Slice-Based Cognitive Complexity Metrics for Defect Prediction

Researchers have identified several quality metrics to predict defects, relying on different information however, these approaches lack metrics to estimate the effort of program understandability of system artifacts. In this paper, novel metrics to compute the cognitive complexity based on program slicing are introduced. These metrics help identify code that is more likely to have defects due to being challenging to comprehension. The metrics include such measures as the total number of slices in a file, the size, the average number of identifiers, and the average spatial distance of a slice. A scalable lightweight slicing tool is used to compute the necessary slicing data. A thorough empirical investigation into how cognitive complexity correlates with and predicts defects in the version histories of 10 datasets of 7 open source systems is performed. The results show that the increase of cognitive complexity significantly increases the number of defects in 94% of the cases. In a comparison study to metrics that have been shown to correlate with understandability and with defects, the addition of cognitive complexity metrics shows better prediction by up to 14% in F1, 16% in AUC, and 35% in R
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