Automatic bug assignment is a crucial task in software development, as engineers heavily rely on bug reports to fix issues. However, the presence of noises in textual bug reports can complicate the process of bug assignment. Traditional Natural Language Processing (NLP) techniques may not always be sufficient to handle these noises effectively.
A recent study led by Zexuan Li delved into the effects of textual and nominal features on bug assignments. The research team focused on utilizing an advanced NLP technique, TextCNN, to analyze the impact of textual features on bug assignment performance. Surprisingly, the results showed that textual features did not outperform nominal features, despite the use of an improved NLP technique.
The study highlighted the importance of nominal features in bug assignment approaches. By employing a statistical perspective, the researchers identified that the influential features for bug assignment were primarily nominal features indicating developers’ preferences. The experimental results indicated that nominal features could achieve competitive results without the reliance on textual information.
The research study aimed to address several key questions. Firstly, it explored the effectiveness of textual features when combined with deep-learning-based NLP techniques. The comparison between textual and nominal features revealed interesting insights into bug assignment performance. Secondly, the study examined the influential features for bug assignment and provided a statistical explanation for their impact. It was noted that nominal features could assist in narrowing down the search scope of classifiers, leading to improved bug assignment outcomes. Lastly, the researchers investigated the extent to which selected influential features could enhance bug assignments. The experimental results indicated a modest improvement in bug assignment accuracy under popular classifiers.
Despite the limited improvement observed with the use of an advanced NLP technique, the study paves the way for future research directions. One potential avenue for further investigation involves integrating source files to establish a knowledge graph connecting influential features and descriptive words. This could lead to better embedding of nominal features in bug assignment processes, enhancing overall performance.
The study sheds light on the significance of both textual and nominal features in bug assignments. While improved NLP techniques may not always result in substantial performance gains, the careful selection and utilization of influential nominal features can significantly impact bug assignment outcomes.
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