Smartphone apps create and handle a large variety of “instance” data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of the smartphone platform, an app can be paused, sent into background, or killed at any time. If the instance data is not saved and restored between runs, in addition to data loss, partially-saved or corrupted data can crash the app upon resume or restart. While smartphone platforms offer API support for data-saving and data-retrieving operations, the use of this API is ad-hoc: left to the programmer, rather than enforced by the compiler. We have observed that several categories of bugs—including data loss, failure to resume/restart or resuming/restarting in the wrong state—are due to incorrect handling of instance data and are easily triggered by just pressing the ‘Home’; or ‘Back’; buttons. To help address this problem, we have constructed a tool chain for Android (the KREfinder static analysis and the KREreproducer input generator) that helps find and reproduce such incorrect handling. We have evaluated our approach by running the static analysis on 324 apps, of which 49 were further analyzed manually. Results indicate that our approach is (i) effective, as it has discovered 49 bugs, including in popular Android apps, and (ii) efficient, completing on average in 61 seconds per app. More generally, our approach helps determine whether an app saves too much or too little state.
Fri 4 NovDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
13:30 - 15:10 | Bug Detection Analysis and Model CheckingOOPSLA at Matterhorn 1 Chair(s): Ben Livshits Microsoft Research | ||
13:30 25mTalk | Finding Compiler Bugs via Live Code Mutation OOPSLA Chengnian Sun University of California, Davis, Vu Le Microsoft, Zhendong Su University of California, Davis DOI Media Attached | ||
13:55 25mTalk | Finding Resume and Restart Errors in Android Applications OOPSLA Zhiyong Shan University of Central Missouri, USA, Tanzirul Azim University of California at Riverside, USA, Iulian Neamtiu New Jersey Institute of Technology, USA DOI Pre-print | ||
14:20 25mTalk | Low-Overhead and Fully Automated Statistical Debugging with Abstraction Refinement OOPSLA Zhiqiang Zuo University of California, Irvine, Lu Fang University of California, Irvine, Siau-Cheng Khoo , Harry Xu University of California, Irvine, Shan Lu University of Chicago DOI Media Attached | ||
14:45 25mTalk | To Be Precise: Regression Aware Debugging OOPSLA DOI |