TY - GEN
T1 - Software Defect Prediction using Oversampling Algorithm
T2 - 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018
AU - Choirunnisa, Shabrina
AU - Meidyani, Biandina
AU - Rochimah, Siti
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
KW - A-SUWO
KW - classification
KW - data balancing
KW - software defect prediction)
UR - http://www.scopus.com/inward/record.url?scp=85065076454&partnerID=8YFLogxK
U2 - 10.1109/EECCIS.2018.8692874
DO - 10.1109/EECCIS.2018.8692874
M3 - Conference contribution
AN - SCOPUS:85065076454
T3 - 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018
SP - 337
EP - 341
BT - 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 October 2018 through 11 October 2018
ER -