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Machine Learning-Based Exploration of the Impact of Move Method Refactoring on object-oriented software Quality Attributes

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第3期49卷 3867-3885页

作者： Al Dallal, Jehad Abdulsalam, Hanady AlMarzouq, Mohammad Selamat, Ali Gulf Univ Sci & Technol Dept Comp Sci Kuwait Kuwait Kuwait Univ Dept Informat Sci Safat Kuwait Kuwait Univ Dept Informat Syst & Operat Management Safat Kuwait Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Fac Comp Johor Baharu 81310 Malaysia Univ Teknol Malaysia Media & Games Ctr Excellence MagicX Johor Baharu 81310 Malaysia

Refactoring is a maintenance task that aims at enhancing the quality of a software's source code by restructuring it without affecting the external behavior. Move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%.

关键词： Class cohesion Class coupling Class size Internal quality attributes Logistic regression analysis Classification tree Machine learning Move method refactoring object-oriented software Quality measures software metrics

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Study of Learning Techniques for Effort Estimation in object-oriented software Development

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2022年 71卷 4602页

作者： Shukla, Suyash Kumar, Sandeep Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 India

software effort estimation (SEE) is helpful for project managers to decide the cost and effort needed to complete the project. The techniques used for estimating effort in conventional software development are not very useful for estimating effort in object-oriented projects because of their varying nature. The machine learning (ML) approaches are achieving greater recognition in SEE research because they can demonstrate the complex relationship between software effort and other attributes. So, there is a need for a systematic literature review (SLR) that can discuss the applicability of ML techniques for SEE in object-oriented projects, which is not available in the literature. This research aims to provide a specific review and analysis of various ML-based SEE works in the object-oriented software development (OOSD) paradigm based on different perspectives: type of learning technique used, type of performance measure used, performance level achieved, the dataset used, etc. Purposefully, we have chosen appropriate articles after applying selection and quality evaluation criteria. After investigation, we found that different ML techniques have been applied in these works, and their performance is better than the classical models. Hence, more efforts are needed to encourage the application of ML techniques for SEE in the OOSD paradigm. Also, most of the works have used small-sized datasets for effort estimation in OOSD, due to which their model's performance cannot be generalized. So, the researchers should collect more large-sized datasets working in line with the software organizations.

关键词： Machine learning (ML) object-oriented software software effort estimation (SEE)

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A Novel Test Case Generation Approach for Adaptive Random Testing of object-oriented software Using K-Means Clustering Technique

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第4期6卷 969-981页

作者： Chen, Jinfu Chen, Haibo Guo, Yuchi Zhou, Minmin Huang, Rubing Mao, Chengying Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Macau Univ Sci & Technol Fac Informat & Technol Macau 999078 Peoples R China Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Jiangxi Peoples R China

Random testing (RT) is considered important owing to the popularity of fuzzing techniques. Yet, its effectiveness has been questioned because it disregards the property that failures are usually conglomerated. Adaptive random testing (ART) has been developed to spread the test cases more evenly. A set of candidate test cases is first generated randomly. Then, the candidate farthest from the executed test cases is selected as the next test case. However, this process is time-consuming, especially for object-oriented programs. In order to improve the efficiency, a forgetting strategy may be added, taking into account only part of the executed test cases. Of course, the failure detection capability is compromised. In this paper, we propose a new approach that improves the efficiency of object-oriented program testing through K-means clustering. Frequency transform and improved wavelet transform are adopted to better determine the dissimilarity of test cases. Two measures, namely, the wavelet transform (WT) metric and the trisection frequency conversion (TFC) metric, are proposed. Based on the metrics, we develop two algorithms called WClustering-ART and TFClustering-ART. Experimental studies and statistical analysis have been conducted to evaluate the F-measure and the Fm-time of our approach. The results show that both algorithms demonstrate better effectiveness and efficiency than RT and object-oriented ART algorithms with forgetting.

关键词： Testing Subspace constraints Measurement Power capacitors Wavelet transforms software Frequency conversion Adaptive random testing frequency transform K-means clustering object-oriented software test case generation trisection frequency conversion wavelet transform

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A Model for Prediction of Understandability and Modifiability of object-oriented software 3rd

A Model for Prediction of Understandability and Modifiabilit...

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3rd Congress on Intelligent Systems (CIS)

作者： Babu, Sumit Singh, Raghuraj Harcourt Butler Tech Univ Kanpur 208002 Uttar Pradesh India

ISBN: (纸本)9789811992247;9789811992254

software quality measurement at an early stage helps in improving the product and process of the development. A good-quality software product is easy to maintain. Maintainability of software refers to the ease with which software can be understood, repaired, and improved. The longevity of software depends on the ability of developers to meet new customer requirements and address the problems faced by them. software maintenance is the most expensive phase of development and consumes more than half of the development budget. This work is carried out to find the early factors of object-oriented software (OOS) that help developers to refine the quality. The proposed model predicts two major attributes of maintainability, namely understandability and modifiability, ofOOSthroughUMLclass diagrams on the basis of size and structural complexity metrics. The model is validated by establishing a high correlationwith the existing predictionmodel for both the factors. In comparison with an existing model, the proposed model gives a correlation of 0.9603 and 0.9424 for understandability and modifiability, respectively.

关键词： object-oriented software software quality Size metric Structural complexity metric Neural network Understandability Modifiability Maintainability

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INTERNATIONAL JOURNAL OF software ENGINEERING AND KNOWLEDGE ENGINEERING 2019年第4期29卷 577-606页

作者： Chen, Jinfu Kudjo, Patrick Kwaku Zhang, Zufa Su, Chenfei Guo, Yuchi Huang, Rubing Song, Heping Jiangsu Univ Sch Comp Sci & Commun Engn 301 Xuefu Rd Zhenjiang Jiangsu Peoples R China

Finding an effective method for testing object-oriented software (OOS) has proven elusive in the software community due to the rapid development of object-oriented programming (OOP) technology. Although significant progress has been made by previous studies, challenges still exist in relation to the object distance measurement of OOS using Adaptive Random Testing (ART). This is partly due to the unique features of OOS such as encapsulation, inheritance and polymorphism. In a previous work, we proposed a new similarity metric called the object and Method Invocation Sequence Similarity (OMISS) metric to facilitate multi-class level testing using ART. In this paper, we broaden the set of models in the metric (OMISS) by considering the method parameter and adding the weight in the metric to develop a new distance metric to improve unit testing of OOS. We used the new distance metric to calculate the distance between the set of objects and the distance between the method sequences of the test cases. Additionally, we integrate the new metric in unit testing with ART and applied it to six open source subject programs. The experimental result shows that the proposed method with method parameter considered in this study is better than previous methods without the method parameter in the case of the single method. Our finding further shows that the proposed unit testing approach is a promising direction for assisting software engineers who seek to improve the failure-detection effectiveness of OOS testing.

关键词： Adaptive random testing object-oriented software object distance unit testing method parameter

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Measuring the effect of clone refactoring on the size of unit test cases in object-oriented software: an empirical study

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INNOVATIONS IN SYSTEMS AND software ENGINEERING 2019年第2期15卷 117-137页

作者： Badri, Mourad Badri, Linda Hachemane, Oussama Ouellet, Alexandre Univ Quebec Trois Rivieres Software Engn Res Lab Dept Math & Comp Sci Trois Rivieres PQ Canada

This paper aims at empirically measuring the effect of clone refactoring on the size of unit test cases in object-oriented software. We investigated various research questions related to the: (1) impact of clone refactoring on source code attributes (particularly size, complexity and coupling) that are related to testability of classes, (2) impact of clone refactoring on the size of unit test cases, (3) correlations between the variations observed after clone refactoring in both source code attributes and the size of unit test cases and (4) variations after clone refactoring in the source code attributes that are more associated with the size of unit test cases. We used different metrics to quantify the considered source code attributes and the size of unit test cases. To investigate the research questions, and develop predictive and explanatory models, we used various data analysis and modeling techniques, particularly linear regression analysis and five machine learning algorithms (C4.5, KNN, Naive Bayes, Random Forest and Support Vector Machine). We conducted an empirical study using data collected from two open-source Java software systems (ANT and ARCHIVA) that have been clone refactored. Overall, the paper contributions can be summarized as: (1) the results revealed that there is a strong and positive correlation between code clone refactoring and reduction in the size of unit test cases, (2) we showed how code quality attributes that are related to testability of classes are significantly improved when clones are refactored, (3) we observed that the size of unit test cases can be significantly reduced when clone refactoring is applied, and (4) complexity/size measures are commonly associated with the variations of the size of unit test cases when compared to coupling.

关键词： object-oriented software Clone refactoring Source code attributes Unit test cases Metrics Relationships Linear regression Machine learning algorithms

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An Automatic Testing Platform for object-oriented software based on Code Coverage 2019

An Automatic Testing Platform for Object-oriented Software b...

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Proceedings of the 2019 the 9th International Conference on Communication and Network Security

作者： Bo Liu Honghe Ge Jinfu Chen Qihao Bao Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China

ISBN: (纸本)9781450376624

The development of automatic object-oriented software testing tools is a challenging and realistic subject in the field of software engineering. A testing prototype system named ARTCovPS (Adaptive Random Testing Coverage-based testing Prototype System) is designed and implemented for the widely used object-oriented method software. ARTCovPS mainly performs automated testing from two aspects, dynamic test case generation of object-oriented software based on coverage and adaptive random testing method based on coverage. To a certain extent, ARTCovPS testing prototype system realizes the software test automation, and the system has a high running efficiency and can automatically run different comparison methods. The experimental results are satisfactory, and the feasibility of the system is also verified.

关键词： test case generation testing tool adaptive random testing object-oriented software code coverage

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Test case prioritization for object-oriented software: An adaptive random sequence approach based on clustering

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JOURNAL OF SYSTEMS AND software 2018年 135卷 107-125页

作者： Chen, Jinfu Zhu, Lili Chen, Tsong Yueh Towey, Dave Kuo, Fei-Ching Huang, Rubing Guo, Yuchi Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 202000 Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Hawthorn Vic 3122 Australia Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315100 Zhejiang Peoples R China

Test case prioritization (TCP) attempts to improve fault detection effectiveness by scheduling the important test cases to be executed earlier, where the importance is determined by some criteria or strategies. Adaptive random sequences (ARSs) can be used to improve the effectiveness of TCP based on white-box information (such as code coverage information) or black-box information (such as test input information). To improve the testing effectiveness for object-oriented software in regression testing, in this paper, we present an ARS approach based on clustering techniques using black-box information. We use two clustering methods: (1) clustering test cases according to the number of objects and methods, using the K-means and K-medoids clustering algorithms;and (2) clustered based on an object and method invocation sequence similarity metric using the K-medoids clustering algorithm. Our approach can construct ARSs that attempt to make their neighboring test cases as diverse as possible. Experimental studies were also conducted to verify the proposed approach, with the results showing both enhanced probability of earlier fault detection, and higher effectiveness than random prioritization and method coverage TCP technique. (C) 2017 Elsevier Inc. All rights reserved.

关键词： object-oriented software Adaptive random sequence Test cases prioritization Cluster analysis Test cases selection

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What Strokes to Modify in the Painting? Code Changes Prediction for object-oriented software 8th

What Strokes to Modify in the Painting? Code Changes Predict...

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8th Annual International Conference on software Analysis, Testing, and Evolution (SATE)

作者： Zhang, Dinan Chen, Shizhan He, Qiang Feng, Zhiyong Huang, Keman Tianjin Key Lab Cognit Comp & Applicat Tianjin 300072 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic 3783 Australia Tianjin Univ Sch Comp Software Tianjin 300072 Peoples R China Sloan Sch Management Cambridge MA 02142 USA

ISBN: (纸本)9783030042721;9783030042714

software systems shall evolve to fulfill users' increasingly various and sophisticated needs. As they become larger and more complex, the corresponding testing and maintenance have become a practical research challenge. In this paper, we employ an approach that can identify the change-proneness in the source code of new object-oriented software releases and predict the corresponding change sizes. We first define two metrics, namely Class Change Metric and Change Size Metric, to describe the features and sizes of code changes. A new software release may be based on several previous releases. Thus, we employ an Entropy Weight Method to calculate the best window size for determining the number of previous releases to use in the prediction of change-proneness in the new release. Based on a series of change evolution matrices, a code change prediction approach is proposed based on the Gauss Process Regression (GPR) algorithm. Experiments are conducted on 17 software systems collected from GitHub to evaluate our prediction approach. The results show that our approach outperforms three existing state-of-the-art approaches with significantly higher prediction accuracy.

关键词： Change histories software evolution object-oriented software software metrics Change-prone source code

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Comparative Analysis of object-oriented software Maintainability Prediction Models

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2018年第4期43卷 359-374页

作者： Zighed, Narimane Bounour, Nora Seriai, Abdelhak-Djamel Badji Mokhtar Univ Annaba Algeria Montpellier Univ Montpellier France

software maintainability is one of the most important aspects when evaluating the quality of a software product. It is defined as the ease with which the existing software can be modified. In the literature, several researchers have proposed a large number of models to measure and predict maintainability throughout different phases of the software Development Life Cycle. However, only a few attempts have been made for conducting a comparative study of the existent proposed prediction models. In this paper, we present a detailed classification and conduct a comparative analysis of object-oriented software maintainability prediction models. Furthermore, we considered the aforementioned proposed models from three perspectives, which are architecture, design and code levels. To the best of our knowledge, such an analysis that comprises the three levels has not been conducted in previous research. Moreover, this study hints at certain fundamental basics concerning the way of how measure the maintainability knowing that at each level the maintainability will be measured differently. In addition, we will focus on the strengths and weaknesses of these models. Consequently, the comparative study yields that several statistical and machine learning techniques have been employed for software maintainability prediction at code level during the last decade, and each technique possesses its specific characteristic to develop an accurate prediction model. At the design level, the majority of the prediction models measured maintainability according to the characteristics of the quality models. Whereas at the architectural level, the techniques adopted are still limited and only a few of studies have been conducted in this regard.

关键词： Metrics Maintainability Prediction object-oriented software Quality Model Prediction Model

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