- THE COCOMO MODEL RESULTED FROM THE FACT DRIVERS
- THE COCOMO MODEL RESULTED FROM THE FACT SOFTWARE
- THE COCOMO MODEL RESULTED FROM THE FACT SERIES
Sadiq M, Mariyam F, Ali A, Khan S, Tripathi P (2011) Prediction of software project effort using fuzzy logic.
Fuzzy Sets Syst 145:141–163ĭiaz NG, Martin CL, Chavoya A (2013) A comparative study of two fuzzy logic models for software development effort estimation. Xu Z, Khoshgoftaar TM (2004) Identification of fuzzy models of software cost estimation. In: Proceedings of the first USA-Morocco workshop on information technology, pp 1–9
Idri A, Abran A, Khoshgoftaar TM (2003) Computational intelligence in empirical software engineering. In: Proceedings of the 7th international conference on fuzzy theory and technology, pp 1–4 Idri A, Abran AL (2000) COCOMO cost model using fuzzy logic. In: Proceedings of IEEE international conference on fuzzy system, pp 331–337 Commun ACM 7(3):77–84įei Z, Liu X (1992) f-COCOMO-fuzzy constructive cost model in software engineering. Zadeh LA (1994) Fuzzy logic, neural network and soft computing. Man KF, Tang KS, Kwong S (2001) Genetic algorithms: concepts and designs. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modelling and control. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with fuzzy logic controller. George JK, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Addison Wesley, New Yorkīoehm BW (1981) Software engineering economics. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Huang X et al (2006) A soft computing framework for software effort estimation. In: Damiani E, Jain LC (eds) Computational intelligence in software engineering. Saliu MO, Ahmed M (2004) Soft computing based effort prediction systems-a survey. Handbook of software engineering, Hong Kong Polytechnic University, pp 1–14. Leung H, Fan Z (2002) Software cost estimation. A significant improvement in %MMRE and Pred (25%) justifies the suitability of genetic algorithms for optimizing proposed fuzzy model. The improvement in performance of proposed optimized model is measured in terms of mean magnitude of relative error (MMRE) and Pred (25%). The proposed model is tested on COCOMO NASA dataset and COCOMO NASA2 dataset using MATLAB.
Selection of parameters characterising fuzzy sets in proposed fuzzy model is further optimized using genetic algorithms. The fuzzy model handles imprecise and ambiguous definition of input ranges of cost drivers. The fuzzy approach is implemented to design a fuzzy model for each cost driver. Thus in the current research, implementation of non algorithmic modelling is carried out using soft computing techniques like fuzzy logic and genetic algorithms.
THE COCOMO MODEL RESULTED FROM THE FACT DRIVERS
Intermediate COCOMO suffers from a problem of imprecise definition of cost drivers resulting in inaccurate estimations. Algorithmic techniques use mathematical equations however, in case of imprecise information these techniques are overpowered by non algorithmic techniques. Algorithmic as well as non algorithmic techniques are used to estimate cost and effort. The major concern in this process is estimation of cost and effort.
THE COCOMO MODEL RESULTED FROM THE FACT SERIES
Software development process is a series of planned activities undertaken to design a software product.