Information-Theoretic Remodularization of Object-Oriented Software Systems

Prajapati, Amarjeet; Chhabra, Jitender Kumar

doi:10.1007/s10796-019-09897-y

Cited by 14 publications

(7 citation statements)

References 41 publications

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“…e second evaluation is external, and its objective is to analyse and describe the degree of similarity between the achieved modularization and the expert-produced modularization (the software system's or developer's lone author) for resembling as much as possible as defined by Schmidt et al [33]. For external, we compared the modularization provided by the algorithms with the expert decomposition using MoJo [43,47] and MoJoFM [27,47].…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…Classes are the building blocks of OOPS that encapsulate an entity's Class inter and intramodule change coupling, index module counts, and size 02 [43] FUP-based cohesion and semantic relatedness 03 [44] e cohesion of relational subsystems (maximize), for composite components, there is a standard subsystem dependency (converge to 1.0), coupling error in a subsystem (minimize), subsystem coupling (minimize), distances between subsystems (minimize), restrict the number of outbound-type dependencies (minimize), and the number of cycles on packages (minimize), subsystem compilation units (minimize) 04 [34] Intra and intercluster dependency, cluster count, and module count per cluster 05 [20] Package connectivity index, density of intrapackage connections, and package size index 06 [20] minimize modifications to the package's structure, maintain semblance coherence reuse affects history 07 [45] Package structure, minimum change, MCA, and ECA properties and functions. According to the [46] reconstruction of the architecture, a class is vital in software.…”

Section: Entity and Relationships Extractionmentioning

confidence: 99%

“…(ii) e majority of clusters are singletons (dust clouds). e NED provided by Prajapati and Chhabra [43] and Rahman et al [51] to examine the extreme distribution of module size is given below, and it can be found by following the instructions.…”

Section: Nonextreme Distribution (Ned)mentioning

confidence: 99%

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An Enhanced Multifactor Multiobjective Approach for Software Modularization

Khan

Naseem

Anwar

et al. 2022

Mathematical Problems in Engineering

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Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED).

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Section: Evaluation Resultsmentioning

confidence: 99%

Section: Entity and Relationships Extractionmentioning

confidence: 99%

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An Enhanced Multifactor Multiobjective Approach for Software Modularization

Khan

Naseem

Anwar

et al. 2022

Mathematical Problems in Engineering

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“…Over the years, developing a bug-free software has been the vital domain of subject regions of software maintenance and software quality assurance. It has been perceived that developers are usually forced by the short deadlines of project delivery, budget restrictions, along with unfamiliarity of existent source code to concentrate on functionality instead of a design structure [5]. It is identified that the violation of the rule 'Put together what belongs together' is the major symptom of poorly structured systems [6].…”

Section: Introductionmentioning

confidence: 99%

DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier

Sudhakar¹,

Nithiyanandam²

2022

IET Software

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There exists a need to generate well-designed software systems because of the extensive adoption of object-oriented programming in software growth. Thus, the total software maintenance cost is decreased and the component's reusability is augmented. However, the software system's internal structure worsens owing to extended maintenance activities. For enhancing the system's overall internal structure without varying its external behaviour, restructuring is an extensively utilised solution in this circumstance. Thus, utilising the Deterministic Initialisation Method based K-Means (DIM-K-Means) and Median Absolute Deviation-based Elastic Net Regulariser Neural Network (MAD-ENRNN) classifier, a framework called Distributed Object-Oriented (DOO) software restructuring model is created by the study. Five steps are undertaken by the developed framework. Centred on source code along with change history, the interactions amongst the classes are initially pre-processed where the dependencies of disparate classes are detected and formulated into a graphical structure. After that, from the graph, the extraction of significant features is done. Utilising a multi variant objective-based Aquila optimiser, the most pertinent features are selected as of the extracted features. Next, for minimising the complexity, the selected features are created into clusters. Then, the formed clusters are offered to the classifier named MAD-ENRNN. The DOO software is effectively restructured by MAD-ENRNN. The proposed methodology's performance is contrasted with the prevailing systems in an experimental evaluation. The outcomes displayed that the proposed framework is capable of restructuring the DOO software with improved accuracy of 9.94% when analogised to the top-notch methods.

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“…Therefore, the logic is simple and easy-to-understand and does not require a solid professional computer background and basic knowledge. Objectoriented technology has become one of the main methods of modern programming (Chen et al, 2020;Lopes et al, 2020;Lv et al, 2020;Prajapati and Chhabra, 2020;Van Hien et al, 2020). Recursion is an algorithm widely used in program design.…”

Section: Introductionmentioning

confidence: 99%

An Object-Oriented Approach for the Recursive Numeration and Visualization of the Key Strata of Coal Mines

Zhou

Xiong

et al. 2021

Front. Earth Sci.

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The exploitation and utilization of coal resources have caused serious ecological and environmental problems that are closely related to the movement and destruction of the overlying strata, especially the activities of the overlying key strata (KS). The existing KS calculation methods are characterized by low efficiency and high costs. This study proposes an object-oriented improved recursive algorithm (OORA) model to achieve efficient calculations for KS. An application program was developed and tested with the KS of the Xiadian coal mine, Shanxi, China. The calculation results were basically consistent with field observations, and the calculation depth was increased by 146.05 m. In addition, five typical KS calculation cases were selected for in-depth testing. The calculation time ranged from 0.175–0.225 s, and the calculation time was shortened by approximately three times compared to that with traditional methods. Therefore, it is feasible to apply the model algorithm for KS calculations, and the model provides benefits such as high efficiency and low costs.

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Information-Theoretic Remodularization of Object-Oriented Software Systems

Cited by 14 publications

References 41 publications

An Enhanced Multifactor Multiobjective Approach for Software Modularization

An Enhanced Multifactor Multiobjective Approach for Software Modularization

DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier

An Object-Oriented Approach for the Recursive Numeration and Visualization of the Key Strata of Coal Mines

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