E-SC4R: Explaining Software Clustering for Remodularisation

Tan, Alvin Jian Jia; Chong, Chun Yong; Aleti, Aldeida

doi:10.1016/j.jss.2021.111162

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…We have aggregated the matched cluster prototypes from different repository sets by taking the mean of the matched prototypes for each cluster -the result is presented in Figure 1 (where the prototypes are normalized between each others for better visualization) -the metrics on the radar plots are numbered following the next order: issues, then commits metrics -full history (1-7 on the radar plots), past month (8)(9)(10)(11)(12)(13)(14), past two weeks (15)(16)(17)(18)(19)(20)(21), the latest date (22)(23)(24)(25)(26)(27)(28). Compared to the results generated on random data, the discrepancy for c 1 shows relatively consistent results in terms of cosine distance between the cluster prototypes.…”

Section: Aggregation Of the Resultsmentioning

confidence: 99%

“…Tan et al (2022) [13] focused on hierarchical clustering and Bunch clustering algorithms for remodularization and provided information about their suitability according to the features of the software repositories such as bugs, code smells, duplications, number of lines of code, size, and number of stars. The resulting clusters were described in terms of how well the proposed clustering algorithms performed according to the MoJoFM metrics, however no cluster interpretation was provided.…”

Section: B Clustering Software Repositoriesmentioning

confidence: 99%

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Qualitative Clustering of Software Repositories Based on Software Metrics

Bugayenko¹,

Daniakin

Farina

et al. 2023

IEEE Access

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Software repositories contain a wealth of information about the aspects related to software development process. For this reason, many studies analyze software repositories using methods of data analytics with a focus on clustering. Software repository clustering has been applied in studying software ecosystems such as GitHub, defect and technical debt prediction, software remodularization. Although some interesting insights have been reported, the considered studies exhibited some limitations. The limitations are associated with the use of individual clustering methods and manifesting in the shortcomings of the obtained results. In this study, to alleviate the existing limitations we engage multiple cluster validity indices applied to multiple clustering methods and carry out consensus clustering. To our knowledge, this study is the first to apply the consensus clustering approach to analyze software repositories and one of the few to apply the consensus clustering to software metrics. Intensive experimental studies are reported for software repository metrics data consisting of a number of software repositories each described by software metrics. We revealed seven clusters of software repositories and relate them to developers' activity. It is advocated that the proposed clustering environment could be useful for facilitating the decision making process for business investors and open-source community with the help of the Gartner's hype cycle.

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Section: Aggregation Of the Resultsmentioning

confidence: 99%

Section: B Clustering Software Repositoriesmentioning

confidence: 99%

Qualitative Clustering of Software Repositories Based on Software Metrics

Bugayenko¹,

Daniakin

Farina

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…They presented an automated solution for cutting a dendrogram to find the best cut level. Tan et al [4] proposed an approach to evaluate the effectiveness of software clustering algorithms by providing information on their strengths and weaknesses according to the software or code features. Their proposed framework provided a clear understanding of the algorithm's behaviour by showing a 2D representation of the effectiveness of software clustering techniques.…”

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confidence: 99%

Using multi‐pattern clustering methods to improve software maintenance quality

2022

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In software engineering, a software development process, also known as software development life cycle (SDLC), involves several distinct activities for developing, testing, maintaining, and evolving a software system. Within the stages of SDLC, software maintenance occupies most of the total cost of the software life. However, after extended maintenance activities, software quality always degrades due to increasing size and complexity. To solve this problem, software modularisation using clustering is an intuitive way to modularise and classify code into small pieces. , A multi-pattern clustering (MPC) algorithm for software modularisation is proposed in this study. The proposed MPC algorithm can be divided into five different steps: (1) preprocessing, (2) file labelling, (3) collection of chain dependencies, (4) hierarchical agglomerative clustering, (5) modification of the clustering result. The performance of the proposed MPC algorithm to selected clustering techniques is compared by using three open-source and one closedsource software programs. Experimental results show that the modularisation quality of the proposed MPC algorithm is nearly 1.6 times better than that of the expert decomposition. Additionally, compared to other software clustering algorithms, the proposed MPC algorithm, on average, has a 13% enhancement in producing results similar to human thinking. Consequently, it can be seen that the proposed MPC algorithm is suitable for human comprehension while producing better module quality compared to other clustering algorithms.

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Automated construction of reference model for software remodularization through software evolution

Meng,

Yu,

Chong

et al. 2024

J Software Evolu Process

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The undocumented evolution of a software project and its underlying architecture underscores the need to recover the architecture from the software's implementation‐level artifacts. Despite the existence of various software remodularization techniques, they often suffer from inaccuracies, and evaluating their effectiveness is challenging due to the absence of accurate “ground‐truth” architectures or reference models. Prior studies on reference model construction are time‐consuming and labor‐intensive as it heavily relies on manual analysis by domain experts. Besides, other existing approaches that directly utilize the directory or package structure of the latest version can be unreliable, lacking in‐depth analysis of the employed software structure. To address the above limitations, in this paper, we propose Automated Construction of Reference Model (ACRM), an approach for automatically constructing reference models by assigning weights to classes for various software projects using the metadata of all software versions and historical maintenance records. We evaluate ACRM through both quantitative and qualitative analyses. The experiment results provide quantitative validation and show that the generated reference models are reasonable, as confirmed by the relationship between proposed reference models and architectural smells or bugs. Furthermore, we conduct a survey among the practitioners from industry, to gain insights from practitioners' practices and further validate the generated reference models. The survey shows that, on average, 87% of the participants agree with the reference models generated by ACRM. Moreover, we propose an improved metric, wc2c, which analyzes the strengths and weaknesses of different types of software clustering techniques using the proposed reference models of the given software. Finally, we discuss the potential benefits of using ACRM in analyzed projects, particularly in terms of improving software quality, reducing maintenance costs, and enhancing developer productivity.

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E-SC4R: Explaining Software Clustering for Remodularisation

Cited by 9 publications

References 27 publications

Qualitative Clustering of Software Repositories Based on Software Metrics

Qualitative Clustering of Software Repositories Based on Software Metrics

Using multi‐pattern clustering methods to improve software maintenance quality

Automated construction of reference model for software remodularization through software evolution

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