Predicting software defects is an important issue in the software development and maintenance process, which is related to the overall success of the software. This is because predicting software failures in the previous phase can improve software quality, reliability and efficiency, and reduce software costs. However, developing robust defect prediction models is a challenging task and many techniques have been proposed in the literature. This paper proposes a software defect prediction model based on the new improved hybrid genetic rule mining algorithm (IHGBR). The supervised IHGBR algorithm has been used to predict future software failures based on historical data. The evaluation process shows that the IHGBR algorithm can be used effectively with high accuracy. The collected results show that the IHGBR method has better performance.

Rule mining, Defect, Genetic, software metrics, Prediction

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