Automatic bug assignment has been a topic of interest in recent years, with researchers and engineers recognizing the importance of textual bug reports in identifying and fixing bugs. While textual bug reports provide valuable information, the presence of noise in the texts can hinder automatic bug assignments. Traditional Natural Language Processing (NLP) techniques may lack the sophistication needed to effectively deal with this noise.
A research team led by Zexuan Li delved into the effects of textual and nominal features on bug assignments. They focused on utilizing an advanced NLP technique, TextCNN, to examine the impact of improved techniques on bug assignment performance. Surprisingly, the results showed that even with advanced NLP techniques, textual features did not outperform nominal features in bug assignments.
The research team identified nominal features as the most influential in bug assignment approaches, as they reflected the preferences of developers. By utilizing the wrapper method and a bidirectional strategy, the team pinpointed the importance of nominal features by training classifiers with different feature groups. The results showcased that nominal features could achieve competitive results without relying on textual information.
The research aimed to answer three fundamental questions regarding bug assignments. Firstly, they explored the effectiveness of textual features when paired with deep-learning-based NLP techniques, comparing them to nominal features. Secondly, they investigated the influential features for bug assignments and examined why nominal features held significance. Lastly, they evaluated the extent to which selected influential features could enhance bug assignment accuracy.
The experimental results emphasized the limited impact of improved NLP techniques on bug assignment performance, with key features achieving modest accuracy improvements under popular classifiers. Moving forward, researchers could focus on integrating source files to establish a knowledge graph that connects influential features and descriptive words, aiming to enhance the embedding of nominal features in bug assignment processes.
The research shed light on the critical role of nominal features in bug assignments, highlighting the need to reconsider the reliance on textual information alone. By exploring innovative strategies and incorporating developer preferences, future bug assignment systems could achieve higher accuracy and efficiency.
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