Bug Severity Assessment Based on Weighted Multi-Facet Features with Particle Swarm Optimization

Severity prediction on software bug reports is an important research issue. Recently, many studies have been conducted. Although previous studies have explored different features to facilitate bug severity assessment, the effectiveness of jointly considering these features is not investigated. In the work, multiple features of three facets are collected are studied. Moreover, this study employs a weight adjustment approach using particle swarm optimization (PSO) to find the most appropriate weights of these features. In the prediction framework, three classification models are used to study the influences of these features. The experimental results show that PSO-optimized multi-facet features with the Random Forests model can achieve the best average prediction performance.