Improving predictive models of cognitive complexity using an evolutionary computational approach: a case study

ABSTRACT

The development of software is a human endeavor and program comprehension is an important factor in software maintenance. Predictive models can be used to identify software components as potentially problematic for the purpose of future maintenance. Such modules could lead to increased development effort, and as such, may be in need of mitigating actions such as refactoring or assigning more experienced developers.

Source code metrics can be used as input features to classifiers, however, there exist a large number of structural measures that capture different aspects of coupling, cohesion, inheritance, complexity and size. In machine learning, feature selection is the process of identifying a subset of attributes that improves a classifier's performance. This paper presents initial results when using a genetic algorithm as a method of improving a classifier's ability to discover cognitively complex classes that degrade program understanding.

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