Software Defect Prediction and Localization with Attention-Based Models and Ensemble Learning

Abstract For the traditional ensemble learning algorithm of software defect prediction, the base predictor exists the problem that too many parameters are difficult to optimize, resulting in the optimized performance of the model unable to be obtained. An ensemble learning algorithm for software defect prediction that is proposed by using the improved sparrow search algorithm to optimize the extreme learning machine, which divided into three parts. Firstly, the improved sparrow search algorithm (ISSA) is proposed to improve the optimization ability and convergence speed, and the performance of the improved sparrow search algorithm is tested by using eight benchmark test functions. Secondly, ISSA is used to optimize extreme learning machine (ISSA-ELM) to improve the prediction ability. Finally, the optimized ensemble learning algorithm (ISSA-ELM-Bagging) is presented in the Bagging algorithm which improve the prediction performance of ELM in software defect datasets. Experiments are carried out in six groups of software defect datasets. The experimental results show that ISSA-ELM-Bagging ensemble learning algorithm is significantly better than the other four comparison algorithms under the six evaluation indexes of Precision, Recall, F-measure, MCC, Accuracy and G-mean, which has better stability and generalization ability.

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Software Defect Prediction Based on Ensemble Learning

Proceedings of the 2019 2nd International Conference on Data Science and Information Technology ◽

10.1145/3352411.3352412 ◽

2019 ◽

Cited By ~ 4

Author(s):

Ran Li ◽

Lijuan Zhou ◽

Shudong Zhang ◽

Hui Liu ◽

Xiangyang Huang ◽

...

Keyword(s):

Ensemble Learning ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defect

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Handling Imbalanced Data using Ensemble Learning in Software Defect Prediction

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) ◽

10.1109/confluence47617.2020.9058124 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ruchika Malhotra ◽

Juhi Jain

Keyword(s):

Ensemble Learning ◽

Imbalanced Data ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defect

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Omni-Ensemble Learning (OEL): Utilizing Over-Bagging, Static and Dynamic Ensemble Selection Approaches for Software Defect Prediction

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213018500240 ◽

2018 ◽

Vol 27 (06) ◽

pp. 1850024 ◽

Cited By ~ 4

Author(s):

Reza Mousavi ◽

Mahdi Eftekhari ◽

Farhad Rahdari

Keyword(s):

Ensemble Learning ◽

Class Imbalance ◽

Defect Prediction ◽

Learning Approaches ◽

Software Defect Prediction ◽

Multiple Classifier Systems ◽

Classifier Systems ◽

Software Defect ◽

Ensemble Selection ◽

Dynamic Ensemble Selection

Machine learning methods in software engineering are becoming increasingly important as they can improve quality and testing efficiency by constructing models to predict defects in software modules. The existing datasets for software defect prediction suffer from an imbalance of class distribution which makes the learning problem in such a task harder. In this paper, we propose a novel approach by integrating Over-Bagging, static and dynamic ensemble selection strategies. The proposed method utilizes most of ensemble learning approaches called Omni-Ensemble Learning (OEL). This approach exploits a new Over-Bagging method for class imbalance learning in which the effect of three different methods of assigning weight to training samples is investigated. The proposed method first specifies the best classifiers along with their combiner for all test samples through Genetic Algorithm as the static ensemble selection approach. Then, a subset of the selected classifiers is chosen for each test sample as the dynamic ensemble selection. Our experiments confirm that the proposed OEL can provide better overall performance (in terms of G-mean, balance, and AUC measures) comparing with other six related works and six multiple classifier systems over seven NASA datasets. We generally recommend OEL to improve the performance of software defect prediction and the similar problem based on these experimental results.

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