###### Abstract  
The 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. c 2021 11663. 14 May 2021 9 10 July 2021 9 10 July 2021 9 10 July 2021 9 10 July 2021 9 10 July 2021 9 10 July 2021 9 10 July 2021 9 10 July 2021 9 10 July 2021 11 116663. 10.0000-00002-0002  
life cycle process, can degrade. Therefore, one solution of this problem is to first detect drift in the currently processed data and to therefore handle the detected drift before further prediction. Only a couple of studies [10; 12; 13; 14] have examined CD in SDP. [PERSON] et al. were the first to investigate the presence of CD in SDP. They proved that software-defect data are prone to CD. The recent work by [PERSON] et al. [13] used an existing Drift Detection Method (DDM) [15] to detect CD and its related impacts on SDP performance. They evaluated the statistical significance of this method using the chi-square test with Yates continuity correction. [PERSON] et al. [14] recently assessed the existence of CD and its impact on performance of SDP. These works create the foundation and are the motivational factors for discovering and handling concept drift in software-defect prediction. Therefore, further work is required to better detect CD points and to adapt the detected CD in SDP. To the best of our knowledge, none of them used the paired learner (PL) to discover concept drift in SDP. In this paper, we present a dynamic approach based on paired learner to detect and adapt to the concept drift in software-defect prediction. Dynamic behavior occurs as the learned experience from the processed modules is used to predict the presence of drift in unseen modules. We investigate the presence of sudden and gradual drifts in software-defect data. The presented approach first divides the validation dataset into assorted separate subsets using a partitioning technique, and then, trains one of the learners using modules in all subsets exercised previously and the other learner from the modules in the recent subset only. Afterwards, both learners are used to disjointedly predict on unseen data modules; then, the difference in predictions of the two learners is compared to a threshold value for the purpose of detecting drift. If drift is detected, the first learner is updated to handle the CD. The following are the contributions of this study:  
1. We present a dynamic approach based on paired learner to detect and adapt to CD in SDP. 2. An exhaustive assessment of the presented method for several defect datasets collected from different open source data repositories is presented. We evaluate the presented method in terms of the improvement in detection of concept drift points using the prediction model in SDP. 3.