Enhancing software modularization via semantic outliers filtration and label propagation

Yang, Kaiyuan; Wang, Junfeng; Fang, Zhiyang; Wu, Peng; Song, Zihua

doi:10.1016/j.infsof.2021.106818

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…We reviewed a total of 39 papers related to architecture recovery technique in the recent five years [5, 8, 9, 11, 15, 20, 23, 27, 28, 30, 32-35, 38, 39, 41-44, 47, 50, 53, 55, 56, 59-62, 65, 68, 70, 72-74, 76, 80-82]. 5 of 39 papers [11,56,60,76,80] contain link to their supplementary materials, and 4 of them [56,60,76,80] provide their artifact for reproduction. Among the 4 techniques, EVOL [80] and CodeSum [60] take some intermediate data as input and we failed to generate such data for them since the data formats are not documented.…”

Section: Methodsmentioning

confidence: 99%

“…5 of 39 papers [11,56,60,76,80] contain link to their supplementary materials, and 4 of them [56,60,76,80] provide their artifact for reproduction. Among the 4 techniques, EVOL [80] and CodeSum [60] take some intermediate data as input and we failed to generate such data for them since the data formats are not documented. Thus, the second part of baseline techniques comprises the remaining two techniques: FCA [76] and SADE [56].…”

Section: Methodsmentioning

confidence: 99%

“…the name of artifacts) to improve their previous work in [35]. EVOL [80] is a semantic-based technique that considers semantic outliers filtration and label propagation to increase their accuracy. The textual-information-based techniques may outperform dependency-based ones in specific scenarios as shown in study [21].…”

Section: Related Workmentioning

confidence: 99%

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Software Architecture Recovery with Information Fusion

Zhang,

Xu,

Liu

et al. 2023

Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Enginee

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Understanding the architecture is vital for effectively maintaining and managing large software systems. However, as software systems evolve over time, their architectures inevitably change. To keep up with the change, architects need to track the implementationlevel changes and update the architectural documentation accordingly, which is time-consuming and error-prone. Therefore, many automatic architecture recovery techniques have been proposed to ease this process. Despite efforts have been made to improve the accuracy of architecture recovery, existing solutions still suffer from two limitations. First, most of them only use one or two type of information for the recovery, ignoring the potential usefulness of other sources. Second, they tend to use the information in a coarse-grained manner, overlooking important details within it.To address these limitations, we propose SARIF, a fully automated architecture recovery technique, which incorporates three types of comprehensive information, including dependencies, code text and folder structure. SARIF can recover architecture more accurately by thoroughly analyzing the details of each type of information and adaptively fusing them based on their relevance and quality. To evaluate SARIF, we collected six projects with published ground-truth architectures and three open-source projects labeled by our industrial collaborators. We compared SARIF with nine state-of-the-art techniques using three commonly-used architecture similarity metrics and two new metrics. The experimental results show that SARIF is 36.1% more accurate than the best of the

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