Recommendation of Exception Handling Code in Mobile App Development

In modern programming languages, exception handling is an effective mechanism to avoid unexpected runtime errors. Thus, failing to catch and handle exceptions could lead to serious issues like system crashing, resource leaking, or negative end-user experiences. However, writing correct exception handling code is often challenging in mobile app development due to the fast-changing nature of API libraries for mobile apps and the insufficiency of their documentation and source code examples. Our prior study shows that in practice mobile app developers cause many exception-related bugs and still use bad exception handling practices (e.g. catch an exception and do nothing). To address such problems, in this paper, we introduce two novel techniques for recommending correct exception handling code. One technique, XRank, recommends code to catch an exception likely occurring in a code snippet. The other, XHand, recommends correction code for such an occurring exception. We have developed ExAssist, a code recommendation tool for exception handling using XRank and XHand. The empirical evaluation shows that our techniques are highly effective. For example, XRank has top-1 accuracy of 70% and top-3 accuracy of 87%. XHand's results are 89% and 96%, respectively.Comment: The 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019) - Late Breaking Result

API Misuse Correction: A Statistical Approach

Modern software development relies heavily on Application Programming Interface (API) libraries. However, there are often certain constraints on using API elements in such libraries. Failing to follow such constraints (API misuse) could lead to serious programming errors. Many approaches have been proposed to detect API misuses, but they still have low accuracy and cannot repair the detected misuses. In this paper, we propose SAM, a novel approach to detect and repair API misuses automatically. SAM uses statistical models to describe five factors involving in any API method call: related method calls, exceptions, pre-conditions, post-conditions, and values of arguments. These statistical models are trained from a large repository of high-quality production code. Then, given a piece of code, SAM verifies each of its method calls with the trained statistical models. If a factor has a sufficiently low probability, the corresponding call is considered as an API misuse. SAM performs an optimal search for editing operations to apply on the code until it has no API issue.Comment: The 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019) - Late Breaking Result