Analyzing and Detecting Information Types of Developer Live Chat Threads

Online chatrooms serve as vital platforms for information exchange among software developers. With multiple developers engaged in rapid communication and diverse conversation topics, the resulting chat messages often manifest complexity and lack structure. To enhance the efficiency of extracting information from chat threads, automatic mining techniques are introduced for thread classification. However, previous approaches still grapple with unsatisfactory classification accuracy, due to two primary challenges that they struggle to adequately capture long-distance dependencies within chat threads and address the issue of category imbalance in labeled datasets. To surmount these challenges, we present a topic classification approach for chat information types named EAEChat. Specifically, EAEChat comprises three core components: the text feature encoding component captures contextual text features using a multi-head self-attention mechanism-based text feature encoder, and a siamese network is employed to mitigate overfitting caused by limited data; the data augmentation component expands a small number of categories in the training dataset using a technique tailored to developer chat messages, effectively tackling the challenge of imbalanced category distribution; the non-text feature encoding component employs a feature fusion model to integrate deep text features with manually extracted non-text features. Evaluation across three real-world projects demonstrates that EAEChat respectively achieves an average precision, recall, and F1-score of 0.653, 0.651, and 0.644, and it marks a significant 7.60% improvement over the state-of-the-art approachs. These findings confirm the effectiveness of our method in proficiently classifying developer chat messages in online chatrooms.