@inproceedings{ff79a9b36f9b4560996de6e1a83f0702,
title = "Software Defect Prediction using Oversampling Algorithm: A-SUWO",
abstract = "To predict software defects required prediction models using defect data and software metrics called Software Defect Prediction (SDP). Some learning algorithms are used to identify possible decay to program modules, thus affecting the optimum utilization and allocation of resources. However, the accuracy of classification is influenced by the robustness and quality of data. The class imbalance in the data will affect the accuracy of predicting defect or not defect. To improve the accuracy of the Software Defect Prediction (SDP) model, we propose a new framework using A-SUWO to handle the class imbalance. Data with a balanced class will be classified to produce an accurate class. The dataset used is NASA. Using A-SUWO shows that the proposed framework can predict defects effectively. The highest accuracy is shown by A-SUWO-Random Forest.",
keywords = "A-SUWO, classification, data balancing, software defect prediction)",
author = "Shabrina Choirunnisa and Biandina Meidyani and Siti Rochimah",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018 ; Conference date: 09-10-2018 Through 11-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/EECCIS.2018.8692874",
language = "English",
series = "2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "337--341",
booktitle = "2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018",
address = "United States",
}