A Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN‐LSTM

ABSTRACT Manual bug triage is a significant bottleneck in modern software development, leading to costly project delays. While automated systems offer a solution, their performance is fundamentally tied to their ability to comprehend the semantic content of bug reports. The text representation methods that power these systems have evolved significantly, yet a systematic comparison to understand their true impact on triage accuracy has been lacking. This paper conducts a large‐scale empirical study to fill this gap, comparing nine text vectorization methods—spanning from classical TF‐IDF to state‐of‐the‐art Sentence‐Transformers (SBERT)—within a hybrid CNN‐LSTM framework. We evaluate the models on six benchmark datasets from projects like Google Chromium and Mozilla. Performance is measured using Top‐k accuracy, a standard metric for this recommendation‐style task, which assesses whether the correct developer is ranked among the top predictions. Our findings reveal that sentence‐level embeddings from SBERT consistently and significantly achieve the highest Top‐k accuracy, outperforming all other techniques, including contextual models like BERT. Our SBERT‐based model establishes a new state‐of‐the‐art, demonstrating that a holistic, sentence‐level semantic understanding is critical for effective bug‐to‐developer assignment.