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Computational intelligence in software defects rules discovery
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AI
DOI：10.1007/s00500-021-06646-9.png)
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Abstract
En 中文
Nowadays, due to the constant increase in size and complexity of the software systems imposed by their evolution, developing qualitative software systems becomes a highly important task. To achieve this goal, early detection of software defects is a must. The paper proposes an approach to generate rules for software defect prediction. In this respect, a Software Defects Rules Discovery (SDRD) algorithm was put forward. This one uses the ant colony system method to discover the best solution based on code metrics values. We conducted 20 experiments in total (five experiments with three metrics and 15 experiments with combinations of two metrics). The results revealed that the metrics that correlate with the dependent variable are CBO (Coupling Between Objects), RFC (Response For a Class) and NPM (Number of Private Methods), and that from all the combinations of two metrics, for the five projects, the best obtained rule is formed with RFC and NPM metrics.
Keywords:
Software metrics
Ant colony system
Defect rules
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