Concept Drift in Software Defect Prediction: A Method for Detecting and Handling the Drift

Abstract

Software Defect Prediction (SDP) is crucial towards software quality assurance in software engineering. SDP analyzes the software metrics data for timely prediction of defect prone software modules. Prediction process is automated by constructing defect prediction classification models using machine learning techniques. These models are trained using metrics data from historical projects of similar types. Based on the learned experience, models are used to predict defect prone modules in currently tested software. These models perform well if the concept is stationary in a dynamic software development environment. But their performance degrades unexpectedly in the presence of change in concept (Concept Drift). Therefore, concept drift (CD) detection is an important activity for improving the overall accuracy of the prediction model. Previous studies on SDP have shown that CD may occur in software defect data and the used defect prediction model may require to be updated to deal with CD. This phenomenon of handling the CD is known as CD adaptation. It is observed that still efforts need to be done in this direction in the SDP domain. In this paper, we have proposed a pair of paired learners (PoPL) approach for handling CD in SDP. We combined the drift detection capabilities of two independent paired learners and used the paired learner (PL) with the best performance in recent time for next prediction. We experimented on various publicly available software defect datasets garnered from public data repositories. Experimentation results showed that our proposed approach performed better than the existing similar works and the base PL model based on various performance measures.

Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting

“…The studies of Gangwar et al [12,13] introduced an approach known as a pair of paired learners called "PoPL" within the realm of SDP, aimed at addressing CD. The primary objective is to improve the prediction performance beyond what the most successful paired learner methods have achieved in recent times.…”

“…In a related study, Kabir et al [62] investigated the performance of class-rebalancing techniques to observe the performance of drift detection and reduction for SDP. The PoPL approach developed by Gangwar et al [12,13] could be one of the considerable methods for accessing CD for CVDP. We kept this work as one of the our future works.…”

“…Changes in data probability, including probability distributions, can lead to inaccurate results, and even a well-trained prediction model will become outdated in the face of such drift, as noted by Dong et al [8]. Furthermore, other researchers [9][10][11][12][13] have reported that the distribution changes among the versions in chronological defect datasets. Findings from streaming data analytics verify that if the historical data change over time, the prediction models become outdated [4].…”

“…Even a prediction model that is well trained can become obsolete in the presence of such drift, as highlighted by Dong et al [8]. Furthermore, other researchers [9][10][11][12][13] have reported that the distribution changes among the versions in chronological defect datasets. Empirical evidence from the field of streaming data analytics corroborates that, as temporal shifts occur in historical data, prediction models experience obsolescence [4].…”

Software Defect Prediction Using an Intelligent Ensemble-Based Model

“…SDP is the art of leveraging historical data and machine learning (ML) techniques to forecast and identify potential defects in software systems before the testing phase [9]. It investigates the complex set of software metrics such as code complexity, size, and historical defect data to build models capable of gauging the likelihood of defects [10].…”