Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

Mining Performance Regression Inducing Code Changes in Evolving Software

During software evolution, the source code of a system frequently changes due to bug fixes or new feature requests. Some of these changes may accidentally degrade performance of a newly released software version. A notable problem of regression testing is how to find problematic changes (out of a large number of committed changes) that may be responsible for performance regressions under certain test inputs. We propose a novel recommendation system, coined as PerfImpact, for automatically identifying code changes that may potentially be responsible for performance regressions using a combination of search-based input profiling and change impact analysis techniques. PerfImpact independently sends the same input values to two releases of the application under test, and uses a genetic algorithm to mine execution traces and explore a large space of input value combinations to find specific inputs that take longer time to execute in a new release. Since these input values are likely to expose performance regressions, PerfImpact automatically mines the corresponding execution traces to evaluate the impact of each code change on the performance and ranks the changes based on their estimated contribution to performance regressions. We implemented PerfImpact and evaluated it on different releases of two open-source web applications. The results demonstrate that PerfImpact effectively detects input value combinations to expose performance regressions and mines the code changes are likely to be responsible for these performance regressions