A Genetic Programming Approach to Software Cost Modeling and Estimation
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Summary
This paper investigates the utilization of Genetic Programming as a method to facilitate better software cost modeling and estimation by generating representative rules through which the usefulness of various project characteristics as explanatory variables, and ultimately as predictors of development effort is investigated.
- Type
- article
- Published
- 2010-01-01
- Cited by
- 1
- References
- 19
- OpenAlex
- https://openalex.org/W46530861
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:30886498
Keywords
Computer science, Genetic programming, Estimation, Software, Programming language
References
- Software cost estimation using artificial neural networks with inputs selection
- Software Cost Estimation with COCOMO II
- Genetic Programming: On the Programming of Computers by Means of Natural Selection
- Resource-limited genetic programming: the dynamic approach
- Comparison of artificial neural network and regression models for estimating software development effort
- Identification of fuzzy models of software cost estimation
- Optimization of analogy weights by genetic algorithm for software effort estimation
- Can genetic programming improve software effort estimation? A comparative evaluation
- A General Empirical Solution to the Macro Software Sizing and Estimating Problem
- Cost estimation of software development: improving the COCOMO model using a genetic algorithm approach
- A Systematic Review of Software Development Cost Estimation Studies
- Measuring application development productivity
- Genetic Algorithms
- Dynamic Maximum Tree Depth
- Software Engineering Economics
- Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets
- Genetic Algorithms Data Structures Evolution Programs
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