Enhancing predictive models using principal component analysis and search based metric selection: a comparative study

ABSTRACT

Predictive models are used for the detection of potentially problematic component that decrease product quality. Source code metrics can be used as input features in predictive models; however, there exist numerous structural measures that capture different aspects of size, coupling, cohesion, inheritance and complexity. An important question to answer is which metrics should be used with a predictor. A comparative analysis of metric selection strategies (principal component analysis, a genetic algorithm and the CK metrics set) has been carried out. Initial results indicate that search-based metric selection gives the best predictive performance in identifying Java classes with high cognitive complexity that degrades product maintenance.

REFERENCES

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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.4 Software/Program Verification
          Subjects: Statistical methods

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.4 Software/Program Verification
          Subjects: Validation
      D.2.8 Metrics
          Subjects: Product metrics; Performance measures

General Terms:
Design, Measurement

Keywords:
genetic algorithm, metric selection, pca, predictive models