TY  - JOUR
T1  - RESEDA: Software REliability Model SElection using DAta-driven Software Reliability Prediction
AU  - Lee, Nakwon 
AU  - Ryu, Duksan 
AU  - Cho, Ilhoon 
AU  - Song, Jeakun 
AU  - Baik, Jongmoon 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.6.443
KW  - software reliability model
KW  - reliability model selection
KW  - data-driven reliability prediction
KW  - machine learning
KW  - meta-heuristic search
AB  - To solve the model generalization problem, i.e., there is no single best model that fits all types of software failure data, model selection techniques and data-driven reliability prediction techniques have been proposed. However, model selection techniques still wrongly select some failure data, and the reliability metrics that the data-driven techniques can observe are limited. In this paper, we propose a software reliability model selection technique using data-driven reliability prediction to improve the prediction accuracy with obtaining reliability metrics. The proposed approach decides either selection or data-driven for target failure data using a classifier generated from historical failure data sets. If data-driven is chosen, the proposed approach builds an augmented failure data using the prediction results of the data-driven technique and selects a model for the augmented data. The proposed approach shows a 21% lower median value of the mean error of prediction compared to the best technique for comparison. With the improved reliability prediction accuracy using the proposed approach, the higher software reliability is achieved.