Automated classification of actions in bug reports of mobile apps

Liu, Hui; Shen, Mingzhu; Jin, Jiahao; Jiang, Yanjie

doi:10.1145/3395363.3397355

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…Several previous research works have focused on augmenting Android bug reports or facilitating the reporting process. Liu et al proposed a machine learning based classifier, Maca [27], which classifies action words of S2Rs into standard categories (click, input etc.) using both the textual information and the AUT information.…”

Section: Related Workmentioning

confidence: 99%

“…Both phases represent significant challenges that make it difficult to fully automate this process. 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].…”

Section: Introductionmentioning

confidence: 99%

“…However, these techniques are unable to handle natural language in bug reports with either previously unseen words or different sentence structures. Maca [27] designs a classifier to normalize action words into a standard form, but also uses simple patterns to parse the sentence and therefore has limitations when handling previously unseen sentence structures. To address the challenge in the second phase, both approaches employ a greedy strategy to explore the app and identify possible mappings of S2Rs to UI events.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Winn²,

Yu³

et al. 2023

Preprint

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show abstract

“…ANDROR2 could be also used be used to perform bug report prioritization based on the quality of S2Rs. Finally, the dataset could be used by techniques that aim to map S2Rs into GUI actions [23]. Additional Usages.…”

Section: Dataset Characteristicsmentioning

confidence: 99%

AndroR2: A Dataset of Manually-Reproduced Bug Reports for Android apps

Wendland,

Sun,

Mahmud

et al. 2021

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Software maintenance constitutes a large portion of the software development lifecycle. To carry out maintenance tasks, developers often need to understand and reproduce bug reports. As such, there has been increasing research activity coalescing around the notion of automating various activities related to bug reporting. A sizable portion of this research interest has focused on the domain of mobile apps. However, as research around mobile app bug reporting progresses, there is a clear need for a manually vetted and reproducible set of real-world bug reports that can serve as a benchmark for future work. This paper presents ANDROR2: a dataset of 90 manually reproduced bug reports for Android apps listed on Google Play and hosted on GitHub, systematically collected via an in-depth analysis of 459 reports extracted from the GitHub issue tracker. For each reproduced report, ANDROR2 includes the original bug report, an apk file for the buggy version of the app, an executable reproduction script, and metadata regarding the quality of the reproduction steps associated with the original report. We believe that the ANDROR2 dataset can be used to facilitate research in automatically analyzing, understanding, reproducing, localizing, and fixing bugs for mobile applications as well as other software maintenance activities more broadly.

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Better together: Automated app review analysis with deep multi-task learning

Wang,

Zhang

et al. 2025

Information and Software Technology

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Automated classification of actions in bug reports of mobile apps

Cited by 17 publications

References 22 publications

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

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

AndroR2: A Dataset of Manually-Reproduced Bug Reports for Android apps

Better together: Automated app review analysis with deep multi-task learning

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