Generating Reproducible and Replayable Bug Reports from Android Application Crashes

White, Martin; Linares‐Vásquez, Mario; Johnson, Peter W.; Bernal-Cárdenas, Carlos; Poshyvanyk, Denys

doi:10.1109/icpc.2015.14

Cited by 42 publications

(20 citation statements)

References 32 publications

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“…Application crashes that happen while the system is operating are usually reported to developer teams through an issue tracking system for debugging purposes . Depending on the amount of information reported from the operation environment, this debugging process may take more or less time.…”

Section: Background and Related Workmentioning

confidence: 99%

Search‐based crash reproduction using behavioural model seeding

Derakhshanfar

Devroey

Perrouin

et al. 2020

Software Testing Verif & Rel

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Summary Search‐based crash reproduction approaches assist developers during debugging by generating a test case, which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the search process to produce realistic sequences of method calls, which create the required objects. In this study, we introduce behavioural model seeding: a new seeding method that learns class usages from both the system under test and existing test cases. Learned usages are then synthesized in a behavioural model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioural model seeding, we evaluate it against test seeding (the state‐of‐the‐art technique for seeding realistic objects) and no seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard‐to‐reproduce crashes stemming from six open‐source projects. Our results indicate that behavioural model seeding outperforms both test seeding and no seeding by a minimum of 6% without any notable negative impact on efficiency.

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Section: Background and Related Workmentioning

confidence: 99%

Search‐based crash reproduction using behavioural model seeding

Derakhshanfar

Devroey

Perrouin

et al. 2020

Software Testing Verif & Rel

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“…For example: on the efficacy of the pre-launch testing and reports; to compare and contrast the efficacy with other techniques such as textual analysis of reviews; to determine the sweetspot for actionable data while protecting privacy of users; i.e. the conundrum of how much data to collect and provide in the reports; and on ways to reproduce crashes on-demand for easier fault diagnosis using the crash clusters, based on [14] for instance.…”

Section: Discussionmentioning

confidence: 99%

Google Play Console: Insightful Development Using Android Vitals and Pre-Launch Reports

Harty¹

2019

2019 IEEE/ACM 6th International Conference on Mobile Software Engineering and Systems (MOBILESoft)

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Through a case study, I present several related software tools provided by the Google Play Console to Android developers. To help developers monitor and diagnose how their apps are performing in the real-world, Google Play Console includes Android Vitals to track technical performance, Release Management in particular automated pre-launch testing and analysis, and User Feedback for ratings and reviews. They enable developers to identify and address issues that affect active users of their Android apps. The (in-)stability reports include pertinent attributes such as the device model and Android version which can help correlate and triangulate when issues adversely affect customers. App developers can also use the findings to discover flaws in their development and testing practices. Ratings and reviews are already popular topics for researchers, perhaps Release Management and Android Vitals deserve equivalent interest and may offer several new rewarding research areas.

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Section: A Input Generation and Testing Tools For Mobile Appsmentioning

confidence: 99%

“…The motivation for CRASHSCOPE stems in part from our previous work in mobile bug and crash reporting [59], [60], [73]. CRASHDROID [73] is capable of translating a call stack from an app crash into expressive steps to reproduce a bug. However, it has two noteworthy limitations: first, it is not able to uncover crashes automatically, but instead relies on pre-existing stack traces; second, the tool requires expensive collection of manual traces from real users that need to be annotated using natural language descriptions, which are needed to construct the bug reports and replayable scenarios.…”

Section: A Input Generation and Testing Tools For Mobile Appsmentioning

confidence: 99%

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Moran

Linares‐Vásquez

Bernal-Cárdenas

et al. 2016

2016 IEEE International Conference on Software Testing, Verification and Validation (ICST)

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152

123

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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.

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Generating Reproducible and Replayable Bug Reports from Android Application Crashes

Cited by 42 publications

References 32 publications

Search‐based crash reproduction using behavioural model seeding

Search‐based crash reproduction using behavioural model seeding

Google Play Console: Insightful Development Using Android Vitals and Pre-Launch Reports

Automatically Discovering, Reporting and Reproducing Android Application Crashes

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