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Predicting and Monitoring Bug-Proneness at the Feature Level

  • Conference paper
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Dependable Software Engineering. Theories, Tools, and Applications (SETTA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 13071))

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Abstract

Enabling quick feature modification and delivery is important for a project’s success. Obtaining early estimates of software features’ bug-proneness is helpful for effectively allocating resources to the bug-prone features requiring further fixes. Researchers have proposed various studies on bug prediction at different granularity levels, such as class level, package level, method level, etc. However, there exists little work building predictive models at the feature level. In this paper, we investigated how to predict bug-prone features and monitor their evolution. More specifically, we first identified a project’s features and their involved files. Next, we collected a suite of code metrics and selected a relevant set of metrics as attributes to be used for six machine learning algorithms to predict bug-prone features. Through our evaluation, we have presented that using the machine learning algorithms with an appropriate set of code metrics, we can build effective models of bug prediction at the feature level. Furthermore, we build regression models to monitor growth trends of bug-prone features, which shows how these features accumulate bug-proneness over time.

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Notes

  1. 1.

    https://www.atlassian.com/software/jira.

  2. 2.

    https://scitools.com/.

  3. 3.

    In this paper, a file means a source file which contains one or more classes. A feature often contains multiple files.

  4. 4.

    https://www.cs.waikato.ac.nz/~ml/weka/.

  5. 5.

    http://activemq.apache.org/.

  6. 6.

    http://camel.apache.org/.

  7. 7.

    http://cassandra.apache.org/.

  8. 8.

    http://hibernate.org/.

  9. 9.

    https://hive.apache.org/.

  10. 10.

    https://wicket.apache.org/.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under the grant No. 62002129, the Hubei Provincial Natural Science Foundation of China under the grant No. 2020CFB473, and the Fundamental Research Funds for the Central Universities under the grant No. CCNU19TD003.

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Authors and Affiliations

  1. School of Computer, Central China Normal University, Wuhan, China

    Shaozhi Wei, Ran Mo, Pu Xiong, Siyuan Zhang, Yang Zhao & Zengyang Li

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  1. Shaozhi Wei
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  2. Ran Mo
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  3. Pu Xiong
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  4. Siyuan Zhang
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Correspondence to Ran Mo .

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Editors and Affiliations

  1. Teesside University, Middlesbrough, UK

    Shengchao Qin

  2. University of York, York, UK

    Jim Woodcock

  3. Institute of Software, Chinese Academy of Sciences, Beijing, China

    Wenhui Zhang

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Wei, S., Mo, R., Xiong, P., Zhang, S., Zhao, Y., Li, Z. (2021). Predicting and Monitoring Bug-Proneness at the Feature Level. In: Qin, S., Woodcock, J., Zhang, W. (eds) Dependable Software Engineering. Theories, Tools, and Applications. SETTA 2021. Lecture Notes in Computer Science(), vol 13071. Springer, Cham. https://doi.org/10.1007/978-3-030-91265-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-91265-9_11

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