A new algorithm for software clustering considering the knowledge of dependency between artifacts in the source code

Sadegi

2020

DTA

PurposeSoftware module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and structural information. The objective of clustering software modules is to achieve minimum coupling among different clusters and create maximum cohesion among the modules of each cluster. Finding the best clustering is considered to be a multi-objective N-P hard optimization-problem, and for solving this problem, different meta-heuristic algorithms have been previously proposed. Achieving higher module lustering quality (MQ), obtaining higher success rate for achieving the best clustering quality and improving convergence speed are the main objectives of this study.Design/methodology/approachIn this study, a method (Bölen) is proposed for clustering software modules which combines the two algorithms of shuffled frog leaping and genetic algorithm.FindingsThe results of conducted experiments using traditional data sets confirm that the proposed method outperforms the previous methods in terms of convergence speed, module clustering quality and stability of the results.Originality/valueThe study proposes SFLA_GA algorithm for optimizing software module clustering, implementing SFLA algorithm in a discrete form by two operators of the genetic algorithm and achieving the above-mentioned purposes in this study. The aim is to achieve higher performance of the proposed algorithm in comparison with other algorithms.

Section: Related Workmentioning

confidence: 99%

Bölen: software module clustering method using the combination of shuffled frog leaping and genetic algorithm

Sadegi

2020

DTA

“…WCA [25] and cooperative clustering [26]) and graph‐based methods (e.g. neighborhood‐based algorithm [27]). In the following, we discuss some popular clustering algorithms in the community.…”

Section: Related Workmentioning

confidence: 99%

New internal metric for software clustering algorithms validity

2020

Self Cite

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.

“…Jalali et al [8] proposed a new multiobjective fitness function for modularization, named MOF, which uses the structural and nonstructural features with EoD algorithm. In [40], a new deterministic clustering algorithm named neighborhood tree algorithm is presented which creates a neighborhood tree using available knowledge in an ADG. Mahouachi [41] proposed a method which used NSGA-II [42] to find the best sequence of refactorings that maximize structural quality, maximize semantic cohesiveness of packages, and minimize the refactoring effort that is able to produce a coherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer's points of view.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to the search-based and hierarchical methods discussed above, there are a number of graph-based and pattern-based methods. Mohammadi and Izadkhah in [40] use a neighboring tree generated from the ADG to cluster a software system. e clustering quality obtained by this algorithm is better than hierarchical methods and less than evolutionary methods.…”

Section: Related Workmentioning

confidence: 99%

A New Metaheuristic-Based Hierarchical Clustering Algorithm for Software Modularization

2020

Self Cite

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.