Systematic Software Reuse with Automated Extraction and Composition for Clone-and-Own

Linsbauer, Lukas; Fischer, Stefan; Michelon, Gabriela Karoline; Assunção, Wesley K. G.; Grünbacher, Paul; Lopez-Herrejon, Roberto E.; Egyed, Alexander

doi:10.1007/978-3-031-11686-5_15

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Currently, some researchers applied visualization technology in code cloning. Linsbauer et al [16] propose a visual software reuse method that automatically extracts and combines code to achieve cloning and reuse. Kuar et al [17] studied the application of machine learning in code clone detection and visual management.…”

Section: B Software Visualizationmentioning

confidence: 99%

Code Clone Detection via Software Visualization Representation Learning

Qiu,

Wang,

Liang

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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Code clone detection technology aims to automatically detect code similarity and help developers identify and reduce code duplication. While code syntax analysis-based methods are commonly used for clone detection, they may not capture semantic information due to bypassing the analysis of code text. To address this issue, this paper proposes a new method called visualization representation learning for code clone detection (VRL4CCD). This method converts source code fragments into grayscale images to preserve textual information and then utilizes VGG16 and a self-attention mechanism to extract features related to code semantic similarity. A siamese neural network is used to learn the similarity pattern between code features. Experimental results on the Big Clone Bench and Google Code Jam datasets demonstrate that VRL4CCD outperforms current clone detection methods regarding precision, recall, and F1-score, indicating the effectiveness of code visualization technology in clone detection tasks.

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Section: B Software Visualizationmentioning

confidence: 99%

Code Clone Detection via Software Visualization Representation Learning

Qiu,

Wang,

Liang

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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“…RtC-TLs can assist software engineers to know which segments of code implement a particular requirement. Recovering TLs between software Requirements and Source code (RaS) is very useful in numerous Software Engineering (SE) tasks such as software maintenance, reuse, and change [7] [8] [9]. Manually recovering and maintaining these TLs puts a further burden on engineers and is error-prone, tedious, costly mission, and some TLs may be missing.…”

Section: Introductionmentioning

confidence: 99%

Requirements Traceability: Recovering and Visualizing Traceability Links Between Requirements and Source Code of Object-oriented Software Systems

Al-Msie’deen¹

2023

IJCDS

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Requirements traceability is an important activity to reach an effective requirements management method in the requirements engineering. Requirement-to-Code Traceability Links (RtC-TLs) shape the relations between requirement and source code artifacts. RtC-TLs can assist engineers to know which parts of software code implement a specific requirement. In addition, these links can assist engineers to keep a correct mental model of software, and decreasing the risk of code quality degradation when requirements change with time mainly in large sized and complex software. However, manually recovering and preserving of these TLs puts an additional burden on engineers and is error-prone, tedious, and costly task. This paper introduces YamenTrace, an automatic approach and implementation to recover and visualize RtC-TLs in Object-Oriented software based on Latent Semantic Indexing (LSI) and Formal Concept Analysis (FCA). The originality of YamenTrace is that it exploits all code identifier names, comments, and relations in TLs recovery process. YamenTrace uses LSI to find textual similarity across software code and requirements. While FCA employs to cluster similar code and requirements together. Furthermore, YamenTrace gives a visualization of recovered TLs. To validate YamenTrace, it applied on three case studies. The findings of this evaluation prove the importance and performance of YamenTrace proposal as most of RtC-TLs were correctly recovered and visualized.

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