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. The total distribution of defect prediction methods is as follows % 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. 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. This literature review aims to identify and analyze the research trends, datasets, methods and frameworks used in software defect prediction research betweeen 2000 and 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. 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. Suprapedi (Lembaga Ilmu Pengetahuan Indonesia) Romi Satria Wahono, M.Eng, Ph.D (Universitas Dian Nuswantoro) Copyright 2015 IlmuKomputer.Comģ Contents SURVEY PAPERS A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks 1-16 Romi Satria Wahono A Systematic Literature Review of Requirements Engineering for Self-Adaptive Systems Slamet Sucipto and Romi Satria Wahono REGULAR PAPERS Penerapan Teknik Ensemble untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software Aries Saifudin and Romi Satria Wahono Absolute Correlation Weighted Naïve Bayes for Software Defect Prediction Rizky Tri Asmono, Romi Satria Wahono and Abdul Syukur Resampling Logistic Regression untuk Penanganan Ketidakseimbangan Class pada Prediksi Cacat Software Harsih Rianto and Romi Satria Wahono Estimasi Proyek Pengembangan Perangkat Lunak Dengan Fuzzy Use Case Points Muhadi Hariyanto and Romi Satria Wahono 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 Purwanto Copyright 2015 IlmuKomputer.ComĤ A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks Romi Satria Wahono Faculty of Computer Science, Dian Nuswantoro University Abstract: 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. 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 Indonesia Copyright 2015 IlmuKomputer.ComĢ Editorial Board Editor-in-Chief: Romi Satria Wahono, M.Eng, Ph.D Editor: Mansyur, S.Kom Mulyana, S.Kom Reviewer: Prof. 1 class CD Sistem ATM ControllerValidasiKartu ControllerValidasiPIN access Login do access UIKotakKartu do UIMenuPIN UIMenuMengecekSaldo do ControllerMengecekSaldo Account has-a SistemATM has-a show UIMenuUtama is-a is-a UIMenuMengambilUang do ControllerMengambilUang access access Balance access is-a has-a has-a is-a UIMengirimUang do ControllerMengirimUang UIKotakUang UIKotakKuitansi UIMenuLogout do ControllerLogout Copyright 2015 IlmuKomputer.Com All rights reserved.
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