The prediction of faulty classes using object-oriented design metrics

Emam, Khaled El; Melo, Walcélio L.; Machado, Javam C.

doi:10.1016/s0164-1212(00)00086-8

Cited by 274 publications

(219 citation statements)

References 49 publications

(68 reference statements)

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“…Indeed, precision and recall, which are traditional evaluation criteria used to evaluate the prediction accuracy of LR models, are subject to change as the selected threshold changes. The ROC curve, which is defined as a plot of sensitivity on the y-coordinate versus its 1-specificity on the xcoordinate, is an effective method of evaluating the performance of prediction models [16,17]. The optimal choice of the cutoff point that maximizes both sensitivity and specificity can be selected from the ROC curve.…”

Section: Model Evaluationmentioning

confidence: 99%

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

Badri

Flageol

2013

Lecture Notes in Computer Science

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Abstract. Software testing plays a crucial role in software quality assurance.Software testing is, however, a time and resource consuming process. It is, therefore, important to estimate as soon as possible the effort required to test software. Unfortunately, little is known about the prediction of the testing effort. The study presented in this paper aims at exploring empirically the prediction of the testing effort from use cases. We address the testing effort from the perspective of test suites size. We used four metrics to characterize the size and complexity of use cases, and three metrics to quantify different perspectives of the size of corresponding test suites. We used the univariate logistic regression analysis to evaluate the individual effect of each use case metric on the size of test suites. The multivariate logistic regression analysis was used to explore the combined effect of the use case metrics. The performance of the prediction models was evaluated using receiver operating characteristic analysis. An experimental study, using data collected from five Java case studies, is reported providing evidence that some of the use case metrics are significant predictors of the size of test suites.

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Section: Model Evaluationmentioning

confidence: 99%

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

Badri

Flageol

2013

Lecture Notes in Computer Science

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“…It estimates the probability of the presence of faults in a component, which helps to take valuable decisions on testing. A lot of research work have been done to identify the fault-prone components in a system [49][50][51][52], which are very relevant to our work. Different authors have focused on different characteristics associated with a component for counting faults.…”

Section: Fault-proneness-basedmentioning

confidence: 99%

“…The predictions are based on the code of the file in the current release, fault, and modification history of the file from previous releases. El Emam et al found that a class having high-export coupling value is more fault-prone [51]. A complex program might contain more faults compared to a simple program [53].…”

Section: Fault-proneness-basedmentioning

confidence: 99%

“…From these papers, it is observed that the estimated defect density (faultproneness) through static analysis and the prerelease defect density computed by testing is strongly correlated. El Emam et al [56] have experimentally proved that inheritance and external coupling metrics are strongly associated with faultproneness. Unlike these papers, our aim is not to investigate the characteristics of various components to check which components have high fault densities.…”

Section: Fault-proneness-basedmentioning

confidence: 99%

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Prioritizing Program Elements: A Pretesting Effort to Improve Software Quality

Ray

Mohapatra

2012

ISRN Software Engineering

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Improving the efficiency of a testing process is a challenging task. Prior work has shown that often, a small number of bugs account for the majority of the reported software failures; and often, most bugs are found in a small portion of the source code of a program. First, prioritizing the code elements according to their criticality and then conducting testing, will promote to reveal the important bugs at the early phase of testing. Keeping it in view, we propose an efficient test effort prioritization method that give a chance to the tester to focus more on the parts of the source code that are highly influenced towards the system failures or in which, the failures have high impact on the system. We consider five important factors such as influence towards system failures, average execution time, structural complexity, severity, and business value associated with a component and estimates the criticality of the component within a system. We have experimentally proved that our proposed test effort prioritization approach is effective in revealing important bugs at the early phase of testing as it is linked to external measure of defect severity and business value, internal measure of frequency, complexity, and coupling.

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“…Emam et al [17] attempted to find the best metric at predicting fault-proneness among 24 metrics that were proposed by Chidamber and Kemerer and Briand et al They found that the OCMEC (Other class-method export coupling) was the best among the 24 to be fault-proneness predictors. Liu and Xu [28] have pro posed an object-oriented metric suite that measures the magnitude of coupling between classes and show that their suite offers a new dimension of measurement complementing other metrics.…”

Section: Established Coupling Metricsmentioning

confidence: 99%

A Coupling-Complexity Metric Suite for Predicting Software Quality

Gray¹

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The prediction of faulty classes using object-oriented design metrics

Abstract: Abstract

Cited by 274 publications

References 49 publications

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

Prioritizing Program Elements: A Pretesting Effort to Improve Software Quality

A Coupling-Complexity Metric Suite for Predicting Software Quality

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