A Fast Clustering Algorithm for Modularization of Large-Scale Software Systems

Teymourian, Navid; Izadkhah, Habib; Isazadeh, Ayaz

doi:10.1109/tse.2020.3022212

Cited by 19 publications

(19 citation statements)

References 33 publications

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“…Teymourian et al [25] presented an approach for the evaluation of dynamic clustering. ey used both static and dynamic features for the modularization process.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Khan

Naseem

Anwar

et al. 2022

Journal of Mathematics

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Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques.

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“…Teymourian et al [25] presented an approach for the evaluation of dynamic clustering. ey used both static and dynamic features for the modularization process.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Khan

Naseem

Anwar

et al. 2022

Journal of Mathematics

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“…In the fast sorting phase, the remaining nodes are sorted and divided into communities according to the obtained elite structure. Inspired by the fast clustering algorithm (FCA) [21], a series of operations are defined on the graph of the community, and we extracted several features from it. The nodes are ordered according to a set of features by utilizing mathematical operations on the graph.…”

Section: Fast Elite Population Initializationmentioning

confidence: 99%

“…In the past years, many methods for the CD have been proposed, such as node importance-based (NIB) methods [5][6][7], good initial substructure-based (GISB) methods [8][9][10], non-negative matrix factorization-based (NMFB) methods [11][12][13][14], deep learningbased (DLB) methods [15][16][17][18], and population intelligence-based (PIB) methods [1,14,19,20]. The methods of NIB, which find the existing dependency and apply mathematical operations between nodes in the graph, or the methods of GISB, which find good initial substructure, can divide the network faster and more efficiently, so they are chosen by more researchers [21,22].…”

Section: Introductionmentioning

confidence: 99%

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Kang

Wang

et al. 2022

Mathematics

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Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.

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“…Hierarchical clustering has high temporal and spatial complexity, low clustering efficiency and large error. The fast hierarchical clustering HAC algorithm reduces the space-time complexity and computation (Teymourian et al. , 2022).…”

Section: Index Classification and Stratificationmentioning

confidence: 99%

Research on optimization of index system design and its inspection method: data quality diagnosis, index classification and stratification

Yin

Cao²,

Zhou³

et al. 2022

MAEM

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PurposeThe purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.Design/methodology/approachUsing modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.FindingsBased on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.Originality/valueThe innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

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A Fast Clustering Algorithm for Modularization of Large-Scale Software Systems

Cited by 19 publications

References 33 publications

A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Research on optimization of index system design and its inspection method: data quality diagnosis, index classification and stratification

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