It is a deficiency in a software product that causes it to perform unexpectedly (McDonald, Musson, & Smith, 2007). It produces either an incorrect, or unexpected result, and behaves in unintended ways. Keywords: systematic literature review, software defect prediction, software defect prediction methods, NASA MDP datasetsġ INTRODUCTION A software defect is a fault, error, or failure in a software (Naik and Tripathy 2008). The results of this research also identified three frameworks that are highly cited and therefore influential in the software defect prediction field. Researchers proposed some techniques for improving the accuracy of machine learning classifier for software defect prediction by ensembling some machine learning methods, by using boosting algorithm, by adding feature selection and by using parameter optimization for some classifiers. From the nineteen methods, seven most applied methods in software defect prediction are identified. Nineteen different methods have been applied to predict software defects. In addition, 64.79% of the research studies used public datasets and 35.21% of the research studies used private datasets. 77.46% of the research studies are related to classification methods, 14.08% of the studies focused on estimation methods, and 1.41% of the studies concerned on clustering and association methods. The total distribution of defect prediction methods is as follows. Analysis of the selected primary studies revealed that current software defect prediction research focuses on five topics and trends: estimation, association, classification, clustering and dataset analysis. Systematic literature review is defined as a process of identifying, assessing, and interpreting all available research evidence with the purpose to provide answers for specific research questions. This literature review has been undertaken as a systematic literature review. Based on the defined inclusion and exclusion criteria, 71 software defect prediction studies published between January 2000 and December 2013 were remained and selected to be investigated further. This literature review aims to identify and analyze the research trends, datasets, methods and frameworks used in software defect prediction research betweeen 20. Many software defect prediction datasets, methods and frameworks are published disparate and complex, thus a comprehensive picture of the current state of defect prediction research that exists is missing. Penerapan Java Dynamic Compilation pada Metode Java Customized Class Loader untuk Memperbaharui Perangkat Lunak pada Saat Runtime dengan Lebih Efisien Tory Ariyanto, Romi Satria Wahono and PurwantoĪ Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks Romi Satria Wahono Faculty of Computer Science, Dian Nuswantoro University Ībstract: Recent studies of software defect prediction typically produce datasets, methods and frameworks which allow software engineers to focus on development activities in terms of defect-prone code, thereby improving software quality and making better use of resources. Resampling Logistic Regression untuk Penanganan Ketidakseimbangan Class pada Prediksi Cacat Software Harsih Rianto and Romi Satria WahonoĮstimasi Proyek Pengembangan Perangkat Lunak Dengan Fuzzy Use Case Points Muhadi Hariyanto and Romi Satria Wahono
Penerapan Teknik Ensemble untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software Aries Saifudin and Romi Satria WahonoĪbsolute Correlation Weighted Naïve Bayes for Software Defect Prediction Rizky Tri Asmono, Romi Satria Wahono and Abdul Syukur Suprapedi (Lembaga Ilmu Pengetahuan Indonesia) Romi Satria Wahono, M.Eng, Ph.D (Universitas Dian Nuswantoro)Ī Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks Romi Satria WahonoĪ Systematic Literature Review of Requirements Engineering for Self-Adaptive Systems Slamet Sucipto and Romi Satria Wahono Nanna Suryana Herman (Universiti Teknikal Malaysia) Affandy, Ph.D (Universitas Dian Nuswantoro) Hendro Subagyo, M.Eng (Lembaga Ilmu Pengetahuan Indonesia) Yudho Giri Sucahyo, Ph.D (Universitas Indonesia) Dana Indra Sensuse, Ph.D (Universitas Indonesia) Dr. Printed in IndonesiaĮditor-in-Chief: Romi Satria Wahono, M.Eng, Ph.D Editor: Mansyur, S.Kom Mulyana, S.Kom Reviewer: Prof. 1, April 2015Ĭopyright © 2015 IlmuKomputer.Com All rights reserved.