Enhancing UML Class Diagram Abstraction with Knowledge Graph

Huang, Liang; Duan, Yucong; Sun, Xiaobing; Lin, Zhaoxin; Zhu, Chuanpu

doi:10.1007/978-3-319-46257-8_65

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…[28] establish a high-quality medical Q&A knowledge graph based on relevant professional knowledge in medical Q&A research. Huang et al [29] introduce a method to abstract class diagrams and to use the knowledge graph as an intermediate layer to analyze and deal with class diagrams. Zhang et al [30] propose a function-oriented approach for eliciting nonfunctional requirements based on a domain knowledge graph.…”

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)

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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: 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)

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“…He noted that graphs could effectively measure structural similarities between class diagrams. Huang [22] also converted a class diagram into a graph. Therefore, a graph can be used to represent a class diagram.…”

Section: Introductionmentioning

confidence: 99%

Automated Class Diagram Assessment using Semantic and Structural Similarities

Fauzan¹,

Siahaan²,

Rochimah³

et al. 2021

IJIES

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Class diagrams show classes in software and the relationships between those classes. A class diagram is a unified modeling language diagram commonly used in education. Thus, an assessment of class diagrams is essential for teachers who usually have students produce class diagrams based on predetermined projects. Teachers assess student-produced class diagrams based on an answer key. However, teachers have a problem with a lack of consistency in assessment as teachers can use different standards between answers. This research attempts to approach class diagram assessment automatically. The proposed approach consists of two assessments: semantic and structural similarities. Semantic similarity is calculated using lexical information in the class diagram, and structural similarity is calculated using the diagram's structure, ignoring its lexical information. Our results show that experts see semantic and structural similarities equally during assessment. The proposed approach shows substantial agreement with experts in class diagram similarity assessment. Therefore, the proposed approach can automatically assess class diagram similarity as reliably as experts can.

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