Hygar

Elyasi, Milad; Simitcioglu, Muhammed Esad; Saydemir, Abdullah; Ekici, Ali; Sözer, Hasan

doi:10.1145/3477314.3507020

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Details of these projects are listed in Table 3. The size of these projects, as measured by lines of code, ranges from about 20K to 11M, which is either equivalent to or exceeds the scale of projects used in previous studies [20,46,75]. The second part comprises 900 popular projects from Github.…”

Section: Data Collection Our Evaluation Utilizes a Two-part Datasetmentioning

confidence: 99%

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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Section: Data Collection Our Evaluation Utilizes a Two-part Datasetmentioning

confidence: 99%

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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A Framework and Taxonomy for Characterizing the Applicability of Software Architecture Recovery Approaches: A Tertiary‐Mapping Study

Qayum,

Zhang,

Colreavy

et al. 2024

Softw Pract Exp

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SummarySoftware architecture assists developers in addressing non‐functional requirements and in maintaining, debugging, and upgrading their software systems. Consequently, consistency between the designed architecture and the implemented software system itself is important; without this consistency the non‐functional requirements targeted may not be addressed and architectural documentation may mis‐direct maintenance efforts that target the associated code‐base. But often, when software is initially implemented or subsequently evolved, the designed architecture and software architecture become inconsistent, with the implemented structure degraded due to issues like developer time‐pressures, or ambiguous communication of the designed architecture. In such cases, Software Architecture Recovery (SAR) or consistency approaches can be applied to reconstruct the architecture of the software system and possibly to compare it to/re‐align it with the designed architecture. Many SAR approaches have been proposed in the research. However, choosing an appropriate architecture recovery approach for software systems is still an open issue. Consequently, this research aims to conduct a tertiary‐mapping study based on available secondary studies of architecture recovery approaches, to uncover important characteristics, towards the selection of appropriate SAR approaches. This research has aggregated 13 secondary studies and 10 primary studies beyond 2020 from 5 databases and, in doing so, identified 111 architecture recovery approaches. Based on these approaches, a taxonomy, containing nine main SAR‐selection categories is proposed and a framework (in the form of a supporting tool to help developers select an appropriate SAR approach) has been developed. Finally, this research identifies six potential open research gaps related to the underlying research that could be helpful for guiding research in the future.

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