Using Coding-Based Ensemble Learning to Improve Software Defect Prediction

Sun, Zhongbin; Song, Qinbao; Zhu, Xiaoyan

doi:10.1109/tsmcc.2012.2226152

Cited by 171 publications

(87 citation statements)

References 52 publications

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“…Sun et al [12], addressed the problem of data skew by using multiclass classification methods with different types of code schema such as (one-against-one, random correcting code, and one-against-all). Sun et al used several types of ensemble learning methods such as (Boosting, Bagging and Random Forest) that integrated with previous coding schemas.…”

Section: Related Workmentioning

confidence: 99%

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

Abdou¹,

Darwish²

2018

IJCA

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Recently, early prediction of software defects using the machine learning techniques has attracted more attention of researchers due to its importance in producing a successful software. On the other side, it reduces the cost of software development and facilitates procedures to identify the reasons for determining the percentage of defect-prone software in future. There is no conclusive evidence for specific types of machine learning that will be more efficient and accurate to predict of software defects. However, some of the previous related work proposes the ensemble learning techniques as a more accurate alternative. This paper introduces the resample technique with three types of ensemble learners; Boosting, Bagging and Rotation Forest, using eight of base learner tested on seven types of benchmark datasets provided in the PROMISE repository. Results indicate that accuracy has been improved using ensemble techniques more than single leaners especially in conjunction with Rotation Forest with the resample technique in most of the algorithms used in the experimental results.

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Section: Related Workmentioning

confidence: 99%

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

Abdou¹,

Darwish²

2018

IJCA

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“…We designed different experiments to evaluate the proposed CSDL algorithm, and compared it with the defect prediction algorithms which are proposed in recent years, including Cost-sensitive Discriminative Dictionary Learning (CDDL) [14], SVM [5], NB [6] ， Coding based Ensemble Learning (CEL) [16] and Cost-Sensitive Boosting Neural Network (CSBNN) [17].…”

Section: Experimental Designmentioning

confidence: 99%

“…However, the sampling strategy will change the distribution of the source data, affecting the effect of the prediction. Sun et al [16] proposed a coding-based integrated learning approach that transforms the class-imbalanced defect data into multi-class balanced data to avoid the class-imbalance problem by specific coding strategies. Jiang et al [17] pointed out that the performance of the defect prediction model is affected by different misclassification cost.…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction Using Dictionary Learning

Wan¹,

Wu²,

Cheng³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-With the popularization of software version control system and defect tracking tools, large amounts of software development data is recorded. How to effectively use these data to improve the quality of software development, has become a hot topic in recent years. Software defect prediction technology can take full advantage of the historical data to build predictive models and automatically detect defective modules for efficient software test to improve the quality of a software system. But the class-imbalanced data makes the prediction model classifying a modules as a defective-free one easily, while the misclassification of defective modules generally incurs much higher cost risk than the misclassification of defective-free ones. To resolve this problem, we propose a cost-sensitive software defect prediction method using dictionary learning. It iteratively optimizes the classifier parameters and the dictionary atoms, to ensure that the extracted features (sparse representation) are optimal for the trained classifier; Moreover, we take the different misclassification costs into account, increasing the punishment on misclassification defective modules in the procedure of dictionary learning, making classification inclining to classify a module as a defective one. Experimental results on the 10 classimbalanced data sets of NASA show that our method is more effective than other methods.

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“…It aims to detect the defect proneness of new software modules via learning from defect data. So far, many efficient software defect prediction approaches [1][2][3][4][5][6] have been proposed, but they are usually confined to within project defect prediction (WPDP). WPDP works well if sufficient data is available to train a defect prediction model.…”

Section: Introductionmentioning

confidence: 99%

Combing Data Filter and Data Sampling for Cross-Company Defect Prediction: An Empricial Study

Zhang³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, the CC data has the highly imbalanced nature between the defectiveprone and non-defective classes, which will degrade the performance of CCDP. To address such issues, this paper proposes an approach, in which data sampling is combined with data filter, to overcome these problems. Data sampling seeks a more balanced dataset through the addition or removal of instances, while data filter is a process of filtering out the irrelevant CC data so that the performance of CCDP models can be improved. We employ two data filtering methods called NN filter and DBSCAN filter combined with SMOTE (Synthetic Minority Oversampling Technique) and RUS (Random UnderSampling). Eight different approaches would be produced when combing these four techniques: 1-NN filter performed prior to RUS; 2-NN filter performed after RUS; 3-NN filter performed prior to SMOTE; 4-NN filter performed after SMOTE; 5-DBSCAN filter performed prior to RUS; 6-DBSCAN filter performed after RUS; 7-DBSCAN filter performed prior to SMOTE; 8-DBSCAN filter performed after SMOTE. The empirical study was carried out on 15 publicly available project datasets. The experimental results demonstrate that NN filter performed prior to RUS (Approach 1) performs better than the other seven approaches.

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Using Coding-Based Ensemble Learning to Improve Software Defect Prediction

Cited by 171 publications

References 52 publications

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

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

Software Defect Prediction Using Dictionary Learning

Combing Data Filter and Data Sampling for Cross-Company Defect Prediction: An Empricial Study

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