Gangwar, Arvind KumarKumar, SandeepMishra, AlokSoftware Engineering2024-07-052024-07-05202132076-341710.3390/app111466632-s2.0-85111559250https://doi.org/10.3390/app11146663https://hdl.handle.net/20.500.14411/1996Kumar, Dr Sandeep/0000-0003-0747-6776; Kumar, Sandeep/0000-0002-3250-4866; Mishra, Alok/0000-0003-1275-2050; Kumar, Sandeep/0000-0001-9633-407XThe early and accurate prediction of defects helps in testing software and therefore leads to an overall higher-quality product. Due to drift in software defect data, prediction model performances may degrade over time. Very few earlier works have investigated the significance of concept drift (CD) in software-defect prediction (SDP). Their results have shown that CD is present in software defect data and tha it has a significant impact on the performance of defect prediction. Motivated from this observation, this paper presents a paired learner-based drift detection and adaptation approach in SDP that dynamically adapts the varying concepts by updating one of the learners in pair. For a given defect dataset, a subset of data modules is analyzed at a time by both learners based on their learning experience from the past. A difference in accuracies of the two is used to detect drift in the data. We perform an evaluation of the presented study using defect datasets collected from the SEACraft and PROMISE data repositories. The experimentation results show that the presented approach successfully detects the concept drift points and performs better compared to existing methods, as is evident from the comparative analysis performed using various performance parameters such as number of drift points, ROC-AUC score, accuracy, and statistical analysis using Wilcoxon signed rank test.eninfo:eu-repo/semantics/openAccessconcept driftnaive Bayesrandom forestsoftware defect predictionsoftware quality assuranceArticleQ21114WOS:000675935700001