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

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Published:22 October 2007Publication History

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

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    • Published in

      cover image DL Hosted proceedings
      CASCON '07: Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
      October 2007
      387 pages

      Publisher

      IBM Corp.

      United States

      Publication History

      • Published: 22 October 2007

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      • Article

      Acceptance Rates

      Overall Acceptance Rate24of90submissions,27%

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