Andror2: A Dataset of Manually-Reproduced Bug Reports for Android apps

Wendland, Tyler; Sun, Jing; Mahmud, Junayed; Mansur, Syeda; Moran, Kevin; Rubin, Julia; Fazzini, Mattia

doi:10.1109/msr52588.2021.00082

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…In this study, we are interested in both user-submitted and developer-submitted bug reports that are reproducible and describe different types of failures. To the best of our knowledge, ANDROR2 [20] is the largest dataset of reproducible bug reports for Android apps that does not exclusively focus on crashes. This dataset contains 90 user-submitted bug reports, which are associated with apps available on the Google Play store [22] and hosted on GitHub [21].…”

Section: A Dataset Creationmentioning

confidence: 99%

“…Previous studies also produced datasets of Android bugs with associated bug reports. Wendland et al [20] created a dataset of reproducible, user-submitted bug reports. Su et al [58] created a dataset of crashing bugs based on GitHub issues.…”

Section: B Bug Report Studies For Mobile Appsmentioning

confidence: 99%

“…In this paper, we conduct and in-depth characterization of reproducible bug reports for Android apps. To this end, we significantly extend ANDROR2 [20] -a dataset of reproducible bug reports for Android apps which contains bugs representing a range of failure types. We augmented the dataset with additional, manually verified and fully reproduced bug reports from open source Android apps hosted on GitHub [21] and available on the Google Play store [22], obtaining a dataset of 180 bug reports.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

Johnson

Mahmud

Wendland

et al. 2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

Self Cite

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One of the key tasks related to ensuring mobile app quality is the reporting, management, and resolution of bug reports. As such, researchers have committed considerable resources toward automating various tasks of the bug management process for mobile apps, such as reproduction and triaging. However, the success of these automated approaches is largely dictated by the characteristics and properties of the bug reports they operate upon. As such, understanding mobile app bug reports is imperative to drive the continued advancement of report management techniques. While prior studies have examined high-level statistics of large sets of reports, we currently lack an in-depth investigation of how the information typically reported in mobile app issue trackers relates to the specific details generally required to reproduce the underlying failures.In this paper, we perform an in-depth analysis of 180 reproducible bug reports systematically mined from Android apps on GitHub and investigate how the information contained in the reports relates to the task of reproducing the described bugs. In our analysis, we focus on three pieces of information: the environment needed to reproduce the bug report, the steps to reproduce (S2Rs), and the observed behavior. Focusing on this information, we characterize failure types, identify the modality used to report the information, and characterize the quality of the information within the reports. We find that bugs are reported in a multi-modal fashion, the environment is not always provided, and S2Rs often contain missing or non-specific enough information. These findings carry with them important implications on automated bug reproduction techniques as well as automated bug report management approaches more generally.

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Section: A Dataset Creationmentioning

confidence: 99%

Section: B Bug Report Studies For Mobile Appsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

Johnson

Mahmud

Wendland

et al. 2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

Self Cite

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“…In the first phase, the natural language is generally unstructured, written by users without a technical background, and has similar concepts described in a multitude of ways [23,27]. Even if the first phase could be done perfectly, many bug reports have missing steps [25,39]. This complicates the second phase, since the approaches must either find some ways to identify plausible missing steps or dead end in their reproduction efforts when no UI element matches the next S2R.…”

Section: Introductionmentioning

confidence: 99%

Automatically Reproducing Android Bug Reports Using Natural Language Processing and Reinforcement Learning

Zhang¹,

Winn²,

Yu³

et al. 2023

Preprint

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As part of the process of resolving issues submitted by users via bug reports, Android developers attempt to reproduce and observe the failures described by the bug report. Due to the low-quality of bug reports and the complexity of modern apps, the reproduction process is non-trivial and time-consuming. Therefore, automatic approaches that can help reproduce Android bug reports are in great need. However, current approaches to help developers automatically reproduce bug reports are only able to handle limited forms of natural language text and struggle to successfully reproduce failures for which the initial bug report had missing or imprecise steps. In this paper, we introduce a new fully automated Android bug report reproduction approach that addresses these limitations. Our approach accomplishes this by leveraging natural language process techniques to more holistically and accurately analyze the natural language in Android bug reports and designing new techniques, based on reinforcement learning, to guide the search for successful reproducing steps. We conducted an empirical evaluation of our approach on 77 real world bug reports. Our approach achieved 67% precision and 77% recall in accurately extracting reproduction steps from bug reports, and reproduced 74% of the bug reports, significantly outperforming state of the art techniques.

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“…Nielebock et al proposed An-droidCompass [22] as a dataset of Android compatibility checks. Recently, Wendland et al [33] released AndroR2, a dataset of bug reports related to Android apps and Li et al [20] released AndroCT, a large-scale dataset of runtime traces of benign and malicious Android apps. However, in the research directions related to logic bombs, the community faces a challenge to build a comprehensive dataset due to the known difficulties in detecting logic bombs.…”

Section: Introductionmentioning

confidence: 99%

TriggerZoo: A Dataset of Android Applications Automatically Infected with Logic Bombs

Samhi¹,

Bissyandé²,

Klein³

2022

Preprint

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Many Android apps analyzers rely, among other techniques, on dynamic analysis to monitor their runtime behavior and detect potential security threats. However, malicious developers use subtle, though efficient, techniques to bypass dynamic analyzers. Logic bombs are examples of popular techniques where the malicious code is triggered only under specific circumstances, challenging comprehensive dynamic analyses. The research community has proposed various approaches and tools to detect logic bombs. Unfortunately, rigorous assessment and fair comparison of state-ofthe-art techniques are impossible due to the lack of ground truth. In this paper, we present TriggerZoo, a new dataset of 406 Android apps containing logic bombs and benign trigger-based behavior that we release only to the research community using authenticated API. These apps are real-world apps from Google Play that have been automatically infected by our tool AndroBomb. The injected pieces of code implementing the logic bombs cover a large pallet of realistic logic bomb types that we have manually characterized from a set of real logic bombs. Researchers can exploit this dataset as ground truth to assess their approaches and provide comparisons against other tools.

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Andror2: A Dataset of Manually-Reproduced Bug Reports for Android apps

Cited by 17 publications

References 18 publications

An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

Automatically Reproducing Android Bug Reports Using Natural Language Processing and Reinforcement Learning

TriggerZoo: A Dataset of Android Applications Automatically Infected with Logic Bombs

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