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

A. Günes Koru; Hongfang Liu

doi:10.1145/1083165.1083172

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

A. Günes Koru, Hongfang Liu

Digital Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

49 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 language	English (US)
Title of host publication	Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	1595931252, 9781595931252
DOIs	https://doi.org/10.1145/1083165.1083172
State	Published - May 15 2005
Event	2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005 - St. Louis, United States Duration: May 15 2005 → …

Publication series

Name	Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005

Other

Other	2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005
Country/Territory	United States
City	St. Louis
Period	5/15/05 → …

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

Access to Document

10.1145/1083165.1083172

Cite this

Koru, A. G., & Liu, H. (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 Article a7 (Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005). Association for Computing Machinery, Inc. https://doi.org/10.1145/1083165.1083172

An investigation of the effect of module size on defect prediction using static measures. / Koru, A. Günes; Liu, Hongfang.
Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005. Association for Computing Machinery, Inc, 2005. a7 (Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Koru, AG & Liu, H 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, Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005, Association for Computing Machinery, Inc, 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005, St. Louis, United States, 5/15/05. https://doi.org/10.1145/1083165.1083172

Koru AG, Liu H. 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. Association for Computing Machinery, Inc. 2005. a7. (Proceedings of the 2005 Workshop on Predictor Models in Software Engineering, PROMISE 2005). doi: 10.1145/1083165.1083172

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

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