Case study on which relations to use for clustering-based software architecture recovery

Stavropoulou, Ioanna; Grigoriou, Marios; Kontogiannis, Kostas

doi:10.1007/s10664-016-9459-z

Cited by 16 publications

(6 citation statements)

References 44 publications

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“…Entity-level dependency has many different types, and different types of dependencies have different level of indication on the architecture. For example, according to the previous study [71], Function Call dependencies can reflect much more architectural information than Uses Type (i.e., a function accesses a specific type) dependencies. However, this limited understanding of their relative usefulness in architecture is not enough for us to manually assign appropriate weights to all 13 types of dependencies extracted by Depends.…”

Section: Information Refinementmentioning

confidence: 98%

“…For example, interfaces of modules have more influence on the architecture than the modules' internal implementations [7], thus they should have higher weights. The second weighting is for dependencies (i.e., the edges in the dependency graph), since different types of dependencies carry different amounts of architectural information [71]. Weigh Entities by Importance.…”

Section: Information Refinementmentioning

confidence: 99%

“…However, different types of dependencies have different influences on the architecture. According to [71], Function Call dependencies can reflect much more architectural information than Use Type dependencies. Moreover, dependencies introduced by different entities also have different impact on the architecture.…”

Section: Introductionmentioning

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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Section: Information Refinementmentioning

confidence: 98%

Section: Information Refinementmentioning

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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“…This clustering technique applies different algorithms (k-means, hierarchical clustering among others) considering classes, methods, packages, and relationships among them in order to determine their modularization into components, packages or files [61], [62], [63], [64], [65], [66] by taking into account static information, design rules [67], patterns [68], runtime executions [69], evolution [70] or software architectures together with their architects' knowledge [71], [72]. The literature on the subject indicates that clustering-based architecture extraction is a well investigated area, although the accuracy of the results depend on: (i) the selected relationships during the architecture [73] and (ii) the characteristics and number of the projects and applications analyzed, which are usually less than 15 projects [70] or datasets are manually generated instead of using real applications [9].…”

Section: Related Workmentioning

confidence: 99%

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

2021

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The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.

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“…Vendome et al [214] License usage and changes: a large-scale study on gitHub [196] Case study on which relations to use for clustering-based software architecture recovery…”

Section: Laboratory Experimentsmentioning

confidence: 99%

The ABC of Software Engineering Research

Stol

Fitzgerald

2018

ACM Trans. Softw. Eng. Methodol.

155

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A variety of research methods and techniques are available to SE researchers, and while several overviews exist, there is consistency neither in the research methods covered nor in the terminology used. Furthermore, research is sometimes critically reviewed for characteristics inherent to the methods. We adopt a taxonomy from the social sciences, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies. ABC refers to the research goal that strives for generalizability over Actors (A) and precise measurement of their Behavior (B), in a realistic Context (C). The ABC framework uses two dimensions widely considered to be key in research design: the level of obtrusiveness of the research and the generalizability of research findings. We discuss metaphors for each strategy and their inherent limitations and potential strengths. We illustrate these research strategies in two key SE domains, global software engineering and requirements engineering, and apply the framework on a sample of 75 articles. Finally, we discuss six ways in which the framework can advance SE research.

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Case study on which relations to use for clustering-based software architecture recovery

Cited by 16 publications

References 44 publications

Software Architecture Recovery with Information Fusion

Software Architecture Recovery with Information Fusion

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

The ABC of Software Engineering Research

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