EH-Recommender: Recommending Exception Handling Strategies Based on Program Context

Exception handling is widely used in software development to guarantee code robustness and system reliability. Developers are expected to choose appropriate handling strategies to ensure exceptions are handled properly without causing program crashes or unintended behaviors. However, making such choices is challenging especially for the novices due to lack of experience on exceptional flow design. To assist developers in deciding how to handle exceptions, we propose a method to automatically recommend exception handling strategies based on program context. This method learns practices of exception handling from existing high-quality projects and code by well-skilled developers. We extracted three type of program context (exceptional context, architectural context, and functional context) as features and applied machine learning techniques to recommend an optimized strategy of exception handling. We conducted the evaluation on 10 open source Java projects. Experimental results show that our approach reaches high prediction accuracy in choosing exception handling strategies.