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Metrics for measuring the quality of modularization of large-scale object-oriented software

IEEE TRANSACTIONS ON software ENGINEERING 2008年第5期34卷 700-720页

作者： Sarkar, Santonu Kak, Avinash C. Rama, Girish Maskeri Accenture Technol Labs Bangalore 560029 Karnataka India Purdue Univ W Lafayette IN 47907 USA Infosys Technol Ltd SETLabs Bangalore 560100 Karnataka India

The metrics formulated to date for characterizing the modularization quality of object-oriented software have considered module and class to be synonymous concepts. But, a typical class in object-oriented programming exists at too low a level of granularity in large object-oriented software consisting of millions of lines of code. A typical module (sometimes referred to as a superpackage) in a large object-oriented software system will typically consist of a large number of classes. Even when the access discipline encoded in each class makes for "clean" class-level partitioning of the code, the intermodule dependencies created by associational, inheritance-based, and method invocations may still make it difficult to maintain and extend the software. The goal of this paper is to provide a set of metrics that characterizes large object-oriented software systems with regard to such dependencies. Our metrics characterize the quality of modularization with respect to the APIs of the modules, on the one hand, and, on the other, with respect to such object-oriented intermodule dependencies as caused by inheritance, associational relationships, state access violations, fragile base-class design, etc. Using a two-pronged approach, we validate the metrics by applying them to popular open-source software systems.

关键词： superpackage modularization software metrics/measurement module interface object-oriented software large-scale software maintenance and enhancement maintainability coupling

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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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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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Using self-organizing maps to analyze object-oriented software measures

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JOURNAL OF SYSTEMS AND software 2001年第1期59卷 65-82页

作者： Pedrycz, W Succi, G Musílek, P Bai, X Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G7 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

In this study, we present self-organizing maps and discuss their role in the analysis and visualization of software modules in the space of software measures. We reveal how self-organizing maps create a user-friendly and interactive visualization tool that helps user/software designer inspect various alternatives and get a thorough insight into the structure of the clusters of the software modules and the related metrics (software measures). We show how using self-organizing maps we can grow clusters in a dynamic fashion and thus explicitly capture relationships between the software measures and quantify these dependencies for larger and less homogeneous clusters of software modules. The experimental environment exploited in this study relies on software measures coming from 10 large public domain systems, 5 Java and 5 C++ systems. (C) 2001 Elsevier Science Inc. All rights reserved.

关键词： software measures analysis self-organizing maps information granules learning object-oriented software software design visualization dependency analysis neural networks

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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 method for investigating the quality of evolving object-oriented software using defects in global software development projects

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JOURNAL OF software-EVOLUTION AND PROCESS 2016年第8期28卷 622-641页

作者： Jabangwe, Ronald Wohlin, Claes Petersen, Kai Smite, Darja Borstler, Jurgen Dundalk Inst Technol Regulated Software Res Ctr Lero Dundalk Ireland Blekinge Inst Technol Dept Software Engn S-37179 Karlskrona Sweden

Context: Global software development (GSD) projects can have distributed teams that work independently in different locations or team members that are dispersed. The various development settings in GSD can influence quality during product evolution. When evaluating quality using defects as a proxy, the development settings have to be taken into consideration. objective: The aim is to provide a systematic method for supporting investigations of the implication of GSD contexts on defect data as a proxy for quality. Method: A method engineering approach was used to incrementally develop the proposed method. This was done through applying the method in multiple industrial contexts and then using lessons learned to refine and improve the method after application. Results: A measurement instrument and visualization was proposed incorporating an understanding of the release history and understanding of GSD contexts. Conclusion: The method can help with making accurate inferences about development settings because it includes details on collecting and aggregating data at a level that matches the development setting in a GSD context and involves practitioners at various phases of the investigation. Finally, the information that is produced from following the method can help practitioners make informed decisions when planning to develop software in comparable circumstances. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： global software development software quality software evolution object-oriented software distributed software development defect analysis

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An exploratory study of package metrics as change size indicators in evolving object-oriented software

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COMPUTER SYSTEMS SCIENCE AND ENGINEERING 2013年第4期28卷 251-257页

作者： Elish, Mahmoud O. King Fahd Univ Petr & Minerals Dept Informat & Comp Sci Dhahran 31261 Saudi Arabia

software change size prediction can be very useful and helpful in guiding the maintenance team;distributing the resources more efficiently;and, thus, enabling project managers and their teams to focus their effort and attention on the highly unstable artifacts during the software evolution process. This paper empirically explores a set of package-level metrics as change size indicators in evolving object-oriented software. Both structural properties (SP) metrics and historical changes (HC) metrics were explored. Metrics were collected and evaluated in the context of seven major releases of Apache Ant. The results indicate that there are statistically significant correlations between each of the HC metrics and packages' change size in multiple, sequential releases of Apache Ant. In addition, it was observed that the HC metrics are more accurate than the SP metrics in predicting change size in packages throughout the releases. Furthermore, there was no confounding effect of package size in the validity of some of the metrics.

关键词： software metrics software evolution change size packages object-oriented software

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Equality to Equals and Unequals: A Revisit of the Equivalence and Nonequivalence Criteria in Class-Level Testing of object-oriented software

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IEEE TRANSACTIONS ON software ENGINEERING 2013年第11期39卷 1549-1563页

作者： Chen, Huo Yan Tse, T. H. Jinan Univ Dept Comp Sci Guangzhou 510632 Guangdong Peoples R China Univ Hong Kong Dept Comp Sci Pokfulam Hong Kong Peoples R China

Algebraic specifications have been used in the testing of object-oriented programs and received much attention since the 1990s. It is generally believed that class-level testing based on algebraic specifications involves two independent aspects: the testing of equivalent and nonequivalent ground terms. Researchers have cited intuitive examples to illustrate the philosophy that even if an implementation satisfies all the requirements specified by the equivalence of ground terms, it may still fail to satisfy some of the requirements specified by the nonequivalence of ground terms. Thus, both the testing of equivalent ground terms and the testing of nonequivalent ground terms have been considered as significant and cannot replace each other. In this paper, we present an innovative finding that, given any canonical specification of a class with proper imports, a complete implementation satisfies all the observationally equivalent ground terms if and only if it satisfies all the observationally nonequivalent ground terms. As a result, these two aspects of software testing cover each other and can therefore replace each other. These findings provide a deeper understanding of software testing based on algebraic specifications, rendering the theory more elegant and complete. We also highlight a couple of important practical implications of our theoretical results.

关键词： software testing equivalence criterion nonequivalence criterion algebraic specification object-oriented software

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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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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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