Although the exception handling mechanism is critical for resolving runtime errors, bugs inside this process can have far-reaching impacts. Therefore, researchers have proposed various approaches to assist catching and handling such thrown exceptions and to detect corresponding bugs. If the thrown exceptions themselves are incorrect, their errors will never be correctly caught and handled. Like bugs in catching and handling exceptions, wrong thrown exceptions have caused real critical bugs. However, to the best of our knowledge, no approach has been proposed to recommend which exceptions shall be thrown. Exceptions are widely adopted in programs, often poorly documented, and sometimes ambiguous, making the rules of throwing correct exceptions rather complicated. A project team can leverage exceptions in a way totally different from other teams. As a result, even experienced programmers can have difficulties in determining which exception shall be thrown, although they have the skills to implement its surrounding code. In this paper, we propose the first approach, ThEx, to predict which exception(s) shall be thrown under a given programming context. The basic idea is to learn a classification model from existing thrown exceptions in source files. Here, the learning features are extracted from various code information surrounding the thrown exceptions, such as the thrown locations and related variable names. Then, given a new context, ThEx can predict its best exception(s).

@inproceedings{paperbot7245,
  title = {Which Exception Shall We Throw?},
  author = {Hao Zhong},
  booktitle = {ASE},
  year = {2022},
  doi = {10.1145/3551349.3556895}
}