An Empirical Analysis on Effective Fault Prediction Model Developed Using Ensemble Methods

Kumar, Lov; Rath, Santanu Kumar; Sureka, Ashish

doi:10.1109/compsac.2017.53

Cited by 8 publications

(10 citation statements)

References 10 publications

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“…In [5], an empirical analysis of feature selection and ensemble learning was performed. Researchers used three heterogeneous ensemble models-BTE, MVE, and NDTFwith two linear combinations (best in training and voting) and one nonlinear combination (DTR) rule.…”

Section: Rq1 : Which Ensemble Learning Techniques Are Applied For Software Defect Prediction?mentioning

confidence: 99%

“…In another study [50], performance measure recall, precision, accuracy, and F-value were used to evaluate and compare results. Other performance measures that were frequently applied include accuracy in [5][6], [8], [44], [49], [52], and [64]; PofB20 by [10]; and the Kolmogorov-Smirnov (K-S) test in [11]. In [41], the results of Auto-Weka and Auto-PBIL-Ens models were compared using mean error rates.…”

Section: Rq2 : Which Evaluation Criterion Is Used To Measure the Performance Of Ensemble Learners?mentioning

confidence: 99%

“…To further analyze and compare results, researchers also utilized different comparison and measurement techniques, such as box plot analysis [11] of the AAE and ARE measure to identify variations in the samples; W/D/L [7] (win/ draw/loss) to compare the number of datasets on which their proposed method performed the best, same or worse than other methods; the Wilcoxon signed-rank test [4][5], [9], [11] to compare the classification technique and selected source of metrics to identify the best performer [4], [11]; the two-tailed Friedman's test to measure the difference between prediction models and Cliff's delta [9] to determine the effect of size.…”

Section: Rq2 : Which Evaluation Criterion Is Used To Measure the Performance Of Ensemble Learners?mentioning

confidence: 99%

“…Fig 6 shows the distribution of studies over dataset repositories used in selected primary studies. Most of the selected primary studies used a publicly available dataset from PROMISE repository [1], [4][5][6][7], [11][12], [42], [50], [51], [54], [55], [57], [63], [64], [68], [73]. This repository contains datasets with three types of software metrics, namely, CK Object-Oriented metrics, Halstead/McCabe procedural code metrics, and some other static code metrics.…”

Section: Rq4 : Which Datasets Are Used To Analyze the Performance Of Ensemble Learning Techniques?mentioning

confidence: 99%

“…They selected 11 releases of 6 projects, which contained more than 300 software modules. Researchers in [5] compared models on 45 releases of 18 software projects, namely, Ant, arc, berek, camel, e-learning, ivy, jedit, kalkulator, log4j, lucene, pdftranslator, prop, redactor, serapion, synapse, termoproject, velocity, and xereces. In [1], researchers analyzed the performance of prediction models on 4 software projects: Ant, Jedit, Tomcat and Xalan.…”

Section: Rq4 : Which Datasets Are Used To Analyze the Performance Of Ensemble Learning Techniques?mentioning

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 Software Defect Prediction?mentioning

confidence: 99%

Section: Rq2 : Which Evaluation Criterion Is Used To Measure the Performance Of Ensemble Learners?mentioning

confidence: 99%

Section: Rq2 : Which Evaluation Criterion Is Used To Measure the Performance Of Ensemble Learners?mentioning

confidence: 99%

Section: Rq4 : Which Datasets Are Used To Analyze the Performance Of Ensemble Learning Techniques?mentioning

confidence: 99%

Section: Rq4 : Which Datasets Are Used To Analyze the Performance Of Ensemble Learning Techniques?mentioning

confidence: 99%

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Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

et al. 2021

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Prediction of software fault-prone classes using an unsupervised hybrid SOM algorithm

Viji

Rajkumar

Duraisamy³

2018

Cluster Comput

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Ensemble Classifiers in Software Defect Prediction: A Systematic Literature Review

Olivares-Galindo,

Sánchez-García,

Barrientos-Martínez

et al. 2023

2023 11th International Conference in Software Engineering Research and Innovation (CONISOFT)

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An Empirical Analysis on Effective Fault Prediction Model Developed Using Ensemble Methods

Cited by 8 publications

References 10 publications

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Prediction of software fault-prone classes using an unsupervised hybrid SOM algorithm

Ensemble Classifiers in Software Defect Prediction: A Systematic Literature Review

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