Software defect prediction using tree-based ensembles

Aljamaan, Hamoud; Alazba, Amal

doi:10.1145/3416508.3417114

Cited by 50 publications

(35 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [69], researchers compared the prediction performance of individual decision tree classifiers to tree-based ensembles, including Extra Trees (ET), XGBoost, Cat-Boost, Gradient Boosting, Hist Gradient Boosting, AdaBoost and RF. They discovered that the decision tree classifier performed worst for all defect datasets, whereas Tree-based ensembles showed a consistent higher prediction performance, except the Ada ensemble.…”

Section: Rq5 : With Which Technique (S) Is the Proposed Ensemble Methods Is Compared?mentioning

confidence: 99%

“…In [69], researchers analysed and compared the prediction performance of seven Tree-based ensembles in defect prediction. Two bagging ensembles, i.e., random forest and Extra Trees, and five boosting ensembles, i.e., Ada boost, Gradient Boosting, Hist Gradient Boosting, XGBoost and CatBoost, were employed.…”

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

confidence: 99%

“…[68] used F-measure and AUC only to evaluate performance. In [69], AUC and accuracy were employed as a measure to compare performance. In [70,74], researchers used Accuracy, precision, Recall and fmeasure to compare the performance of their proposed models.…”

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

confidence: 99%

“…In [59], KEEL software was used to perform classification on KEEL datasets. In [69], researchers applied scikit-learn, CatBoost and XGBoost libraries to build tree-based ensembles.…”

Section: Rq3 : What Are the Most Commonly Employed Tools To Implement Ensemble Learning Techniques?mentioning

confidence: 99%

“…In [48], 30 projects were collectively obtained from NASA, Softlab, Relink, AEEEM, and PROMISE repository. In [69], 11 datasets of NASA software projects were employed; in [72], 5 datasets of NASA software projects were utilized. In [70], researchers selected five projects with a homogeneous set of metrics from http://openscience.us and www.Promisedata.org.…”

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

confidence: 99%

See 4 more Smart Citations

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Rq5 : With Which Technique (S) Is the Proposed Ensemble Methods Is Compared?mentioning

confidence: 99%

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%

“…In [59], KEEL software was used to perform classification on KEEL datasets. In [69], researchers applied scikit-learn, CatBoost and XGBoost libraries to build tree-based ensembles.…”

Section: Rq3 : What Are the Most Commonly Employed Tools To Implement Ensemble Learning Techniques?mentioning

confidence: 99%

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

confidence: 99%

See 3 more Smart Citations

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

show abstract

Software Change Prediction with Homogeneous Ensemble Learners on Large Scale Open-Source Systems

Khanna

Shanmugapriya

Mehra

2021

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Customizability, extensive community support and ease of availability have led to the popularity of Open-Source Software (OSS) systems. However, maintenance of these systems is a challenge especially as they become considerably large and complex with time. One possible method of ensuring effective quality in large scale OSS is the adoption of software change prediction models. These models aid in identifying change-prone parts in the early stages of software development, which can then be effectively managed by software practitioners. This study extensively evaluates eight Homogeneous Ensemble Learners (HEL) for developing software change prediction models on five large scale OSS datasets. HEL, which integrate the outputs of several learners of the same type are known to generate improved results than other non-ensemble classifiers. The study also statistically compares the results of the models developed by HEL with ten nonensemble classifiers. We further assess the change in performance of HEL for developing software change prediction models by substituting their default base learners with other classifiers. The results of the study support the use of HEL for developing software change prediction models and indicate Random Forest as the best HEL for the purpose.

show abstract

An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product

Saheed

Longe

Baba

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Software defect prediction using tree-based ensembles

Cited by 50 publications

References 59 publications

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Software Change Prediction with Homogeneous Ensemble Learners on Large Scale Open-Source Systems

An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product

Contact Info

Product

Resources

About