Event trace reduction for effective bug replay of Android apps via differential GUI state analysis

Sui, Yulei; Zhang, Yifei; Zheng, Wei; Zhang, Manqing; Xue, Jingling

doi:10.1145/3338906.3341183

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…It used event data captured during the recording process to generate a heatmap that facilitates developers understanding of how users are interacting with an application. Additional tools including ECHO [69], Reran [39], Barista [42], and Android Bot Maker [2] are program-analysis related applications for the developer to record and replay interactions. However, they required the installation of underlying frameworks such as replaykit [4], troyd [40], or instrumenting apps which is too heavy for end users.…”

Section: Bug Record and Replay From App Running Informationmentioning

confidence: 99%

GIFdroid: Automated Replay of Visual Bug Reports for Android Apps

Feng¹,

Chen²

2021

Preprint

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Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using software. However, it is difficult for non-technical users to write clear descriptions about the bug occurrence. Therefore, more and more users begin to record the screen for reporting bugs as it is easy to be created and contains detailed procedures triggering the bug. But it is still tedious and time-consuming for developers to reproduce the bug due to the length and unclear actions within the recording. To overcome these issues, we propose GIFdroid, a lightweight approach to automatically replay the execution trace from visual bug reports. GIFdroid adopts image processing techniques to extract the keyframes from the recording, map them to states in GUI Transitions Graph, and generate the execution trace of those states to trigger the bug. Our automated experiments and user study demonstrate its accuracy, efficiency, and usefulness of the approach. CCS CONCEPTS• Software and its engineering → Software testing and debugging.

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Section: Bug Record and Replay From App Running Informationmentioning

confidence: 99%

GIFdroid: Automated Replay of Visual Bug Reports for Android Apps

Feng¹,

Chen²

2021

Preprint

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“…As GUI components have similar shapes and can be used across devices or platforms, they are intuitive for users to operate without the need to learn. Although software applications provide easy-to-use GUIs, users experience bugs in many different forms [4,5]; for example, by operating the program in an illogical manner owing to a lack of background knowledge of the program or by causing bugs while using GUI-driven software. Bug resolution is one of the most important tasks in maintaining software, and developers are interested in analyzing these bugs to improve software quality [6].…”

Section: Introductionmentioning

confidence: 99%

GUI Component Detection-Based Automated Software Crash Diagnosis

Nam

Seo

2023

Electronics

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This study presents an automated software crash-diagnosis technique using a state transition graph (STG) based on GUI-component detection. An STG is a graph representation of the state changes in an application that are caused by actions that are executed in the GUI, which avoids redundant test cases and generates bug-reproduction scenarios. The proposed technique configures the software application STG using computer vision and artificial intelligence technologies and performs automated GUI testing without human intervention. Four experiments were conducted to evaluate the performance of the proposed technique: a detection-performance analysis of the GUI-component detection model, code-coverage measurement, crash-detection-performance analysis, and crash-detection-performance analysis in a self-configured multi-crash environment. The GUI-component detection model obtained a macro F1-score of 0.843, even with a small training dataset for the deep-learning model in the detection-performance analysis. Furthermore, the proposed technique achieved better performance results than the baseline Monkey in terms of code coverage, crash detection, and multi-crash detection.

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A systematic mapping study for graphical user interface testing on mobile apps

Nie

Said²,

et al. 2023

IET Software

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Mobile apps with tested Graphical User Interface (GUI) tend to have higher downloads in the apps store. In recent years, few efforts were made to analyse the research community and research status of the literature for GUI testing on mobile apps, which brings an obstacle to characterise and understand this field. In this study, the authors propose a systematic mapping study to gain insights into the field. First, the authors conduct an extensive search of relevant literature over seven popular digital libraries. From 4427 candidate studies, 114 primary studies published between January 2011 and September 2022 were selected. Next, the authors analyse these primary studies from the perspectives of bibliometric and qualitative analysis. For the bibliometric analysis, first, the authors analyse the popular research topics and their relationships. Second, the authors study the authors' community. For the qualitative analysis, the authors analyse the objectives, approaches and evaluation metrics employed in these primary studies. Their investigation reports several major findings: (1) there are relatively more studies on two topics, that is, test case generation and the automated test; (2) the most productive authors tend to collaborate and often have relatively broad research interests; (3) the functionality is the main objective of GUI testing; the model-based approach is the most widely used.

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Event trace reduction for effective bug replay of Android apps via differential GUI state analysis

Cited by 7 publications

References 26 publications

GIFdroid: Automated Replay of Visual Bug Reports for Android Apps

GIFdroid: Automated Replay of Visual Bug Reports for Android Apps

GUI Component Detection-Based Automated Software Crash Diagnosis

A systematic mapping study for graphical user interface testing on mobile apps

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