Class level fault prediction using software clustering

Scanniello, Giuseppe; Gravino, Carmine; Marcus, Andrian

doi:10.1109/ase.2013.6693126

Cited by 53 publications

(62 citation statements)

References 29 publications

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“…For instance, some MCC and MCEC metrics in Table 15 already have obtained AUC greater than 0.7, which indicates an acceptable predictor for software faults. In fault prediction practices, class cohesion metrics are usually used together with many other software metrics to obtain more practical and actionable results (Gyimothy et al, 2005;Scanniello et al, 2013).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Exploring community structure of software Call Graph and its applications in class cohesion measurement

Guan

Zheng

et al. 2015

Journal of Systems and Software

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Section: Accepted Manuscriptmentioning

confidence: 99%

Exploring community structure of software Call Graph and its applications in class cohesion measurement

Guan

Zheng

et al. 2015

Journal of Systems and Software

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“…The relationship is expressed in the form of an equation that predicts the response variable as a linear function of predictor variable. [42,24,51,25] Linear Regression: Y=a+bX+u 2. Association Rule Mining: It is a method for discovering interesting relationships between variables in large databases.…”

Section: Software Defect Predictionmentioning

confidence: 99%

A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

Prasad¹,

Florence²,

Arya³

2015

IJDTA

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“…Examples of algorithms are logistic regression used by Zimmermann et al [40]; Multi-Layer Perceptron (MLP), radial basis function (RBF), k-nearest neighbor (KNN), regression tree (RT), dynamic evolving neuro-fuzzy inference system (DENFIS), and Support Vector Regression (SVR) used by Elish [14]; Bayesian networks used by Bechta [31]; and Naive Bayes, J48, Alternative Decision Tree (ADTree), and One-R considered by Nelson et al [30]. Recently, other researchers have proposed further advanced machine learning techniques, such as ensemble learning [23], clustering algorithms [36], and combined techniques [32]. Lessman et al [22] evaluated 22 classification models and showed that there is no statistical difference between the top-17 models when classifying software modules as defect prone.…”

Section: Previous Workmentioning

confidence: 99%

A Search-based Training Algorithm for Cost-aware Defect Prediction

Panichella

Alexandru

Panichella

et al. 2016

Proceedings of the Genetic and Evolutionary Computation Conference 2016

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Research has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each others' code changes. Developers are unlikely to devote the same effort to inspect each software artifact predicted to contain defects, since the effort varies with the artifacts' size (cost) and the number of defects it exhibits (effectiveness). We propose to use Genetic Algorithms (GAs) for training prediction models to maximize their cost-effectiveness. We evaluate the approach on two well-known models, Regression Tree and Generalized Linear Model, and predict defects between multiple releases of six open source projects. Our results show that regression models trained by GAs significantly outperform their traditional counterparts, improving the cost-effectiveness by up to 240%. Often the top 10% of predicted lines of code contain up to twice as many defects. ABSTRACTResearch has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each others' code changes. Developers are unlikely to devote the same effort to inspect each software artifact predicted to contain defects, since the effort varies with the artifacts' size (cost) and the number of defects it exhibits (effectiveness). We propose to use Genetic Algorithms (GAs) for training prediction models to maximize their cost-effectiveness. We evaluate the approach on two well-known models, Regression Tree and Generalized Linear Model, and predict defects between multiple releases of six open source projects. Our results show that regression models trained by GAs significantly outperform their traditional counterparts, improving the cost-effectiveness by up to 240%. Often the top 10% of predicted lines of code contain up to twice as many defects.

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Class level fault prediction using software clustering

Cited by 53 publications

References 29 publications

Exploring community structure of software Call Graph and its applications in class cohesion measurement

Exploring community structure of software Call Graph and its applications in class cohesion measurement

A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

A Search-based Training Algorithm for Cost-aware Defect Prediction

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