Scientific Programming

Review Article

Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Table 5

List of reviewed primary studies.
IdAim of research workRef.YearType ofpublication
[L01]Prediction of software defect vulnerability level with the textual corpus[30]2021Conference
[L02]Deploying ensemble learning for software defect prediction[31]2021Journal
[L19]Software defect predictions with techniques[32]2020Conference
[L28]To use text analytics on apache projects for bug severity prediction[33]2020Journal
[L29]Questioning and answering approach to bug prediction[34]2020Journal
[L30]To use the machine in the prediction of software defect[35]2020Journal
[L31]Weighted extreme learning machine approach to predict software defect[36]2020Journal
[L36]Heterogeneous ensemble classification approach to defect prediction[37]2020Journal
[L12]To use a feature selection strategy for improved defect prediction[38]2019Journal
[L16]Virtual classification approach to software defect prediction[39]2019Conference
[L20]Code smell prediction using an extreme learning machine[40]2019Conference
[L27]To use CNN and random forest for the classification of bug severity[41]2019Journal
[L33]Approaching software defect prediction through feature selection[38]2019Journal
[L35]Predicting software bugs in closed-source projects[42]2019Journal
[L45]Cluster and hybrid feature selection approach to defect prediction[43]2019Conference
[L50]Software defect prediction using an unsupervised approach[44]2019Conference
[L11]Using bug report classification for incorporate and textual field knowledge[45]2018Conference
[L38]Identifying impacts of imbalanced ensemble learning methods for cross projects[46]2018Journal
[L43]Semisupervised approach to defect prediction in cross-project and within-project[47]2018Conference
[L46]Heterogeneous piling and SMOTE approach to ensemble defect prediction[48]2018Conference
[L51]Deployment of the ensemble for class imbalance in defect prediction[49]2018Journal
[L10]Corpus vulnerability description approach towards defect prediction[50]2017Conference
[L17]Using feature-dependent Naïve Bayes approach[51]2017Journal
[L26]Adoption of predictive analytics for code smell prediction[52]2017Journal
[L37]Assessment of bug severity in cross-project while in training candidates[53]2017Journal
[L42]SMOTE and ensemble approach to defect severity prediction[54]2017Conference
[L44]Unsupervised machine learning approach to defect prediction[55]2017Conference
[L04]Diversity selection approach to defect prediction using ensemble[56]2016Conference
[L09]Using a semisupervised approach on imbalance set for defect prediction[57]2016Conference
[L14]Machine learning techniques for defect prediction in android software[58]2016Journal
[L25]Machine learning and text mining approach to bug severity classification[59]2016Journal
[L48]Predictive analytics on software project report[60]2016Journal
[L49]Diversity selection and ensemble approach to defect prediction[61]2016Conference
[L08]Using a dictionary of critical terms for predicting software bug severity[62]2015Conference
[L03]Using a dictionary of known terms for defect prediction[63]2014Conference
[L05]Mining software repository for defect prediction[64]2014Book
[L07]Neural network approach to predictive analytics[65]2014Conference
[L15]Improving VAB-SVM prediction for defect prediction in cross-project[66]2014Conference
[L22]Evaluating learners performance on imbalanced dataset for defect prediction[67]2014Journal
[L23]Defect prediction using machine learning techniques[68]2014Journal
[L24]Comparing statistical and machine learning methods for faulty modules[69]2014Journal
[L47]Data mining and multi-layer perceptron approach to defect prediction[70]2014Journal
[L06]Iterative and noniterative feature engineering approach for software defect prediction[71]2013Journal
[L18]Trying decision tree approach to software defect prediction[72]2013Journal
[L39]Deploying supervised and unsupervised learning approaches to defect prediction[73]2013Journal
[L13]Machine learning techniques adoption for bug severity prediction[74]2012Conference
[L21]Two-level data preprocessing approach to defect prediction[75]2012Conference
[L32]To identify the impacts of different classifiers to predict software defect[76]2012Journal
[L34]Ensemble learning approach to improve software defect prediction[77]2012Journal
[L40]Feature selection approach to defect prediction in software[78]2012Conference
[L41]An AHP-based evaluation method for ensemble defect prediction[79]2011Journal
[L42]Enhanced random forest (extRF) approach in IOT-based application processing environment using a business process management and improvement concept[80]2022Journal