Effect of Feature Selection in Software Fault Detection

The quality of software is enormously affected by the faults associated with it. Detection of faults at a proper stage in software development is a challenging task and plays a vital role in the quality of the software. Machine learning is, now a days, a commonly used technique for fault detection and prediction. However, the effectiveness of the fault detection mechanism is impacted by the number of attributes in the publicly available datasets. Feature selection is the process of selecting a subset of all the features that are most influential to the classification and it is a challenging task. This paper thoroughly investigates the effect of various feature selection techniques on software fault classification by using NASA’s some benchmark publicly available datasets. Various metrics are used to analyze the performance of the feature selection techniques. The experiment discovers that the most important and relevant features can be selected by the adopted feature selection techniques without sacrificing the performance of fault detection.

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