Predicting fault-prone modules with case-based reasoning

Khoshgoftaar, Taghi M.; Ganesan, K.; Allen, Edward B.; Ross, Fletcher D.; Munikoti, Rama; Goel, Nishith; Nandi, Amit

doi:10.1109/issre.1997.630845

Cited by 46 publications

(13 citation statements)

References 15 publications

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“…However, it is also desirable that a single accuracy measure be used, as opposed to two. The reason is best exemplified by the results in [43]. Here the authors were comparing nonparametric discriminant analysis with case-based reasoning classification.…”

Section: Eqnmentioning

confidence: 99%

The prediction of faulty classes using object-oriented design metrics

Emam¹,

Melo²,

Machado

2001

Journal of Systems and Software

274

200

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Section: Eqnmentioning

confidence: 99%

The prediction of faulty classes using object-oriented design metrics

Emam¹,

Melo²,

Machado

2001

Journal of Systems and Software

274

200

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show abstract

“…This results in a binary variable. Another approach that is used is to dichotomize a continuous dependent variable around the median [29], or even on the third quartile [74].…”

Section: Measurement Of Dependent Variablesmentioning

confidence: 99%

Methodology for Validating Software Product Metrics

Emam

Schneidewind²

2002

Encyclopedia of Software Engineering

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A large number of software product metrics have been proposed in software engineering. We use the term metric here to be consistent with prevailing international standards. Product metrics quantitatively characterize some aspect of the structure of a software product, such as a requirements specification, a design, or source code. They are also commonly collectively known as complexity metrics . While many of these metrics are based on good ideas about what is important to measure in software to capture its complexity, it is still necessary to systematically validate them. Two types of validation are recognized internal and external. Internal validation is a theoretical exercise that ensures that the metric is a proper numerical characterization of the property it claims to measure. Demonstrating that a metric measures what it purports to measure is a form of theoretical validation. Typically, one defines the properties of the attribute that is to be measured, for example, the properties of module coupling. Then one demonstrates analytically that the product metric satisfies these properties. External validation involves empirically demonstrating that the product nature is associated which is important external metter or an improvement is shown above. Internal and external validation are also commonly referred to as theoretical and empirical validation respectively This article presents a comprehensive methodology for validating software product metrics. Our focus will be limited to empirical validation.

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“…Experimental observation may suggest that a module presently undergoing development is said to be fault-prone if it has comparable properties which are measured as a result of software metrics on the basis of similar module that has been developed or released earlier in the same environment [31]. Thus, historical information helps us predict fault-proneness.…”

Section: Related Workmentioning

confidence: 99%

Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor

Armah¹,

Luo²,

Qin³

et al. 2016

LNSE

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Abstract-Empirical studies on software defect prediction models have come up with various predictors. In this study we examined variable regularized factors in conjunction with Logistic regression. Our work was built on eight public NASA datasets commonly used in this field. We used one of the datasets for our learning classification out of which we selected the regularization factor with the best predictor model; we then used the same regularization factor to classify the other seven datasets. Our proposed algorithm Variant Variable Regularized Logistic Regression (VVRLR) and modified VVRLR; were then used in the following metrics to measure the effectiveness of our predictor model: accuracy, precision, recall and F-Measure for each dataset. We measured above metrics using three Weka models, namely: BayesianLogisticRegression, NaiveBayes and Simple Logistic and then compared these results with VVRLR. VRLR and modified VVRLR outperformed the weka algorithms per our metric measurements. The VVRLR produced the best accuracy of 100.00%, and an average accuracy of 91.65 %; we had an individual highest precision of 100.00%, highest individual recall of 100.00% and F-measure of 100.00% as the overall best with an average value of 76.41% was recorded by VVRLR for some datasets used in our experiments. Our proposed modified VVRLR and variant VVRLR algorithms for F-measures outperformed the three weka algorithms.Index Terms-F-measure, precision, recall, variant variable regularized logistic regression.

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Predicting fault-prone modules with case-based reasoning

Cited by 46 publications

References 15 publications

The prediction of faulty classes using object-oriented design metrics

The prediction of faulty classes using object-oriented design metrics

Methodology for Validating Software Product Metrics

Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor

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