Early Prediction of Software Defect using Ensemble Learning: A Comparative Study

Abdou, Ashraf; Darwish, Nagy Ramadan

doi:10.5120/ijca2018917185

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…Abdou et al [16] evaluated EL models and introduced the resample technique with Boosting, Bagging, and Rotation Forest. The study concludes that the accuracy improved using EL approaches more than single classifiers.…”

Section: Related Workmentioning

confidence: 99%

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Al-Fraihat,

Sharrab,

Al-Ghuwairi

et al. 2024

IEEE Access

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Software Bug Prediction (SBP) is an integral process to the software's success that involves predicting software bugs before their occurrence. Detecting software bugs early in the development process enhances software quality, performance, and reduces software costs. The integration of Machine Learning (ML) algorithms has significantly improved software bug prediction accuracy and concurrently reduced costs and resource utilization. Numerous studies have explored the impact of Hyperparameter Optimization on single classifiers, enhancing these models' overall performance in SBP analysis. Ensemble Learning (EL) approaches have also demonstrated increased model accuracy and performance on SBP datasets. This study proposes a novel learning model for predicting software bugs through the utilization of EL and tuning hyperparameters. The results are compared with single hypothesis learning models using the WEKA software. The dataset, collected by the National Aeronautics and Space Administration (NASA) U.S.A., comprises 10,885 instances with 20 attributes, including a classifier for defects in one of their coding projects. The findings indicate that EL models outperform single hypothesis learning models, and the proposed model's accuracy increases after optimization. Furthermore, the accuracy of the proposed model demonstrates improvement following the optimization process. These results underscore the efficacy of ensemble learning, coupled with hyperparameter optimization, as a viable approach for enhancing the predictive capabilities of software bug prediction models.

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Section: Related Workmentioning

confidence: 99%

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Al-Fraihat,

Sharrab,

Al-Ghuwairi

et al. 2024

IEEE Access

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show abstract

“…In (2018), researchers Ashraf Sayed and Naji Ramadan [11] explained that predicting software defects in the advanced stages of the software life cycle, which depends on collective learning, produces successful software. Through their study, the researchers confirm that there is not enough evidence to say that any particular type of machine learning techniques is more effective or accurate in predicting software defects.…”

Section: Related Workmentioning

confidence: 99%

A Survey of Software Systems Anomaly States Prediction Based on Artificial Techniques

Azad Hassan,

Ahmed Saleh

2024

IRJIET

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Predicting software anomaly is essential for improving software quality and saving costs and time associated with software testing and maintenance in advanced stages life of software development. Complex software systems can be made more reliable and field failures reduced by having the ability to anticipate failures before they occur, as the field of forecasting has witnessed. Software defect prediction (SDP) has seen recent developments, such as combining several classification algorithms to create an ensemble or hybrid approach. Many laterals have been conducted in the field of predicting, including predicting anomalies and disruptions in performance and predicting software errors. This paper presents a study and review of the literature on predicting anomalies in software systems, strategies and methods for detecting them, and a brief overview of predicting defects and future trends in forecasting, as the results of the reviews showed. Because ensemble predictors can, in some cases, enhance bug detection performance, many problems have been solved by machine learning techniques as a result of this recent success.

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“…Abdou et al 30 suggested using ensemble learning techniques to detect software defects. They explored three ensemble approaches: Bagging, Boosting, and Rotation Forest, which combine re-sampling techniques.…”

Section: Background/literature Reviewmentioning

confidence: 99%

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

Rao,

Dewangan,

Mishra

et al. 2023

Sci Rep

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Detecting code smells may be highly helpful for reducing maintenance costs and raising source code quality. Code smells facilitate developers or researchers to understand several types of design flaws. Code smells with high severity can cause significant problems for the software and may cause challenges for the system's maintainability. It is quite essential to assess the severity of the code smells detected in software, as it prioritizes refactoring efforts. The class imbalance problem also further enhances the difficulties in code smell severity detection. In this study, four code smell severity datasets (Data class, God class, Feature envy, and Long method) are selected to detect code smell severity. In this work, an effort is made to address the issue of class imbalance, for which, the Synthetic Minority Oversampling Technique (SMOTE) class balancing technique is applied. Each dataset's relevant features are chosen using a feature selection technique based on principal component analysis. The severity of code smells is determined using five machine learning techniques: K-nearest neighbor, Random forest, Decision tree, Multi-layer Perceptron, and Logistic Regression. This study obtained the 0.99 severity accuracy score with the Random forest and Decision tree approach with the Long method code smell. The model's performance is compared based on its accuracy and three other performance measurements (Precision, Recall, and F-measure) to estimate severity classification models. The impact of performance is also compared and presented with and without applying SMOTE. The results obtained in the study are promising and can be beneficial for paving the way for further studies in this area.

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Early Prediction of Software Defect using Ensemble Learning: A Comparative Study

Cited by 11 publications

References 31 publications

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

A Survey of Software Systems Anomaly States Prediction Based on Artificial Techniques

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

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