Empirical analysis for investigating the effect of object‐oriented metrics on fault proneness: a replicated case study

Aggarwal, K. K.; Singh, Yogesh; Kaur, Amandeep; Malhotra, Ruchika

doi:10.1002/spip.389

Cited by 110 publications

(90 citation statements)

References 24 publications

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“…It is suitable for building software quality classification models. It is used to predict the dependent variable from a set of independent variables to determine the percent of variance in the dependent variable explained by the independent variables [9, 51,52]. This technique has been widely applied to the prediction of fault-prone classes [20,[52][53][54][55][56].…”

Section: Logistic Regression Analysis: Researchmentioning

confidence: 99%

Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

Badri¹,

Touré²

2012

JSEA

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In this paper, we investigate empirically the relationship between object-oriented design metrics and testability of classes. We address testability from the point of view of unit testing effort. We collected data from three open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required unit testing effort, in two categories: high and low. In order to evaluate the relationship between object-oriented design metrics and unit testing effort of classes, we used logistic regression methods. We used the univariate logistic regression analysis to evaluate the individual effect of each metric on the unit testing effort of classes. The multivariate logistic regression analysis was used to explore the combined effect of the metrics. The performance of the prediction models was evaluated using Receiver Operating Characteristic analysis. The results indicate that: 1) complexity, size, cohesion and (to some extent) coupling were found significant predictors of the unit testing effort of classes and 2) multivariate regression models based on object-oriented design metrics are able to accurately predict the unit testing effort of classes.

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Section: Logistic Regression Analysis: Researchmentioning

confidence: 99%

Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

Badri¹,

Touré²

2012

JSEA

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“…The study described by Arvinder et al [32] is a replication of an analogous study conducted by Briand et al [7]. The study provided empirical evidence to draw the strong conclusions across studies.…”

Section: Related Workmentioning

confidence: 72%

Empirical Evaluation of Machine Learning Algorithms for Fault Prediction

Kaur¹,

Kaur²

2014

LNSE

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Abstract-Producing quality software is a very challenging task looking at the size and complexity of software developed these days. Predicting software quality early helps in using testing resources optimally. So, many statistical and machine learning techniques are used to predict quality classes in software. In this work, six machine learning classifiers have been used to estimate the fault proneness of 5885 classes used in five open source software on the basis of object-oriented metrics calculated on these classes. Bagging and J48 classifiers turn out to be the best one amongst the classifiers used.

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“…It is suitable for building software quality classification models. It is used to predict the dependent variable from a set of independent variables to determine the percent of variance in the dependent variable explained by the independent variables [1,7,37]. This technique has been widely applied to the prediction of fault-prone classes [e.g., 11,12,20,26,33,37].…”

Section: Logistic Regression Analysis: Research Methodologymentioning

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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Empirical analysis for investigating the effect of object‐oriented metrics on fault proneness: a replicated case study

Cited by 110 publications

References 24 publications

Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

Empirical Evaluation of Machine Learning Algorithms for Fault Prediction

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

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