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Effects of measurements on correlations of software code metrics
Department of Computer Science and Engineering, Chalmers / University of Gothenburg, Sweden.
Department of Computer Science and Engineering, Chalmers / University of Gothenburg, Sweden.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.ORCID iD: 0000-0003-2895-0780
2019 (English)In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 24, no 4, p. 2764-2818Article in journal (Refereed) Published
Abstract [en]

Context

Software metrics play a significant role in many areas in the life-cycle of software including forecasting defects and foretelling stories regarding maintenance, cost, etc. through predictive analysis. Many studies have found code metrics correlated to each other at such a high level that such correlated code metrics are considered redundant, which implies it is enough to keep track of a single metric from a list of highly correlated metrics.

Objective

Software is developed incrementally over a period. Traditionally, code metrics are measured cumulatively as cumulative sum or running sum. When a code metric is measured based on the values from individual revisions or commits without consolidating values from past revisions, indicating the natural development of software, this study identifies such a type of measure as organic. Density and average are two other ways of measuring metrics. This empirical study focuses on whether measurement types influence correlations of code metrics.

Method

To investigate the objective, this empirical study has collected 24 code metrics classified into four categories, according to the measurement types of the metrics, from 11,874 software revisions (i.e., commits) of 21 open source projects from eight well-known organizations. Kendall’s τ -B is used for computing correlations. To determine whether there is a significant difference between cumulative and organic metrics, Mann-Whitney U test, Wilcoxon signed rank test, and paired-samples sign test are performed.

Results

The cumulative metrics are found to be highly correlated to each other with an average coefficient of 0.79. For corresponding organic metrics, it is 0.49. When individual correlation coefficients between these two measure types are compared, correlations between organic metrics are found to be significantly lower (with p <0.01) than cumulative metrics. Our results indicate that the cumulative nature of metrics makes them highly correlated, implying cumulative measurement is a major source of collinearity between cumulative metrics. Another interesting observation is that correlations between metrics from different categories are weak.

Conclusions

Results of this study reveal that measurement types may have a significant impact on the correlations of code metrics and that transforming metrics into a different type can give us metrics with low collinearity. These findings provide us a simple understanding how feature transformation to a different measurement type can produce new non-collinear input features for predictive models.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 24, no 4, p. 2764-2818
Keywords [en]
Software code metrics, Measurement effects on correlations, Collinearity, Software engineering, Cumulative measurement
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:his:diva-17532DOI: 10.1007/s10664-019-09714-9ISI: 000477582700029Scopus ID: 2-s2.0-85066026436OAI: oai:DiVA.org:his-17532DiVA, id: diva2:1343089
Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-16Bibliographically approved

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