Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Abstract

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.

Assessment of Smart Home Assistants as an IoT

“…In spite of the architecture for SPA being intricate as well as possessing explicit characteristics, all its systems have analogous functions and share conjoint features. Particularly, the elementary architecture of blockchain and NLP in SPA comprises of elements, which include cloud-based processing and exchangeable interaction features with other systems including, the voice-based intelligent personal agent and smart speaker (as will be discussed later) [9]. Imperatively, SPA is an internet-based technology that utilizes updates, which flourish with new internet services.…”

Integrating Blockchain Technology with Internet of things to Efficiency

“…The result also demonstrates that each of the techniques used by company to integrate IoT with blockchain chain faces unique challenges and opportunities that they should attempt to address [23]. The challenges that are unique to each of the architectures are regarded as the main factor that contributes to the differences in the levels of accuracy [21]. As a result, it is appropriate that research studies on IoT and blockchain focus in addressing the loophole s that might compromise the functionality of blockchain and IoT devices and systems as suggested in [41].…”

Improving IoT Privacy, Data Protection and Security Concerns

“…Blockchain is emerging as a powerful industry that supports the operation of many firms and businesses across different sectors [10]. The emergence of blockchain as an innovative and disruptive technology has evidently helped revolutionize the information, communication and transactions [39]. Currently, there are several attempts and research studies aimed at integrating blockchain technology with IoT.…”