An investigation of the effect of module size on defect prediction using static measures

A. Günes Koru, Hongfang D Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Scopus citations

Abstract

We used several machine learning algorithms to predict the defective modules in five NASA products, namely, CM1, JM1, KC1, KC2, and PC1. A set of staticmeasures were employed as predictor variables. While doing so, we observed that a large portion of the modules were small, as measured by lines of code (LOC). When we experimented on the data subsets created by partitioning according to module size, we obtained higher prediction performance for the subsets that include larger modules. We also performed defect prediction using class-level data for KC1 rather than the method-level data. In this case, the use of class-level data resulted in improved prediction performance compared to using methodlevel data. These findings suggest that quality assurance activities can be guided even better if defect prediction is performed by using data that belong to larger modules.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)1595931252, 9781595931252
DOIs
StatePublished - May 15 2005
Externally publishedYes
Event2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005 - St. Louis, United States
Duration: May 15 2005 → …

Other

Other2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005
CountryUnited States
CitySt. Louis
Period5/15/05 → …

Keywords

  • Defect Prediction
  • Prediction Models
  • Software Metrics
  • Software Quality Management
  • Static Measures

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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  • Cite this

    Koru, A. G., & Liu, H. D. (2005). An investigation of the effect of module size on defect prediction using static measures. In Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005 [a7] Association for Computing Machinery, Inc. https://doi.org/10.1145/1083165.1083172