Test Case Reuse Based on Software Testing Knowledge Graph and Collaborative Filtering Recommendation Algorithm

Yang, Wansheng; Deng, Fei; Ma, Siyou; Wu, Linbo; Sun, Zhe; Hu, Chi

doi:10.1109/qrs-c55045.2021.00020

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…The above studies all show the values of the KG when it is applied in software engineering tasks, and it can effectively store heterogeneous data and can provide a better reference for the software engineering tasks [34]. These studies inspire us to utilize KG to organize the domain knowledge of human testers.…”

Section: A Kg Application In Software Engineeringmentioning

confidence: 91%

“…Zhou et al [33] employ the knowledge graph technology for intelligent bug fixing, i.e., to study effective search and recommendation techniques based on the knowledge graph for effective bug understanding, bug location, and bug resolution. Yang et al [34] construct a software testing knowledge graph based on the historical data of testing tasks. The approach is used to recommend usable and valuable test cases that are more likely to find defects in software.…”

Section: A Kg Application In Software Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform

Fang

Feng

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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The fragmentation problem has extended from Android to different platforms, such as iOS, mobile web, and even mini-programs within some applications (app), like WeChat 1. In such a situation, recording and replaying test scripts is one of the most popular automated mobile app testing approaches. However, such approach encounters severe problems when crossing platforms. Different versions of the same app need to be developed to support different platforms relying on different platform supports. Therefore, mobile app developers need to develop and maintain test scripts for multiple platforms aimed at completely the same test requirements, greatly increasing testing costs. However, we discover that developers adopt highly similar user interface layouts for versions of the same app on different platforms. Such a phenomenon inspires us to replay test scripts from the perspective of similar UI layouts. In this paper, we propose an image-driven mobile app testing framework, utilizing Widget Feature Matching and Layout Characterization Matching to analyze app UIs. We use computer vision (CV) technologies to perform UI feature comparison and layout hierarchy extraction on mobile app screenshots to obtain UI structures containing rich contextual information of app widgets, including coordinates, relative relationship, etc. Based on acquired UI structures, we can form a platform-independent test script, and then locate the target widgets under test. Thus, the proposed framework non-intrusively replays test scripts according to a novel platform-independent test script model. We also design and implement a tool named LIT to devote the proposed framework into practice, based on which, we conduct an empirical study to evaluate the effectiveness and usability of the proposed testing framework. The results show that the overall replay accuracy reaches around 63.39% on Android (14% improvement over state-of-the-art approaches) and 21.83% on iOS (98% improvement over state-of-the-art approaches).

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Section: A Kg Application In Software Engineeringmentioning

confidence: 91%

Section: A Kg Application In Software Engineeringmentioning

confidence: 99%

LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform

Fang

Feng

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

View full text Add to dashboard Cite

show abstract

“…With these KGs, searches can be performed according to the domain language of software requirements, and corresponding test cases can be automatically generated or recommended. Therefore, KGs can enhance the efficiency of software testing [15] and the reuse rate of software test cases [16,17] to a certain extent.…”

Section: Knowledge Graphs For Software Engineeringmentioning

confidence: 99%

UIGuider: Detecting Implicit Design Guidelines Using a Domain Knowledge Graph Approach

Yang,

2024

Electronics

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Mobile application developers must adhere to a set of design guidelines to ensure consistency in graphical user interfaces (GUIs) and strive for best practices. Violating these widely accepted design guidelines can negatively impact user experience and diminish an application’s market value. Although a few explicit design guidelines outline specific user interface layouts to avoid, substantial design knowledge remains scattered across various web pages. This implicit design knowledge includes design guidelines for UI component usage scenarios, anatomy, fine-grained categorizations, etc. Manually inspecting design guideline violations is knowledge-intensive and time-consuming, demanding high levels of expertise. To address this, we propose UIGuider, a novel approach to automatically constructing a component-based design knowledge graph using multimodal data. UIGuider can discern valuable text containing design guidelines, extract component and concept entities, and establish relationships among them. Our experiments demonstrate the effectiveness and usefulness of UIGuider in automatically detecting implicit design guidelines and constructing domain knowledge graphs. Additionally, the result presentation of a design violation detection tool is optimized. The results of user studies confirm that the use of the knowledge graph and real-world app datasets enhances the overall usability of the tool.

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Research on Automatic Generation and Verification of Test Sequence for New Train Control System

Zang,

Li,

et al. 2023

Lecture Notes in Electrical Engineering

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Test Case Reuse Based on Software Testing Knowledge Graph and Collaborative Filtering Recommendation Algorithm

Cited by 4 publications

References 36 publications

LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform

LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform

UIGuider: Detecting Implicit Design Guidelines Using a Domain Knowledge Graph Approach

Research on Automatic Generation and Verification of Test Sequence for New Train Control System

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