shorter) and more effective at reducing the expected number of defects.

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Notes

  1. According to the Oxford English Dictionary, the bellwether is the leading sheep of a flock, with a bell on its neck.

  2. https://zenodo.org/communities/seacraft/

  3. And recall in Section 1.2 these versions were given less formal names, specifically older, newer, latest.

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Acknowledgments

The work is partially funded by NSF awards #1506586 and #1302169.

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  1. Computer Science, Columbia University, New York, NY, USA

    Rahul Krishna

  2. Computer Science, NC State University, Raleigh, NC, USA

    Tim Menzies

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Krishna, R., Menzies, T. Learning actionable analytics from multiple software projects. Empir Software Eng 25, 3468–3500 (2020). https://doi.org/10.1007/s10664-020-09843-6

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