Software Architecture Recovery with Information Fusion

• URL: http://arxiv.org/abs/2311.04643v1
• Date: Wed, 8 Nov 2023 12:35:37 GMT
• Title: Software Architecture Recovery with Information Fusion
• Authors: Yiran Zhang, Zhengzi Xu, Chengwei Liu, Hongxu Chen, Jianwen Sun, Dong Qiu, Yang Liu
• Abstract summary: We propose SARIF, a fully automated architecture recovery technique. It incorporates three types of comprehensive information, including dependencies, code text and folder structure. SARIF is 36.1% more accurate than the best of the previous techniques on average.
• Score: 14.537490019685384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 implementation-level 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 previous techniques on average. By providing comprehensive architecture, SARIF can help users understand systems effectively and reduce the manual effort of obtaining ground-truth architectures.

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