An empirical study of ensemble techniques for software fault prediction

Rathore, Santosh Singh; Kumar, Sandeep

doi:10.1007/s10489-020-01935-6

Cited by 38 publications

(23 citation statements)

References 94 publications

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“…The results showed that no single ensemble method outperformed others in all datasets. However, the [72] 2020 Springer Link Conference International Conference on Computational Science and Its Applications (ICCSA 2020) [73] 2021 Springer Link Journal Applied Intelligence [74] 2021 Springer Link Journal Neural Computing and Applications researchers observed that the ensembles of a few ranking techniques performed better than the ensembles of many ranking techniques. In [59], a review of state-of-the-art ensemble techniques for class imbalance problems was conducted.…”

Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Sofmentioning

confidence: 99%

“…In [73], researchers empirically accessed the performance of seven ensemble techniques, namely, Dagging, Decorate, Grading, MultiBoostAB, RealAdaBoost, Rotation Forest, and Ensemble Selection. Naive Bayes, logistic regression, and J48 (decision tree) were used as base learners.…”

Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Sofmentioning

confidence: 99%

“…In [72], the AUC, Accuracy and F-measure are employed to measure the performance of the proposed defect prediction model. In [73], five performance measures namely precision, recall, AUC (area under ROC curve), specificity, and G-means were used.…”

Section: Rq2 : Which Evaluation Criterion Is Used To Measure the Perfmentioning

confidence: 99%

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Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

et al. 2021

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Recent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions that cover the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that feature selection and data sampling were important pre-processing steps that improve the performance of ensemble classifiers.

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Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Sofmentioning

confidence: 99%

Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Sofmentioning

confidence: 99%

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Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

et al. 2021

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“…Liu et al [ 55 ] propose an adaptive electrical period partition algorithm for open-circuit fault detection. Software fault prediction by ensemble techniques is investigated by [ 56 ]. In [ 57 ], the RF id is used to build a distributed energy system.…”

Section: Related Workmentioning

confidence: 99%

“…At each instant n, the action probability vector pi(n) is updated by the linear learning algorithm given in equation ( 13) if the chosen action ai(k) is rewarded by the environment, and it is updated according to equation ( 14) if the chosen action is penalized [104]. [11], [12], [13] Global problem [26], [27], [28] Healthcare [32], [33], [34], [35], [36], [41], [98], [37], [39], [40], [42], [43], [45], [46], [47], [48], [49], [50], [51], Industrial [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62] Network [63], [67], [68], [69], [99], [100] Physics [71], [72] Text processing …”

Section: Learning Automatamentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Cross‐version defect prediction using threshold‐based active learning

Mei

Liu

et al. 2023

J Software Evolu Process

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Because defects in software modules (e.g., classes) might lead to product failure and financial loss, software defect prediction enables us to better understand and control software quality. Software development is a dynamic evolutionary process that may result in data distributions (e.g., defect characteristics) varying from version to version. In this case, effective cross‐version defect prediction (CVDP) is not easy to achieve. In this paper, we aim to investigate whether the defect prediction method of the threshold‐based active learning (TAL) can tackle the problem of the different data distribution between successive versions. Our TAL method includes two stages. At the active learning stage, a committee of investigated metrics is constructed to vote on the unlabeled modules of the current version. We pick up the unlabeled module with the median of voting scores to domain experts. The domain experts test and label the selected unlabeled module. Then, we merge the selected labeled module and the remaining modules with pseudo‐labels from the current version into the labeled modules of the prior version to form enhanced training data. Based on the training data, we derive the metric thresholds used for the next iteration. At the defect prediction stage, the iterations stop when a predefined threshold is reached. Finally, we use the cutoff threshold of voting scores, that is, 50%, to predict the defect‐prone of the remaining unlabeled modules. We evaluate the TAL method on 31 versions of 10 projects with three prevalent performance indicators. The results show that TAL outperforms the baseline methods, including three variations methods, two common supervised methods, and the state‐of‐the‐art method Hybrid Active Learning and Kernel PCA (HALKP). The results indicate that TAL can effectively address the different data distribution between successive versions. Furthermore, to keep the cost of extensive testing low in practice, selecting 5% of candidate modules from the current version is sufficient for TAL to achieve a good performance of defect prediction.

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An empirical study of ensemble techniques for software fault prediction

Cited by 38 publications

References 94 publications

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

A New Random Forest Algorithm Based on Learning Automata

Cross‐version defect prediction using threshold‐based active learning

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