Search for: All records

The dynamics of mobile networks make it difficult for mobile apps to deliver a seamless user experience. In particular, intermittent connections and weak signals pose challenges for app developers. While recent network libraries have simplified network programming, much expert knowledge is still required. However, most mobile app developers are relative novices and tend to assume a reliable network connection, paying little attention to handling network errors in programming until users complain and leave bad reviews. We argue that the difficulty of avoiding such software defects can be mitigated through an annotation language that allows developers to declaratively state desired and actual properties of the application, largely without reference to fault-tolerant concepts, much less implementation. A pre-compiler can process these annotations, replacing calls to standard networking libraries with customized calls to a specialized library that enhances the reliability. This paper presents ANEL, a declarative language and middleware for Android that enables non-experts. We demonstrate the expressiveness and practicability of ANEL annotation through case studies and usability studies on real-world networked mobile apps. We also show that the ANEL middleware introduces negligible runtime performance overhead. 

Code changes are often reviewed before they are deployed. Popular source control systems aid code review by presenting textual differences between old and new versions of the code, leaving developers with the difficult task of determining whether the differences actually produced the desired behavior. Fortunately, we can mine such information from code repositories. We propose aiding code review with inter-version semantic differential analysis. During review of a new commit, a developer is presented with summaries of both code differences and behavioral differences, which are expressed as diffs of likely invariants extracted by running the system's test cases. As a result, developers can more easily determine that the code changes produced the desired effect. We created an invariant-mining tool chain, GETTY, to support our concept of semantically-assisted code review. To validate our approach, 1) we applied GETTY to the commits of 6 popular open source projects, 2) we assessed the performance and cost of running GETTY in different configurations, and 3) we performed a comparative user study with 18 developers. Our results demonstrate that semantically-assisted code review is feasible, effective, and that real programmers can leverage it to improve the quality of their reviews.