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50th Annual Convention of the Computer-Society-of-India (CSI) on Digital Life

作者： Singh, Satwinder Singla, Rozy BBSBEC Fatehgarh Sahib Punjab India

ISBN: (纸本)9789811088483;9789811088476

Today, defect prediction is an important part of software industry to meet deadlines for their products. Defect prediction techniques help the organizations to use their resources effectively which results in lower cost and time requirements. Various metrics are used for defect prediction in within company (WC) and cross-company (CC) projects. In this paper, we used object-oriented metrics to build a defect prediction model for within company and cross-company projects. In this paper, feed-forward neural network (FFNN) model is used to build a defect prediction model. The proposed model was tested over four datasets against within company defect prediction (WCDP) and cross-company defect prediction (CCDP). The proposed model gives good results for WCDP and CCDP as compared to previous studies.

关键词： object-oriented metrics Defect prediction Artificial neural network (ANN)

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Mining the impact of evolution categories on object-oriented metrics

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SOFTWARE QUALITY JOURNAL 2013年第4期21卷 529-549页

作者： Rocha, Henrique Couto, Cesar Maffort, Cristiano Garcia, Rogel Simoes, Clarisse Passos, Leonardo Valente, Marco Tulio Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil CEFET MG Dept Comp Belo Horizonte MG Brazil Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Despite the relevance of the software evolution phase, there are few characterization studies on recurrent evolution growth patterns and on their impact on software properties, such as coupling and cohesion. In this paper, we report a study designed to investigate whether the software evolution categories proposed by Lanza can be used to explain not only the growth of a system in terms of lines of code (LOC), but also in terms of metrics from the Chidamber and Kemerer (CK) object-oriented metrics suite. Our results show that high levels of recall (ranging on average from 52 to 72 %) are achieved when using LOC to predict the evolution of coupling and size. For cohesion, we have achieved smaller recall rates (< 27 % on average).

关键词： Software evolution categories Patterns of evolution object-oriented metrics CK metrics Evolution matrix

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The confounding effect of class size on the validity of object-oriented metrics

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2001年第7期27卷 630-650页

作者： Emam, KE Benlarbi, S Goel, N Rai, SN Natl Res Council Canada Inst Informat Technol Ottawa ON K1A 0R6 Canada Cistel Technol Nepean ON K2E 7L5 Canada St Jude Childrens Res Hosp Dept Biostat & Epidemiol Memphis TN 38105 USA

Much effort has been devoted to the development and empirical validation of object-oriented metrics. The empirical validations performed thus far would suggest that a core set of validated metrics is close to being identified. However, none of these studies allow for the potentially confounding effect of class size. In this paper, we demonstrate a strong size confounding effect and question the results of previous object-oriented metrics validation studies. We first investigated whether there is a confounding effect of class size in validation studies of object-oriented metrics and show that, based on previous work, there is reason to believe that such an effect exists. We then describe a detailed empirical methodology for identifying those effects. Finally, we perform a study on a large C++ telecommunications framework to examine if size is really a confounder. This study considered the Chidamber and Kemerer metrics and a subset of the Lorenz and Kidd metrics. The dependent variable was the incidence of a fault attributable to a field failure (fault-proneness of a class). Our findings indicate that, before controlling for size, the results are very similar to previous studies: The metrics that are expected to be validated are indeed associated with fault-proneness. After controlling for size, none of the metrics we studied were associated with fault-proneness anymore. This demonstrates a strong size confounding effect and casts doubt on the results of previous object-oriented metrics validation studies. It is recommended that previous validation studies be reexamined to determine whether their conclusions would still hold after controlling for size and that future validation studies should always control for size.

关键词： object-oriented metrics software quality metrics validation validation methodology object-oriented quality coupling metrics inheritance metrics cohesion metrics

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Fault prediction considering threshold effects of object-oriented metrics

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EXPERT SYSTEMS 2015年第2期32卷 203-219页

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

Software product quality can be enhanced significantly if we have a good knowledge and understanding of the potential faults therein. This paper describes a study to build predictive models to identify parts of the software that have high probability of occurrence of fault. We have considered the effect of thresholds of object-oriented metrics on fault proneness and built predictive models based on the threshold values of the metrics used. Prediction of fault prone classes in earlier phases of software development life cycle will help software developers in allocating the resources efficiently. In this paper, we have used a statistical model derived from logistic regression to calculate the threshold values of object oriented, Chidamber and Kemerer metrics. Thresholds help developers to alarm the classes that fall outside a specified risk level. In this way, using the threshold values, we can divide the classes into two levels of risk - low risk and high risk. We have shown threshold effects at various risk levels and validated the use of these thresholds on a public domain, proprietary dataset, KC1 obtained from NASA and two open source, Promise datasets, IVY and JEdit using various machine learning methods and data mining classifiers. Interproject validation has also been carried out on three different open source datasets, Ant and Tomcat and Sakura. This will provide practitioners and researchers with well formed theories and generalised results. The results concluded that the proposed threshold methodology works well for the projects of similar nature or having similar characteristics.

关键词： empirical validation logistic regression machine learning receiver operating characteristics object-oriented metrics software quality

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Prioritizing unit tests using object-oriented metrics, centrality measures, and machine learning algorithms

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INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2024年 1-27页

作者： Levasseur, Marc-Antoine Badri, Mourad Univ Quebec Trois Rivieres Software Engn Res Lab Dept Math & Comp Sci Trois Rivieres PQ G9A 5H7 Canada

Nowadays, increasing complexity and size of object-oriented software systems bring new software quality assurance challenges. Unit testing is one of the main phases of the testing process, where each class (as a software unit) is early and individually tested using dedicated unit test cases. Due to limited resources and tight time deadlines constraints, unit testing often has to be done under severe pressure. As a consequence, the unit (tests) testing efforts have to be focused (prioritized). Many studies have proposed approaches based on object-oriented metrics and machine learning classifiers to predict the unit testing efforts (prioritizing unit tests). To the best of our knowledge, there is no study combining object-oriented metrics and centrality measures (network measures) for predicting the unit testing efforts (prioritizing unit tests). This paper aims basically at: (1) investigating the usefulness of centrality measures as indicators of the unit testing effort, (2) exploring how they can be combined to object-oriented metrics (already used in the literature) to improve the prediction of the unit testing efforts (prioritize unit tests), (3) exploring if the combination of object-oriented metrics and centrality measures can be used to predict different levels of the unit testing effort, and (4) exploring if the combination of object-oriented metrics and centrality measures can be used to identify a set of suitable candidate classes for unit testing (prioritizing unit tests). We investigated the performance of object-oriented metrics and centrality measures, and different machine learning algorithms in the prediction of the unit testing efforts (prioritizing unit tests). The unit testing effort of classes is addressed from the perspective of unit test cases construction. We focused particularly on the effort involved in writing the code of unit test cases. To capture the involved unit testing effort of classes, we used two test code metrics used in many studie

关键词： object-oriented metrics Centrality measures Unit testing effort prediction Machine learning classification

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Empirical Study on the Distribution of object-oriented metrics in Software Systems 25th

Empirical Study on the Distribution of Object-Oriented Metri...

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25th International Conference on Information and Software Technologies (ICIST)

作者： Muthukumaran, K. Murthy, N. L. Bhanu Janani, P. Sarguna Manipal Univ Jaipur Sch Comp & Informat Technol Jaipur Rajasthan India BITS Pilani Dept Comp Sci & Informat Syst Hyderabad Campus Hyderabad Telangana India

ISBN: (纸本)9783030302757;9783030302740

We attempt to model the probability distribution of object-oriented software metrics. We employ 5 distribution models to find out the distributions the metrics follow. We use AIC, BIC and RMSE as goodness-of-fit measures. Though the past studies have shown that the software projects frequently follow power law, having a Pareto distribution, we seek to study more number of software systems and distribution models to infer more generalizable results, since they occasionally seem to follow Log-normal or Gamma distribution as well. Apart from these three models we have also considered Weibull distribution and Generalized Pareto Distribution (GPD). In this study, we have made an attempt to answer the hypothesis that the object-oriented software metrics follow a particular distribution by comparing various distributions applied over a large number of projects using a recognized statistical framework.

关键词： object-oriented metrics Software metrics distribution Software quality Model selection

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An Outlier Detection Algorithm Based on object-oriented metrics Thresholds

An Outlier Detection Algorithm Based on Object-Oriented Metr...

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24th International Symposium on Computer and Information Sciences

作者： Alan, Oral Catal, Cagatay TUBITAK Marmara Res Ctr Inst Informat Technol TR-41470 Kocaeli Turkey

ISBN: (纸本)9781424450213

Detection of outliers in software measurement datasets is a critical issue that affects the performance of software fault prediction models built based on these datasets. Two necessary components of fault prediction models, software metrics and fault data, are collected from the software projects developed with object-oriented programming paradigm. We proposed an outlier detection algorithm based on these kinds of metrics thresholds. We used Random Forests machine learning classifier on two software measurement datasets collected from jEdit open-source text editor project and experiments revealed that our outlier detection approach improves the performance of fault predictors based on Random Forests classifier.

关键词： outlier detection software fault prediction metrics thresholds object-oriented metrics

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Theoretical Validation of object-oriented metrics for Data Warehouse Multidimensional Model 5th

Theoretical Validation of Object-Oriented Metrics for Data W...

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5th International Conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Gosain, Anjana Gupta, Rakhi Guru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol New Delhi 110078 India

ISBN: (纸本)9789811031533;9789811031526

metrics are commonly used to guide the designers to build quality data warehouse models. Recently, researchers have defined various object-oriented metrics for data warehouse conceptual model to access their quality. These metrics require theoretical and empirical validation to confirm their applicability in real time. Empirical validation of object-oriented metrics has already been carried out but theoretical validation has not been taken into account. In this paper, theoretical validation for object-oriented metrics using Zuse's framework is presented to show that these metrics may be considered as strong measures for evaluating quality of object-oriented conceptual models of data warehouse.

关键词： object-oriented metrics object-oriented data warehouse Theoretical validation Zuse's framework

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Detection of Fault-Prone Classes Using Logistic Regression Based object-oriented metrics Thresholds 2

Detection of Fault-Prone Classes Using Logistic Regression B...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Hussain, Shahid Keung, Jacky Khan, Arif Ali Bennin, Kwabena Ebo City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509037131

Background: In the plethora of studies, the object-oriented metrics have been empirically validated to assess the design properties and quantify the high-level quality attributes such as fault-proneness, either at the method or class granularity levels of software. Motivation: A more precise value of an object-oriented metric can be used as an indicator for the developers to make the informed decisions regarding the detection of design flaws and classify the fault-proneness classes. Method: Bender used an approach in the domain of epidemiology studies to derive the threshold values for the risk factors. In our study, we follow the Bender's approach and propose a model to derive the thresholds for a set of software design metrics via non-linear functions, which are described through logistic regression coefficients. Subsequently, we perform four types of analysis and three experiments in order to evaluate and compare the effectiveness of derived thresholds in the domain of classification of fault proneness classes. We use the Precision, Recall, F-measure and classification accuracy performance measures to assess the effectiveness of derived metrics thresholds. Results: We compare the derive threshold values of DIT, CA, LCOM and NPM metrics with their existing data distribution based threshold values, and observed the significant increase in the classification accuracy of fault-prone classes. For example, DIT (27%), Ca (2%), NPM (2%) and LCOM (15%) for the Ant-1.5 project. Conclusion: The analysis results suggest that the proposed model can be applied to derive the thresholds of other object-oriented metrics which present either with or without heavy-tailed distribution, however, the proposed model to derive thresholds cannot generalize for all the systems due to variation in data characteristics.

关键词： object-oriented metrics Logistic Regression Fault Proneness Complexity Classification Performance Measures

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Validating the Effectiveness of object-oriented metrics over Multiple Releases for Predicting Fault Proneness

Validating the Effectiveness of Object-Oriented Metrics over...

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19th Asia-Pacific Software Engineering Conference (APSEC)

作者： Rathore, Santosh Singh Gupta, Atul Indian Inst Informat Technol Design & Mfg Jabalpur India

ISBN: (纸本)9780769549224

In this paper, we empirically investigate the relationship of existing class level object-oriented metrics with fault proneness over the multiple releases of the software. Here we first, evaluate each metric for their potential to predict faults independently by performing univariate logistic regression analysis. Next, we perform cross-correlation analysis between the significant metrics to find the subset of these metrics for an improved performance. The obtained metrics subset was then used to predict faults over the subsequent releases of the same project datasets. In this study, we used five publicly available project datasets over their multiple successive releases. Our results reported that the identified subset metrics demonstrated an improved fault prediction with higher accuracy and reduced misclassification errors.

关键词： object-oriented metrics software fault prediction empirical validation

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