Automatic bug assignment has been a topic of interest in recent years, with researchers and engineers focusing on utilizing textual bug reports to identify and fix software bugs. However, the presence of noise in text data poses challenges for effective bug assignments, particularly due to limitations in traditional Natural Language Processing (NLP) techniques.
A research team, led by Zexuan Li, delved into the effects of textual and nominal features on bug assignments. Their study, published in Frontiers of Computer Science, aimed to determine whether advancements in NLP techniques, specifically TextCNN, could improve bug assignment performance based on textual features.
The team’s results highlighted that even with enhanced NLP techniques, textual features did not outperform nominal features in bug assignments. Through their exploration of influential features, the researchers discovered that nominal features, reflecting developers’ preferences, played a crucial role in achieving competitive bug assignment results.
The research study sought to answer three key questions:
1. **Effectiveness of Textual Features:** By comparing the performance of textual features with nominal features using TextCNN, the team evaluated the effectiveness of deep-learning-based NLP techniques in bug assignments.
2. **Identification of Influential Features:** Through the wrapper method and bidirectional strategy, the researchers determined influential features for bug assignments and explained their significance. They emphasized the role of nominal features in reducing classifier search scope.
3. **Impact of Selected Features:** By training models with differing feature groups and employing popular classifiers like Decision Tree and SVM, the study assessed the extent to which selected features improved bug assignments across various datasets.
While the research demonstrated that enhanced NLP techniques had limited impact on bug assignment accuracy, the identification of key nominal features showcased potential for significant improvements. The researchers suggested future work could focus on incorporating source files to establish a knowledge graph linking influential features and descriptive words for enhanced nominal feature embedding.
The study sheds light on the importance of nominal features in bug assignments and underscores the need for continued exploration of innovative approaches to optimize bug resolution processes in software development.
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