Programming Assistant for Exception Handling with CodeBERT

With practical code reuse, the code fragments from developers' fo-rums often migrate to applications. Owing to the incomplete nature of such fragments, they often lack the details on exception handling. The adaptation for exception handling to the codebase is not trivial as developers must learn and memorize what API methods could cause exceptions and what exceptions need to be handled. We propose Neurex, an exception handling recommender that learns from complete code, and accepts a given Java code snippet and recommends 1) if a try-catch block is needed, 2) what statements need to be placed in a try block, and 3) what exception types need to be caught in the catch clause. Inspired by the sequence chunking techniques in natural language processing, we design NEurex via a multitasking model with the fine-tuning of the large language model CodeBERT for these three exception handling recommen-dation tasks. Via the large language model, Neurex can learn the surrounding context, leading to better learning the dependencies among the API elements, and the relations between the statements and the corresponding exception types needed to be handled. Our empirical evaluation shows that Neurex correctly performs all three exception handling recommendation tasks in 71.5% of the cases with a F1-score of 70.2%, which is a relative improvement of 166% over the baseline. It achieves high F1-score from 98.2%-99.7% in try-catch block necessity checking (a relative improvement of up to 55.9% over the baselines). It also correctly decides both the need for try-catch block(s) and the statements to be placed in try blocks with the F1-scores of 74.7% and 87.1% at the instance and statement levels, an improvement of 129.1% and 44.9% over the baseline, respectively. Our extrinsic evaluation shows that Neurex relatively improves over the baseline by 56.5% in F1-score for detecting exception-related bugs in incomplete Android code snippets.