An Empirical Investigation of Predicting Fault Count, Fix Cost and Effort Using Software Metrics

Shatnawi, Raed; Li, Wei

doi:10.14569/ijacsa.2016.070264

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…13 No. 4 November 2021 https://doi.org/10.20895/infotel.v13i4.726 approach to classifying software defects is to use regression models such as Logistics Regression (LR) [18] and Multiple Linear Regression (MLR) [19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression

2021

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The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the highest accuracy. The combination of the proposed methods first is the resampling process using oversampling, under-sampling, or hybrid methods. The second process uses the classification method, namely the Support Vector Machine (SVM) algorithm and the Logistic Regression (LR) algorithm. The proposed, tested model uses five NASA MDP data sets with the same number attributes of 37. Based on the t-test, the < = 0.0344 < 0.05 and the > = 3.1524 > 2.7765 which indicates that the combination of the proposed methods is suitable for classifying imbalanced class. The performance of the classification algorithm has also improved with the use of the resampling process. The average increase in AUC values using the resampling in the SVM algorithm is 17.19%, and the LR algorithm is at 7.26% compared to without the resampling process. Combining the three resampling methods with the SVM algorithm and the LR algorithm shows that the best combining method is the oversampling method with the SVM algorithm to software defects prediction in imbalanced class with an average accuracy value of 84.02% and AUC 91.65%.

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Section: Introductionmentioning

confidence: 99%

Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression

2021

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Software Defect Prediction Using Principal Component Analysis and Naïve Bayes Algorithm

Dhamayanthi

Lavanya

2019

Proceedings of International Conference on Computational Intelligence and Data Engineering

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Machine Learning Based Prediction of Complex Bugs in Source Code

Uqaili

Ahsan

2019

IAJIT

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During software development and maintenance phases, the fixing of severe bugs are mostly very challenging and needs more efforts to fix them on a priority basis. Several research works have been performed using software metrics and predict fault-prone software module. In this paper, we propose an approach to categorize different types of bugs according to their severity and priority basis and then use them to label software metrics’ data. Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules. Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.

show abstract

An Empirical Investigation of Predicting Fault Count, Fix Cost and Effort Using Software Metrics

Cited by 3 publications

References 30 publications

Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression

Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression

Software Defect Prediction Using Principal Component Analysis and Naïve Bayes Algorithm

Machine Learning Based Prediction of Complex Bugs in Source Code

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