Performance of Genetic Programming-based Software Defect Prediction Models

doi:10.23940/ijpe.21.09.p5.787795

Abstract

Abstract: The performance of software defect prediction (SD) suffers from the problem of dataset imbalance and noisy attributes. Genetic programming (GP) based techniques can boost the performance of SDP models by performing a global search on the complete solution space to locate an optimal solution. With the help of a novel diagram, this paper explains the operations of a typical GP process. Examining the literature, this paper presents a summary of 26 GP based SDP techniques along with the datasets that they have worked on, features that they have examined, performance measures, and their performance metrics. The review finds that most of the GP based SDP techniques have reported performance above the mean performance score of 71%. The paper also finds inadequacy in the literature about the empirical description of GP based SDP techniques. Many GP techniques are not well described in an individual empirical study along with the theoretical foundation of the technique. The paper contributes a novel graphical summary of the GP algorithm and a comprehensive listing of pure GP techniques.

Key words: genetic programming, software defect prediction

Mahesha Pandit, Deepali Gupta. Performance of Genetic Programming-based Software Defect Prediction Models [J]. Int J Performability Eng, 2021, 17(9): 787-795.

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