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An extensive analysis of search-based techniques for predicting defective classes

COMPUTERS & ELECTRICAL ENGINEERING 2018年 71卷 611-626页

作者： Malhotra, Ruchika Delhi Technol Univ Dept Comp Sci & Engn Discipline Software Engn Delhi India

In spite of constant planning, effective documentation and proper implementation of a software during its life cycle, many defects still occur. Various empirical studies have found that prediction models developed using software metrics can be used to predict these defects. Researchers have advocated the use of search-based techniques and their hybridized versions in literature for developing software quality prediction models. This study conducts an extensive comparison of 20 search-based techniques, 16 hybridized techniques and 17 machine-learning techniques amongst each other, to develop software defect prediction models using 17 data sets. The comparison framework used in the study is efficient as it (i) deals with the stochastic nature of the techniques (ii) provides a fair comparison amongst the techniques (iii) promotes repeatability of the study and (iv) statistically validates the results. The results of the study indicate promising ability of search-based techniques and their hybridized versions for predicting defective classes. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Defect prediction object-oriented metrics Search-based techniques Hybridized techniques Empirical validation

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Source code and design conformance, design pattern detection from source code by classification approach

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APPLIED SOFT COMPUTING 2015年 26卷 357-367页

作者： Chihada, Abdullah Jalili, Saeed Hasheminejad, Seyed Mohammad Hossein Zangooei, Mohammad Hossein Tarbiat Modares Univ Elect & Comp Engn Fac Dept Comp Engn SCS Lab Tehran Iran

Nowadays, software designers attempt to employ design patterns in software design phase, but design patterns may be not used in the implementation phase. Therefore, one of the challenging issues is conformance checking of source code and design, i.e., design patterns. In addition, after developing a system, usually its documents are not maintained, so, identifying design pattern from source code can help to achieve the design of an existing system as a reverse engineering task. The variant implementations (i.e., different source codes) of a design pattern make hard to detect the design pattern instances from the source code. To address this issue, in this paper, we propose a new method which aims to map the design pattern detection problem into a learning problem. The proposed design pattern detector is made by learning from the information extracted from design pattern instances which normally include variant implementations. To evaluate the proposed method, we applied it on open source codes to detect six different design patterns. The experimental results show that the proposed method is promising and effective. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Design pattern detection Machine learning Support vector machine object-oriented metrics

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Software metrics fluctuation: a property for assisting the metric selection process

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INFORMATION AND SOFTWARE TECHNOLOGY 2016年 72卷 110-124页

作者： Arvanitou, Elvira-Maria Ampatzoglou, Apostolos Chatzigeorgiou, Alexander Avgeriou, Paris Univ Groningen Dept Math & Comp Sci Zernike Campus Groningen Netherlands Univ Macedonia Dept Appl Informat Thessaloniki Greece

Context: Software quality attributes are assessed by employing appropriate metrics. However, the choice of such metrics is not always obvious and is further complicated by the multitude of available metrics. To assist metrics selection, several properties have been proposed. However, although metrics are often used to assess successive software versions, there is no property that assesses their ability to capture structural changes along evolution. objective: We introduce a property, Software Metric Fluctuation (SMF), which quantifies the degree to which a metric score varies, due to changes occurring between successive system's versions. Regarding SMF, metrics can be characterized as sensitive (changes induce high variation on the metric score) or stable (changes induce low variation on the metric score). Method: SMF property has been evaluated by: (a) a case study on 20 OSS projects to assess the ability of SMF to differently characterize different metrics, and (b) a case study on 10 software engineers to assess SMF's usefulness in the metric selection process. Results: The results of the first case study suggest that different metrics that quantify the same quality attributes present differences in their fluctuation. We also provide evidence that an additional factor that is related to metrics' fluctuation is the function that is used for aggregating metric from the micro to the macro level. In addition, the outcome of the second case study suggested that SMF is capable of helping practitioners in metric selection, since: (a) different practitioners have different perception of metric fluctuation, and (b) this perception is less accurate than the systematic approach that SMF offers. Conclusions: SMF is a useful metric property that can improve the accuracy of metrics selection. Based on SMF, we can differentiate metrics, based on their degree of fluctuation. Such results can provide input to researchers and practitioners in their metric selection processes. (C) 2

关键词： object-oriented metrics Fluctuation Case study Software evolution

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Finding software metrics threshold values using ROC curves

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JOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION-RESEARCH AND PRACTICE 2010年第1期22卷 1-16页

作者： Shatnawi, Raed Li, Wei Swain, James Newman, Tim Jordan Univ Sci & Technol Comp Informat Syst Dept Irbid 22110 Jordan Jordan Univ Alabama Dept Comp Sci Huntsville AL 35899 USA Univ Alabama Dept Ind Engn Huntsville AL 35899 USA

An empirical study of the relationship between object-oriented (00) metrics and error-severity categories is presented. The focus of the study is to identify threshold values of software metrics using receiver operating characteristic curves. The study used the three releases of the Eclipse project and found threshold values for some OO metrics that separated no-error classes from classes that had high-impact errors. Although these thresholds cannot predict whether a class will definitely have errors in the future, they can provide a more scientific method to assess class error proneness and can he used by engineers easily. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： object-oriented design object-oriented metrics thresholds ROC curve

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An implementation of metrics extraction and analyzer tool

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2007年第1期16卷 121-128页

作者： Quah, Tong-Seng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Nowadays, developers use N-tier application architecture which is a flexible and reusable structure for distribution to any number of client interfaces In this study, we have implemented metrics extraction and analyzer tool for Business Tier and Data Access Tier source codes. We used existing object oriented metrics and complexity software metrics as proposed for Business Tier code(1,3). We use a set of metrics for the Data Access Tier that has been proposed in our previous study(2). The primary aim of the implementation of the metric extraction and analyzer tool is to facilitate generation of software metrics which are used as independent variables in software quality prediction models. A secondary goal is to gather and present information on the attributes of source code to assist the programmers in managing the software projects. This tool is able to generate the principal components correlation analysis report and descriptive statistics report. These analysis reports are essential reports for the study of software measurement and software quality management.

关键词： N-tier architecture data access tier business logic tier object-oriented metrics metrics extraction and analyzer tool

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Analyzing Machine Learning Techniques for Fault Prediction Using Web Applications

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2018年第3期14卷 751-770页

作者： Malhotra, Ruchika Sharma, Anjali Delhi Technol Univ Dept Comp Sci & Engn Delhi India CSIR Natl Phys Lab Dept Planning Monitoring & Evaluat Delhi India

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

关键词： Empirical Validation Fault prediction Machine Learning object-oriented metrics Web Application Quality

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Comparative analysis of software fault prediction using various categories of classifiers

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第3期12卷 520-535页

作者： Kaur, Inderpreet Kaur, Arvinder GGSIPU USICT Dwarka India

The quality of the software being developed varies with the size and complexity of the software. It is a matter of concern in software development as it impairs the faith of customers on the software companies. The quality of software can be improved if the prediction of faults and flaws in it are done in the early phases of the software development and thus reducing the resources to be used in the testing phase. The rise in the use of object-oriented technology for developing software has paved the way for considering the object-oriented metrics for software fault prediction. Numerous machine learning and statistical techniques have been used to predict the defects in software using these software metrics as independent variables and bug proneness as dependent variable. Our work aims at finding the best category and hence the best classifier for classification of faults. This work uses twenty-one classifiers belonging to five categories of classification on five open source software having object-oriented metrics. The classification LearnerApp of MATLAB has been used to evaluate various classification models. The work proposes the use of Ensemble and SVM techniques over KNN, Regression, and Tree. The bagged trees (ensemble) and cubic (SVM) are found to be the best predictors amongst the twenty-one classifiers.

关键词： object-oriented metrics Fault prediction Ensemble Machine learning Nimenyi test Statistical tests

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Measuring software evolution at a nuclear fusion experiment site: a test case for the applicability of OO and reuse metrics in software characterization

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INFORMATION AND SOFTWARE TECHNOLOGY 2002年第10期44卷 593-600页

作者： Manduchi, G Taliercio, C EURATOM ENEA Ric Fus CONSORZIO RFX I-35127 Padua Italy

A set of software metrics has been used to provide empirical evidence on how code organization changes when a software product evolves. A Java application for graphical data display used in experimental physics has been used as a test case. Exploiting common patterns in the way software applications evolve is desirable as it would give designers and managers a better understanding of the software process. The analysis in which framework reuse has also been considered, highlighted a limited use of the inheritance mechanism and, despite an increase in the overall complexity, a substantial invariance of the internal application organization. This fact is explained by the increasing framework usage and integration during the product's lifetime. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： software evolution object-oriented metrics reuse metrics

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Software change prediction: a literature review

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2016年第4期54卷 240-256页

作者： Malhotra, Ruchika Bansal, Ankita Jain Delhi Technol Univ Dept Software Engn Delhi India

In industrial organisations, software products are quite large and complex, consisting of a number of classes. Thus, it is not possible to test all the products with a finite number of resources. Hence, it would be beneficial if we could predict in advance some of the attributes associated with the classes such as change proneness, defect proneness, maintenance effort, etc. In this paper, we have dealt with one of the quality attributes, i.e., change proneness. Changes in the software are unavoidable and thus, early prediction of change proneness will help the developers to focus the limited resources on the classes which are predicted to be change-prone. We have conducted a systematic review which evaluates all the available important studies relevant to the area of change proneness. This will help us to identify gaps in the current technology and discuss possible new directions of research in the areas related to change proneness.

关键词： empirical validation change prediction machine learning object-oriented metrics software quality

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Enhancing the fault prediction accuracy of CK metrics using high precision cohesion metric

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2016年第4期54卷 290-296页

作者： Kayarvizhy, N. Kanmani, S. Uthariaraj, V. Rhymend AMC Engn Coll Dept Comp Sci & Engn Bangalore 560083 Karnataka India Pondicherry Engn Coll Dept Informat Technol Pondicherry 605014 India Anna Univ Ramanujam Comp Ctr Madras 600025 Tamil Nadu India

object-oriented programs can be viewed as a collection of objects communicating with each other to perform a unique task. Many complex commercial applications have taken the object-oriented approach because of the benefits that it offers. The need for a reliable software resulted in the study and analysis of object-oriented metrics. The Chidamber and Kemerer (CK) metric suite has been considered as a pioneering work on object-oriented metrics and is the default standard for any new metric to be compared against. In this paper we evaluate the fault prediction capability of CK metric suite and validate it empirically. To further improve the accuracy of fault prediction we explore replacing the cohesion metric (LCOM) in CK suite with the proposed cohesion metric (high precision cohesion metric). We have considered data from 500 classes spread across 12 projects for the study. The results show that there is a considerable improvement in the prediction accuracy.

关键词： object-oriented metrics cohesion high precision cohesion metric HPCM

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