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Code metrics collected at the method level are often aggregated usingsummation to capture system properties at higher levels (e.g., file- orpackage-level). Since defect data is often available at these higher levels,this aggregation allows researchers to build defect prediction models. Recentfindings by Landman et al. indicate that aggregation is likely to inflate thecorrelation between size and complexity metrics. In this paper, we explore theeffect of nine aggregation techniques on the correlation between three types ofcode metrics, namely Lines of Code, McCabe, and Halstead metrics. In additionto summation, we study aggregation techniques that are measures of: (1) centraltendency (average and median), (2) dispersion (standard deviation andinter-quartile range), (3) shape (skewness and kurtosis), and (4) incomeinequality (Theil index and Gini coefficient). Our results show that defectprediction models built using summation outperform those built using otheraggregation techniques. We also find that more complex aggregations are nodifferent than much simpler ones and that incorporating all aggregation typesin the same model does not provide a significant improvement over usingsummation alone.

Investigating the Impact of Metric Aggregation Techniques on Defect Prediction