Automatically Discovering, Reporting and Reproducing Android Application Crashes
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2021Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviewscited by 7
- 2025Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Stepscited by 2
- 2024AURORA: Navigating UI Tarpits via Automated Neural Screen Understandingcited by 0
- 2025Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Steps6 Feb 2025
- 2024AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding1 Apr 2024
- 2021Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews12 Apr 2021