Multi-programming language software systems modularization

Kargar, Masoud; Isazadeh, Ayaz; Izadkhah, Habib

doi:10.1016/j.compeleceng.2019.106500

Cited by 21 publications

(12 citation statements)

References 16 publications

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“…Let GTS and ES indicate the co-cluster pairs in the ground-truth decomposition and co-cluster pairs in the resultant clustering, respectively. Precision and Recall are calculated using (9) and (10):…”

Section: Internal Metricsmentioning

confidence: 99%

“…The available techniques to cluster validity can be typically categorised into (i) external metrics and (ii) internal metrics [6, 7]. External metrics use prelabelled data sets with ‘known’ cluster configurations and measure how well clustering techniques perform with respect to these known clusters [8, 9]. This predetermined clustering manually created by experts of a target system (other names: expert decomposition and authoritative decomposition), which is also called the ground‐truth architecture [10].…”

Section: Introductionmentioning

confidence: 99%

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New internal metric for software clustering algorithms validity

2020

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Clustering (modularisation) techniques are often employed for the meaningful decomposition of a program aiming to understand it. In the software clustering context, several external metrics are presented to evaluate and validate the resultant clustering obtained by an algorithm. These metrics use a ground-truth decomposition to evaluate a resultant clustering. When there exists no ground-truth decomposition for a software system, internal metrics are utilised to validate clustering algorithms. Due to the comparison with a reference decomposition, external metrics are preferred to internal metrics. Available internal metrics used to measure the clustering quality are not appropriate for evaluating because they do not consider the purpose of software clustering, which is to understand a software system. In this study, the authors present six criteria that influence the understanding of a program. Then the authors design an internal metric for estimating the software clustering quality considering those criteria. They selected ten folders of Mozilla Firefox with different sizes and functionalities to assess the reliability of the proposed metric. The experimental results confirm that the proposed internal metric is more accurate than the existing internal metrics in terms of proximity to expert decomposition. The proposed internal metric can be a substitute for external metrics.

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Section: Internal Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New internal metric for software clustering algorithms validity

2020

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“…Existing encoding methods used in software modularization are real-based (e.g., BUNCH [5], ECA [12], and SGA [14]) or permutation-based (e.g., DAGC [15] and E-CDGM [16]), in which these methods show only a flat modularization. (3) A new objective function is proposed to evaluate hierarchical remodularization.…”

Section: Contributionmentioning

confidence: 99%

“…al have some research studies on the remodularization of multiprogramming language software systems. In [14], they have presented two dependency graphs called semantic dependency graph (SDG) and nominal similarity graph (NSG). Both of these graphs are constructed independently of programming languages syntax.…”

Section: Related Workmentioning

confidence: 99%

“…e proximity of the remodularization generated by an algorithm to the decomposition given by a domain expert shows the acceptable achievement of the remodularization algorithm [67]. Like in [14,67], we use the directory structure to prepare an expert decomposition from source code. In this paper, we used Mozilla Firefox and five other software systems, whose authoritative decomposition (i.e., directory structure) is there to assess the proposed algorithm.…”

Section: Case Study Mozilla Firefox a Web Browser Is A Largescale And Open-source Application Developed By The Mozilla Foundation And Itsmentioning

confidence: 99%

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A New Metaheuristic-Based Hierarchical Clustering Algorithm for Software Modularization

2020

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Software refactoring is a software maintenance action to improve the software internal quality without changing its external behavior. During the maintenance process, structural refactoring is performed by remodularizing the source code. Software clustering is a modularization technique to remodularize artifacts of source code aiming to improve readability and reusability. Due to the NP hardness of the clustering problem, evolutionary approaches such as the genetic algorithm have been used to solve this problem. In the structural refactoring literature, there exists no search-based algorithm that employs a hierarchical approach for modularization. Utilizing global and local search strategies, in this paper, a new search-based top-down hierarchical clustering approach, named TDHC, is proposed that can be used to modularize the system. The output of the algorithm is a tree in which each node is an artifact composed of all artifacts in its subtrees and is a candidate to be a software module (i.e., cluster). This tree helps a software maintainer to have better vision on source code structure to decide appropriate composition points of artifacts aiming to create modules (i.e., files, packages, and components). Experimental results on seven folders of Mozilla Firefox with different functionalities and five other software systems show that the TDHC produces modularization closer to the human expert’s decomposition (i.e., directory structure) than the other existing algorithms. The proposed algorithm is expected to help a software maintainer for better remodularization of a source code. The source codes and dataset related to this paper can be accessed at https://github.com/SoftwareMaintenanceLab.

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