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GUI-based software modularization through module clustering in edge computing based IoT environments

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第3期13卷 1625-1639页

作者： Seo, Yeong-Seok Huh, Jun-Ho Yeungnam Univ Dept Comp Engn 280 Daehak Ro Gyongsan 38541 Gyeongbuk South Korea Korea Maritime & Ocean Univ Dept Data Informat 727 Taejong Ro Busan South Korea

With edge computing-based Internet-of-Things environments widely recognized, major software technologies are rapidly integrated. Among the various areas of such technologies, software evolution and maintenance are essential yet challenging tasks both for research and in practice. In particular, for large-scale software, the maintenance costs are often much more than the development cost. In order to help to resolve this issue, software module clustering approaches have been studied to build adequate modules with high cohesion and low coupling;such modular structures can help the comprehension and maintenance of complex systems. The existing studies apply either structural, semantic, or historic-based factors as clustering criteria;however, to the best of our knowledge, no previous study considers Graphical User Interface (GUI) as a factor for software module clustering. Because GUI is linked to the functionalities of a software system, it can be a useful source to find cohesive modules and generate well-defined modular structures. Thus, in this study, we propose a GUI-based approach for software module clustering to improve the quality of software modularization. Quantitative and qualitative experiments are performed to evaluate the performance of the proposed approach and comparing it with three existing approaches;using two open source software systems. The evaluation results show that our proposed approach generates feasible software modules and provides closer estimates to actual modularization than those of the existing approaches.

关键词： Software maintenance module clustering Remodularization Graphical User Interface Software profiling Dynamic analysis Software Engineering

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The Modular Affordance Deployment Method for module clustering Process of the Integrated Service Generalized Product 21

The Modular Affordance Deployment Method for Module Clusteri...

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21st ISPE Inc International Conference on Concurrent Engineering

作者： Wu, Chunlong Ji, Yangjian Lu, Wenbo Qi, Guoning Gu, Xinjian Zhejiang Univ Dept Mech Engn Ind Engn Ctr Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781614994404;9781614994398

In the new century of global competition, the wise companies try to supply the modular generalized product which integrates tangible physical product and intangible service. To deal with the module clustering process of the integrated service generalized product, we adopt a broader concept "Affordance" as the base of generalized product design and propose a practical matrix based method-Modular Affordance Deployment (MAD). And we use vacuum cleaner as an illustrated example to show the complete MAD process that cluster modules for the integrated service generalized product.

关键词： Modular design MFD Affordance Generalized product PSS module clustering

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RUN: a robust cluster-based planning for fast self-reconfigurable modular robotic systems

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INTELLIGENT SERVICE ROBOTICS 2023年第1期16卷 75-85页

作者： Majed, Aliah Harb, Hassan Nasser, Abbass Clement, Benoit Amer Univ Culture & Educ AUCE ICCS Lab Beirut Lebanon ENSTA Bretagne Univ Lab STICC UMR CNRS 6585 Brest France Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait ENSTA Bretagne Lab STICC UMR Brest France

Recently, the industrial revolution has led to the emergence of a new generation of robotics called as modular robotic system (MRS). However, such new technology faces technical and practical challenges, in particular the self-reconfiguration. Subsequently, converting from one morphology to another is a complicated task for MRS that may take a long time because of the huge number of communications needed among the modules. In this paper, we propose an efficient and robust cluster-based planning, called RUN, for fast self-reconfigurable modular robots. RUN works on two stages and aims to group the modules into clusters in order to reduce the communication overhead between them and offers a fast reconfiguration process for the MRS. The first stage selects a set of modules, called cliques, and then, it divides the modules into clusters based on the shortest paths between modules and cliques. The second stage introduces two communication algorithms: the inter-module algorithm that allows an efficient communication between the cliques of the clusters, and the intra-module algorithm that reduces the communication number between the modules of the same clusters. We show the efficiency of RUN in terms of communication reduction and fast reconfiguration process, through simulations on Roombots compared to other exiting techniques.

关键词： Modular robotic system Self-reconfiguration module clustering Efficient communication Roombots

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Context-Aware Expert for Software Architecture Recovery (CAESAR): An automated approach for recovering software architectures

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第8期35卷

作者： Ibrahim, Khaled Hassan, Hesham Wassif, Khaled T. Makady, Soha Cairo Univ Fac Comp & Artificial Intelligence 5 Dr Ahmed Zewail St Giza 12613 Orman Egypt

Software architecture recovery approaches help to reconstruct the architecture of complex software systems. However, the manual effort and expertise required to use such techniques are high, as the user demands detailed knowledge about software architecture recovery process and preparing additional software artifacts to use as input for such approaches. Such practices lack consideration for the context standards of the recovered software. Additionally, proper module detection rules that are aware of the modeled system's context should be studied to enhance the cluster quality of the detected modules. We introduce a novel approach to software architecture recovery by implementing an automated knowledge-based rules solution that considers the context of the developed software while identifying and clustering that system's software modules. Such solution is aided by a novel code distance model that is utilized to enhance the cohesiveness of the recovered software modules, improving the recovered software architecture quality, and enhancing readability through providing visualizations at different abstraction levels for the recovered architecture. The proposed method has been evaluated on two software systems with varying sizes and from different contexts and involved experienced participants. The results demonstrate significant improvements in cohesion and coupling of the produced clusters compared to alternative architecture recovery approaches. (C) 2023 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Software architecture Recovery algorithm Reverse engineering module clustering AUTOSAR FACE Software visualization

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Savalan: Multi objective and homogeneous method for software modules clustering

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JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS 2022年第1期34卷

作者： Arasteh, Bahman Fatolahzadeh, Ahmad Kiani, Farzad Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran

Reverse engineering is used for extracting and understanding software architecture models from source code when the source code is the only available software product. Software module clustering is a reverse engineering method which decomposes software modules into several clusters (subsystems) by using module dependency graph. Finding the best clusters for the modules of software is a multi-objective and NP-hard problem;maximizing the cohesion among the modules, minimizing the coupling among different clusters, and maximizing the software modularization quality are considered as the main objectives of software module clustering. Some of these objectives, such as cohesion and coupling, are in contradiction with each other. Simultaneous improvement of all clustering objectives (cohesion, coupling, modularization quality, size, and number of clusters) is the main goal of this study. In this paper, by capitalizing on multi objective genetic algorithm and a new combination of objective functions, we proposed a homogeneous method, namely, Savalan, for clustering software modules. The proposed method generates high-quality clusters with strong cohesion within clusters and weak connections between clusters for the input source code. The results of conducted experiments on the 14 benchmark programs indicate that simultaneous improvement of all clustering objectives is the main merit of this method. According to the experimental results, the proposed algorithm was able to outperform the previous multi objective methods.

关键词： homogenous clustering module clustering multi objective genetic algorithm software maintenance

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PSO-MoSR: a PSO-based multi-objective software remodularisation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第4期15卷 254-263页

作者： Prajapati, Amarjeet Kumar, Sanjeev Jaypee Inst Informat Technol Dept CSE & IT Noida India ABESIT Ghaziabad Dept Comp Engn Ghaziabad Uttar Pradesh India

The quality of modular structure of a software system highly affects the success of a software project. Software remodularisation which is used to improve the software structure is a complex task and involves the optimisation of multiple conflicting aspects. To address the optimisation of multiple objectives, many metaheuristic optimisation algorithms have been designed. The customisation of these algorithms according to the suitability of real-world multi-objective software remodularisation problem is a challenging task. In this article, particle swarm optimisation (PSO) a widely used metaheuristic heuristic technique is customised and proposed a PSO-based multi-objective software remodularisation (PSO-MoSR) to address the optimisation of multiple objective issues of software remodularisation. The effectiveness of the proposed PSO-MoSR is evaluated by conducting several experiments by modularising 17 real-world software systems.

关键词： remodularisation restructuring optimisation module clustering

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A Study on module clustering and Evaluation of Large Gantry Machining Centers

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2013年第2期13卷 021006-021006页

作者： Chen, Weifang Cao, Lijie Ye, Wenhua Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

Due to the various requirements of users and large volume of machine tools, most machine tool manufacturers are adopting the small-lot production mode, which largely prolongs the product development cycle. To raise the design efficiency and reduce the complexity, modular design is becoming widely used in serial production of machine tools, especially large gantry machining centers. In this paper, we introduced a function and structure decomposition method to construct the function and structure correlations between components and employed a clustering method based on atomic theory to solve the module clustering problems. The clustering algorithm is modified by integrating the function and structure matrices in order that it applies to modularization of large machine tools. Furthermore, an evaluation method based on the concept of entropy is proposed here to evaluate and optimal select module partition schemes. Finally, we took the large gantry machining center QLMT6300 as an example and compiled the program with Microsoft Visual C++ 6.0 to illustrate the validity of the methods proposed. The presented clustering and evaluation method can be easily applied to different machine tools for modular design, reducing the work amount of designers.

关键词： modular design module clustering module evaluation atomic theory function and structure decomposition gantry machining center

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Uneven granular module clustering and intelligent optimization for customizable products

Uneven granular module clustering and intelligent optimizati...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Zhang, Meng Li, Guoxi Li, Wei Gong, Jingzhong Natl Univ Def Technol Sch Mechatron Engn & Automat Changsha 410073 Hunan Peoples R China PLA Shifang 618419 Peoples R China

ISBN: (纸本)9783037855010

To solve the problems of the traditional approach to uniform granular module clustering, a new method for module clustering based on uneven granularity and intelligent optimization oriented to the customizable product design was proposed. Considering the impacts of requirements, functions and structures, the integrated fuzzy similarity matrix of parts was built using the correlativity analysis, and then the hierarchical structure was generated through a fuzzy clustering algorithm. All the universes of the granular layers in the hierarchical structure were gathered and the uneven granular module clustering scheme was formally presented. Four quantified indices including customizability index, customer satisfaction degree, design complexity and assembly complexity, were proposed to set up four optimization objective functions. Use nondominated sorting genetic algorithm II to solve the problem in order to obtain the Pareto optimal set. A design case of the single mast storage/retrieval machine was studied to demonstrate the feasibility of the proposed method.

关键词： uneven granular module clustering optimization single mast storage/retrieval machine

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Software module clustering as a Multi-Objective Search Problem

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2011年第2期37卷 264-282页

作者： Praditwong, Kata Harman, Mark Yao, Xin Univ Birmingham Sch Comp Sci Ctr Excellence Res Computat Intelligence & Applic Birmingham B15 2TT W Midlands England UCL Ctr Res Evolut Search & Testing CREST Software Engn Grp Dept Comp Sci London WC1E 6BT England

Software module clustering is the problem of automatically organizing software units into modules to improve program structure. There has been a great deal of recent interest in search-based formulations of this problem in which module boundaries are identified by automated search, guided by a fitness function that captures the twin objectives of high cohesion and low coupling in a single-objective fitness function. This paper introduces two novel multi-objective formulations of the software module clustering problem, in which several different objectives (including cohesion and coupling) are represented separately. In order to evaluate the effectiveness of the multi-objective approach, a set of experiments was performed on 17 real-world module clustering problems. The results of this empirical study provide strong evidence to support the claim that the multi-objective approach produces significantly better solutions than the existing single-objective approach.

关键词： SBSE module clustering multi-objective optimization evolutionary computation

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API Hyperlinking via Structural Overlap

API Hyperlinking via Structural Overlap

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7th Joint Meeting of the European Software Engineering Conference/ ACM SIGSOFT Symposium on the Foundations of Software Engineering

作者： Long, Fan Wang, Xi Cai, Yang Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781605580012

This paper presents a tool Altair that automatically generates APT function cross-references, which emphasizes reliable structural measures and does not depend on specific client code. Altair ranks related API functions for a given query according to pair-wise overlap, i.e., how they share state, and clusters tightly related ones into meaningful modules. Experiments against several popular C software packages show that Altair recommends related API functions for a given query with remarkably more precise and complete results than previous tools, that it can extract modules from moderate-sized software (e.g., Apache with 1000+ functions) at high precision and recall rates (e.g., both exceeding 70% for two modules in Apache), and that the computation can finish within a few seconds.

关键词： API recommendation overlap rank module clustering

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