Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction

Tong, Haonan; Wang, Shihai; Li, Guangling

doi:10.3390/app10228059

Cited by 15 publications

(20 citation statements)

References 93 publications

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“…Through the evaluation results, it was found that the proposed model surpassed several defect prediction models when applied to eight pairs of open-source Java projects. Tong et al [13] presented a novel approach to address the issue of class imbalance in SDP. The effectiveness of the proposed method was extensively evaluated on diverse datasets and compared with existing techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Upon examination of earlier research on SBP, it was observed that the majority of proposed techniques neglect the problem of class imbalance. Research that specifically dealt with the issue of class imbalance and provided solutions as referenced in [13], [15] emphasizes the crucial role of data balancing methods in enhancing SBP accuracy. Hence, the main aim of this study is to mitigate the problem of class imbalance and enhance the efficacy of the proposed models.…”

Section: Related Workmentioning

confidence: 99%

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Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Khleel,

Nehéz

2023

IJEECS

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<span>The process of identifying software bugs is of paramount importance as it ensures software reliability and facilitates maintenance activities. The quality improvement process of software relies heavily on software bug prediction (SBP). In SBP, the task of accurately identifying defective source code poses a significant challenge. Numerous of machine learning (ML) models has been developed specifically to address this challenge in SBP. Nonetheless, the class imbalance issue restricts the potential of these models to predict software bugs accurately. This issue poses a significant hindrance to the efficiency of these models, leading to imbalanced false-positive and false-negative outcomes. Previous studies have paid limited attention to addressing the challenge of class imbalance in SBP. This study aims to fill this research gap by employing a combination of two recurrent neural networks (RNNs), namely <a name="_Hlk141433135"></a>long-short-term memory (LSTM) and gated recurrent unit (GRU), along with an undersampling method (near miss) to effectively tackle this issue. Experiments have been conducted on publicly available benchmark datasets, considering both class-level and file-level metrics. The experimental results lead to the conclusion that our models outperform others and the combination of RNNs models with undersampling methods leads to improved bug prediction performance, particularly for datasets with imbalanced class distributions.</span>

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Khleel,

Nehéz

2023

IJEECS

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“…We manage this problem by modifying the original datasets to increase the realism of the data [36]. The most common methods used to deal with distributions of unbalanced classes are sampling techniques, might be divided into two categories: oversampling techniques and under-sampling techniques [43,44]. Oversampling techniques supplements instances of the minority class to the dataset, while the under-sampling techniques eliminate samples of the majority class for the goal of obtaining a balanced dataset [15].…”

Section: 4class Imbalance and Sampling Techniquesmentioning

confidence: 99%

A Novel Approach for Software Defect Prediction using CNN and GRU Based on SMOTE Tomek Method

Khleel

Nehéz

2022

Preprint

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Software defect prediction (SDP) plays an important role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to the methods that use historical defect data to build the relationship between software metrics and software defects. Several prediction models such as machine learning (ML), deep learning (DL) have been developed and adopted to recognize defect in software modules and many methodologies and frameworks have been presented. One of the most difficult problems that these models face in binary classification is the classes imbalance. When the distribution of classes is unbalanced, the accuracy may be high, but the model cannot recognize data instances in the minority class, this will lead to weak classifications. So far, few research have been done in the previous studies that address the problem of class imbalance in SDP. To address the class imbalance problem, we propose a novel SDP approach based on convolutional neural network (CNN) and gated recurrent unit (GRU) combined with synthetic minority oversampling technique plus Tomek link (SMOTE Tomek). To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets which obtained from the PROMISE repository and the experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), mean square error (MSE). The average accuracy of the proposed models on the original datasets were 89% for CNN and 87% for GRU, while the average accuracy of the proposed models on the balanced datasets were 94% for CNN and 92% for GRU. The results showed that the proposed models on the balanced datasets improves the average accuracy by 5% for both models compared to original datasets. This indicates the positive effects of combining ML techniques with data balancing methods on the performance of defect prediction regarding datasets with imbalanced class distributions.

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“…Defect prediction 18 is an important research field of software engineering. Several approaches have been presented in the literature including ones that estimate the number of defects in a software program, 19 others 20 that find “associations” among defects (i.e., they detect additional problems starting from a defected piece of code), and ones 21 that classify the defect‐proneness of software components (as the ones discussed in this manuscript).…”

Section: Background and Related Workmentioning

confidence: 99%

Predictive maintenance of infrastructure code using “fluid” datasets: An exploratory study on Ansible defect proneness

Quattrocchi

Tamburri

2022

J Software Evolu Process

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This work consolidates and compounds previous investigations in recognizing defects for infrastructure‐as‐code (IaC) scripts using general software development quality metrics with a focus on defect severity but adding to previous work an explorative look at creating datasets, which may boost the predictive power of provided models—we call this notion a fluid dataset. More specifically, we experiment with 50 different metrics harnessing a multiple dataset creation process whereby different versions of the same datasets are rigged with auto‐training facilities for model retraining and redeployment in a DataOps fashion. At this point, with a focus on the Ansible infrastructure code language—as a de facto standard for industrial‐strength infrastructure code—we build defect prediction models and manage to improve on the state of the art by finding an F1 score of 0.52 and a recall of 0.57 using a Naive–Bayes classifier. On the one hand, by improving state‐of‐the‐art defect prediction models using metrics generalizable for different IaC languages, we provide interesting leads for the future of infrastructure‐as‐code. On the other hand, we have barely scratched the surface on the novel approach of fluid‐datasets creation and automated retraining of Machine Learning (ML) defect prediction models, warranting for more research on the same direction in the future.

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Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction

Cited by 15 publications

References 93 publications

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

A Novel Approach for Software Defect Prediction using CNN and GRU Based on SMOTE Tomek Method

Predictive maintenance of infrastructure code using “fluid” datasets: An exploratory study on Ansible defect proneness

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