COMPARATIVE ANALYSIS OF SOFTWARE EFFORT ESTIMATION USING DATA MINING TECHNIQUE AND FEATURE SELECTION
نویسندگان
چکیده
Software development involves several interrelated factors that influence efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time budget control in software development. Effort Estimation or cost/effort can help a company overcome difficulties experienced estimating efforts. This study aims compare Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Decision Tree Random Forest (DTRF) calculate estimated software. Then these five approaches be tested on dataset projects as many 10 projects. So it produce new knowledge about what machine learning non-machine methods are most accurate for business. As well knowing between selection using Particle Swarm Optimization (PSO) attributes without PSO, which one increase accuracy business estimation. The data mining algorithm used optimal effort estimate is with an average RMSE value 1603,024 datasets tested. PSO feature reduce 1552,999. result indicates that, compared original regression linear model, error rate has increased by 3.12% applying
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ژورنال
عنوان ژورنال: JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)
سال: 2021
ISSN: ['2527-4864', '2685-8223']
DOI: https://doi.org/10.33480/jitk.v6i2.1968