ARCADE: an extensible workbench for architecture recovery, change, and decay evaluation

Laser, Marcelo Schmitt; Medvidović, Nenad; Le, Duc Minh; Garcia, Joshua

doi:10.1145/3368089.3417941

Cited by 26 publications

(12 citation statements)

References 27 publications

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“…Investigating if software system maturity reduces architectural instability and discovered unstable software components based on project design instability and project call instability measures 26 . Unintentional architectural changes, decay, and the presence of vulnerabilities, as well as the identification and tracking of architectural decay across the evolution history of a software system, all require a reliable determination and understanding of architectural change based on architecture-to-architecture and cluster coverage measures [27][28][29][30] .…”

Section: Architectural Change Approachmentioning

confidence: 99%

An Empirical Analysis of Approach-Based Metrics Model for Architectural Erosion Detection

Baabad

Zulzalil

Hassan

et al. 2023

Preprint

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Software architecture determines success or failure in the domain of software development and design. As a system evolves, software architecture erodes. This phenomenon is called architectural erosion. Several studies that focused on various approaches to the problem of architectural erosion have been conducted. As a direct consequence of this, the metrics strategy has emerged as the most widely used solution for architectural erosion. However, providing a comprehensive perception of the elements required to evaluate the phenomenon of architectural erosion with an acceptable level of quality is a challenge. The primary goals of this research, which drew from the prior literature about identifying architectural erosion, were to (1) determine whether various adopted measures approaches determine architectural erosion in order to develop a formal model, and (2) evaluate the construct reliability and construct validity of the model. This research presents a model based on the chosen measures approaches for identifying architectural degradation. This model can be used as the cornerstone for a formal definition of general approaches and adopted metrics. Data was collected from 130 software engineering professionals with experience in architecture erosion and software metrics via a questionnaire-based survey. Structural equation Modelling (SEM) was used to analyse construct reliability, construct validity, and research hypotheses. The results demonstrate a substantial association between all metrics approach classes and architectural erosion, except for architectural complexity and architectural technical debt. Both researchers and practitioners can significantly benefit from this model’s empirical assessment and evaluation, which includes a valuable information in this context.

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Section: Architectural Change Approachmentioning

confidence: 99%

An Empirical Analysis of Approach-Based Metrics Model for Architectural Erosion Detection

Baabad

Zulzalil

Hassan

et al. 2023

Preprint

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show abstract

“…We obtained the executable of ACDC, Bunch, FCA and SADE from the author's websites. For WCA, LIMBO and ARC, we adopted the implementations from ARCADE [69]. Since WCA, LIMBO and ARC allows user to select a preset number of clusters, we ran these tools with 10 to 100 clusters with an incremental step of 5.…”

Section: Baseline Implementation and Parametersmentioning

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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“…In addition, we found that it is a challenge to adequately quantify various AEr symptoms, though several studies (e.g., [S16], [S41], and [S26]) attempted to propose metrics and tools for detecting AEr symptoms. For example, ARCADE mentioned in [S16] is a tool used to analyze and quantify different structural symptoms (e.g., architectural smells and dependency information) for given systems, which is employed to identify and recover the eroded architecture (e.g., references 58,59 ). A potential drawback of entirely relying on structural symptoms might ignore other AEr symptoms (e.g., evolution symptoms).…”

Section: Symptoms Of Architecture Erosionmentioning

confidence: 99%

Understanding software architecture erosion: A systematic mapping study

Liang

Soliman

et al. 2022

J Software Evolu Process

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Architecture erosion (AEr) can adversely affect software development and has received significant attention in the last decade. However, there is an absence of a comprehensive understanding of the state of research about the reasons and consequences of AEr, and the countermeasures to address AEr. This work aims at systematically investigating, identifying, and analyzing the reasons, consequences, and ways of detecting and handling AEr. With 73 studies included, the main results are as follows: (1) AEr manifests not only through architectural violations and structural issues but also causing problems in software quality and during software evolution; (2) non-technical reasons that cause AEr should receive the same attention as technical reasons, and practitioners should raise awareness of the grave consequences of AEr, thereby taking actions to tackle AEr-related issues; (3) a spectrum of approaches, tools, and measures has been proposed and employed to detect and tackle AEr; and (4) three categories of difficulties and five categories of lessons learned on tackling AEr were identified. The results can provide researchers a comprehensive understanding of AEr and help practitioners handle AEr and improve the sustainability of their architecture. More empirical studies are required to investigate the practices of detecting and addressing AEr in industrial settings.

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ARCADE: an extensible workbench for architecture recovery, change, and decay evaluation

Cited by 26 publications

References 27 publications

An Empirical Analysis of Approach-Based Metrics Model for Architectural Erosion Detection

An Empirical Analysis of Approach-Based Metrics Model for Architectural Erosion Detection

Software Architecture Recovery with Information Fusion

Understanding software architecture erosion: A systematic mapping study

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