How Does Pre-trained Language Model Perform on Deep Learning Framework Bug Prediction?

Understanding and predicting bugs is crucial for developers seeking to enhance testing efficiency and mitigate issues in software releases. Bug reports, though semi-structured texts, contain a wealth of semantic information, rendering their comprehension a critical aspect of bug prediction. In light of the recent success of pre-trained language models (PLMs) in the domain of natural language processing, numerous studies have leveraged these models to grasp various forms of textual information. However, the capability of PLMs to understand bug reports remains uncertain. To tackle this challenge, we introduce KnowBug, a framework with a bug report knowledgeenhanced PLM. In this framework, utilizing bug reports obtained from open-source deep learning frameworks as input, prompts are designed and the PLM is fine-tuned for evaluating KnowBug's ability to comprehend bug reports and predict bug types.