Data quality issues in software fault prediction: a systematic literature review

Bhandari, Kirti; Kumar, Kuldeep; Sangal, Amrit Lal

doi:10.1007/s10462-022-10371-6

Cited by 13 publications

(5 citation statements)

References 200 publications

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“…The class overlap problem 3 refers to the existence of some samples in an unbalanced dataset as valid samples in multiple categories, which makes the prediction boundary vague and affects the model prediction accuracy 4 . In the current SDP study, the class overlap problem is mainly related to noise cleaning.…”

Section: Related Workmentioning

confidence: 99%

Neighbor cleaning learning based cost‐sensitive ensemble learning approach for software defect prediction

Li,

Su,

Zhao

2024

Concurrency and Computation

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SummaryThe class imbalance problem in software defect prediction datasets leads to prediction results that are biased toward the majority class, and the class overlap problem leads to fuzzy boundaries for classification decisions, both of which affect the model's prediction performance on the dataset. A neighbor cleaning learning (NCL) is an effective technique for defect prediction. To solve the class overlap problem and class imbalance problem, the NCL‐based cost‐sensitive ensemble learning approach for software defect prediction (NCL_CSEL) model is proposed. First, the bootstrap resampled data are trained using the base classifier. Subsequently, multiple classifiers are integrated by a static ensemble to obtain the final classification results. As the base classifier, the Adaptive Boosting (AdaBoost) classifier combining NCL and cost‐sensitive learning is proposed, and the class overlap problem and class imbalance problem are solved by balancing the proportion of overlap sample removal in NCL and the size of the cost factor in cost‐sensitive learning. Specifically, the NCL algorithm is used to initialize the sample weights, while the cost‐sensitive method is employed to update the sample weights. Experiments based on the NASA dataset and AEEEM dataset show that the defect prediction model can improve the bal value by approximately 7% and the AUC value by 9.5% when the NCL algorithm is added. NCL_CSEL can effectively solve the class imbalance problem and significantly improve the prediction performance compared with existing methods for solving the class imbalance problem.

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Section: Related Workmentioning

confidence: 99%

Neighbor cleaning learning based cost‐sensitive ensemble learning approach for software defect prediction

Li,

Su,

Zhao

2024

Concurrency and Computation

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“…A comprehensive systematic literature review 38 focusing on data quality challenges in software fault prediction is discussed. The paper delves into the critical aspects related to the quality of data used for training and evaluating software fault prediction models.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

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“…They found that two imbalanced variants of the bagging classifier performed better than other techniques, even in cross-project settings [45]. Bhandari et al (2023) conducted a systematic literature review on data quality issues in software fault prediction datasets. They analyzed 145 primary studies and identified various data quality issues, such as class imbalance and dimensionality, suggesting the need for further research in this area [46].…”

Section: Cross-project Prediction For Software Fault Predictionmentioning

confidence: 99%

“…Bhandari et al (2023) conducted a systematic literature review on data quality issues in software fault prediction datasets. They analyzed 145 primary studies and identified various data quality issues, such as class imbalance and dimensionality, suggesting the need for further research in this area [46]. Rathi et al (2023) performed an extensive evaluation of different combinations of data sampling and feature selection techniques to improve software fault prediction models.…”

Section: Cross-project Prediction For Software Fault Predictionmentioning

confidence: 99%

Software Fault Prediction Using Cross-Project Analysis: A Study on Class Imbalance and Model Generalization

Kaliraj,

Kishoore,

Sivakumar

2024

IEEE Access

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Software fault prediction is a critical aspect of software engineering aimed at improving software quality and reliability. However, it faces significant challenges, including the class imbalance issue in fault data and the need for robust predictive models that generalize well across different projects. In this research, we delve into these challenges and investigate the impact of class imbalance and model generalization on software fault prediction using cross-project analysis. Our study addresses three primary research questions: Firstly, we examine the critical issue of class imbalance in fault prediction, which poses a significant hurdle to accurate model performance. Through extensive experimentation with various classifiers on diverse datasets from different software projects, we highlight the variations in classifier performance and the necessity of addressing class imbalance for reliable predictions. Secondly, we evaluate the reliability of cross-project prediction, aiming to understand how effectively predictive models trained on one project can generalize to predict faults in other projects. We demonstrate the importance of training with datasets sharing similar characteristics with the target project for achieving reliable cross-project prediction. Thirdly, we analyze the impact of increasing training samples from different projects on prediction accuracy, emphasizing the benefits of utilizing cross-project analysis to enhance predictive model performance. In addition to addressing these research questions, we provide a comprehensive comparison of classifier performance metrics, including accuracy, precision, recall, and F1 Score. Our findings not only shed light on the challenges and opportunities in software fault prediction but also emphasize the importance of considering class imbalance and model generalization for developing robust and reliable fault prediction models. This research contributes to advancing the field by providing insights into effective modeling approaches and highlighting the motivation behind addressing these challenges.

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Data quality issues in software fault prediction: a systematic literature review

Cited by 13 publications

References 200 publications

Neighbor cleaning learning based cost‐sensitive ensemble learning approach for software defect prediction

Neighbor cleaning learning based cost‐sensitive ensemble learning approach for software defect prediction

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Software Fault Prediction Using Cross-Project Analysis: A Study on Class Imbalance and Model Generalization

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