Automatic bug assignment has been a significant area of research in recent years. Bug reports play a crucial role in helping engineers identify and fix software bugs. However, the presence of noise in textual bug reports can hinder automatic bug assignment due to the limitations of conventional Natural Language Processing (NLP) techniques.
A research team led by Zexuan Li recently conducted a study on the impact of textual features on bug assignment approaches. The team focused on leveraging an advanced NLP technique, TextCNN, to evaluate the effectiveness of textual features compared to nominal features in bug assignment.
Contrary to expectations, the study revealed that textual features did not outperform nominal features, even with the utilization of an improved NLP technique. The team identified that nominal features, which indicate developer preferences, played a more influential role in bug assignment approaches.
The research aimed to answer three important questions. Firstly, it sought to determine the effectiveness of textual features when combined with deep-learning-based NLP techniques. Secondly, the study aimed to identify the influential features for bug assignment and explain why they are significant. Lastly, the research investigated the extent to which selected influential features can enhance bug assignments.
By employing the wrapper method and bidirectional strategy, the research team trained classifiers with different feature groups to assess their importance. The results indicated that nominal features could significantly contribute to reducing the search scope of classifiers, ultimately improving bug assignment accuracy.
While the study found that improved NLP techniques had limited impact on bug assignment performance, the identification of key nominal features that enhance bug assignments is a significant development. Future research could focus on incorporating source files to establish a knowledge graph that links influential features and descriptive words for better embedding of nominal features. This could potentially lead to further enhancements in bug assignment accuracy and efficiency.
Overall, the research underscores the importance of considering both textual and nominal features in automatic bug assignment approaches. By understanding the impact of these features, researchers and engineers can develop more effective strategies for identifying and resolving software bugs.
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