Deriving metrics thresholds using log transformation

Abstract

Software metrics are surrogates of many software quality factors such as fault proneness, reusability, and maintenance effort. Software metrics are numbers collected from software code to assess and evaluate where problems are more probable to happen. These numbers are used to flag warnings of the problematic parts of software code using threshold values. However, the proposed techniques did not consider the data distribution and skewness in data. In this research, we aim to propose a methodology based on log transformation to improve the metrics quality. To explore the effect of log transformation on data analysis, we conduct analysis of using software metrics after transformation in identifying fault-prone areas on multireleases of 11 products 41 releases. The results show that the log transformation can be used to derive threshold values for all metrics under investigation. The results of the transformation are then used to conduct fault-proneness classification based on threshold values and compared against the results without transformation. The fault classification with transformation was more successful than without transformation. Copyright © 2015 John Wiley & Sons, Ltd.