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Predicting Fault-Prone Modules by Word Occurrence in Identifiers

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Software Engineering Research, Management and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 578))

Abstract

Prediction of fault-prone modules is an important area of software engineering. We assumed that the occurrence of faults is related to the semantics in the source code modules. Semantics in a software module can be extracted from identifiers in the module. We then analyze the relationship between occurrence of “words” in identifiers and the existence of faults. To do so, we first decompose the identifiers into words, and investigate the occurrence of words in a module. Modeling by the random forest technique, we made a model of occurrence of words and existence of faults. We compared the word occurrence model with traditional models using CK metrics and LOC. The result of comparison showed that the occurrence of words is a good prediction measure as well as CK metrics and LOC.

Currently, The author is in Nara Advanced Institute of Science and Technology.

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Notes

  1. 1.

    https://github.com/.

  2. 2.

    http://promisedata.googlecode.com/.

  3. 3.

    https://github.com/doofuslarge/lscp. lcsp is a lightweight source code preprocesser. lscp can be used to isolate and manipulate the linguistic data (i.e., identifier names, comments, and string literals) from source code files.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 24500038.

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Authors and Affiliations

  1. Software Engineering Laboratory, Graduate School of Science and Technology, Kyoto Institute of Technology, Kyoto, Japan

    Naoki Kawashima & Osamu Mizuno

Authors
  1. Naoki Kawashima
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  2. Osamu Mizuno
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Corresponding author

Correspondence to Naoki Kawashima .

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Editors and Affiliations

  1. Software Engineering and Information Technology Institute, Central Michigan University, Mount Pleasant, Michigan, USA

    Roger Lee

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Kawashima, N., Mizuno, O. (2015). Predicting Fault-Prone Modules by Word Occurrence in Identifiers. In: Lee, R. (eds) Software Engineering Research, Management and Applications. Studies in Computational Intelligence, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-11265-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-11265-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11264-0

  • Online ISBN: 978-3-319-11265-7

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