May 1, 2018

Comparison of machine learning methods for software project effort estimation

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Resumen

Accurate estimation of development effort is highly crucial for software project planning since it affects project delivery time and project costs. In this work, Machine Learning based software effort estimation methods are compared and their error rates are documented. Decision Tree, Naive Bayes and Multiple Regression models were inspected and they were trained and tested using data obtained from a local software house.

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Yurdakurban et al. (Tue,) studied this question.

synapsesocial.com/papers/6a12c89d83732aa7db9e4eb6 https://doi.org/https://doi.org/10.1109/siu.2018.8404495

Also Consider

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1Measures for Excellence: Reliable Software on Time, Within Budget1991 · 235 citations
2Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest2014 · 75 citations
3Cost models for future software life cycle processes: COCOMO 2.01995 · 652 citations
4A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models1997 · 285 citations
5Artificial intelligence: a modern approach1995 · 22,249 citations

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