Estimate Programmatic Effort using the Traditional COCOMO Model and Neural Networks
Estimation models in software engineering are used to predict some important and future features for software project such as effort estimation for developing software projects. Failures of software are mainly due to the faulty project management practices. software project effort estimati...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
Mosul University
2013-03-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.mosuljournals.com/article_163464_5aa93b18240498ef964605044a7bfa6d.pdf |
| Summary: | Estimation models in software engineering are used to predict some important and future features for software project such as effort estimation for developing software projects. Failures of software are mainly due to the faulty project management practices. software project effort estimation is an important step in the process of software management of large projects. Continuous changing in software project makes effort estimation more challenging. The main objective of this paper is find a model to get a more accurate estimation. In this paper we used the Intermediate COCOMO model which is categorized as the best of traditional Techniques in Algorithmic effort estimation methods. also we used an Artificial approaches which is presented in (FFNN,CNN,ENN,RBFN) because of the Ability of ANN(Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers)which makes it as a potential tool for estimation. This paper presents a performance analysis of ANNs used in effort estimation. We create and simulate this networks by MATLAB11 NNTool depending on NASA aerospace dataset which contains a features of 60 software project and its actual effort. the result of estimation in this paper shows that the neural networks in general enhance the performance of traditional COCOMO and we proved that the ENN was the best network between neural networks and the CNN was the next best network and the COCOMO have the worst between the used methods. |
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| ISSN: | 1815-4816 2311-7990 |