Current Proceedings on Technology
Yazarlar: Zeinab Abbasi Khalifehlou, Farhad Soleimanian Gharehchopogh
Konular:-
Anahtar Kelimeler:Software Cost Estimation,Expert Systems,Data Mining,COCOMO,Artificial Intelligence
Özet: Software Cost Estimation (SCE) is one of complex problems in software engineering area. So, a lot of attention has been allocated to solving this problem. Thus the correct estimate of the cost and effort required for software companies and company executives and companies is very significant. In required effort prediction, estimated size and cost of the project with high accuracy is still vast challenge for projects masters. In 1981, had been developed algorithmic models which called Constructive Cost Model (COCOMO) by Boehm. These models were used for required effort prediction. Since, COCOMO 81 is not suitable model for responding new requirements in recent years, so COCOMO II model in 2000 published. COCOMO II is most transparent model among algorithmic models (COCOMO family) for estimates the cost of software. Today’s, there are very smart methods more efficient and effective algorithmic COCOMO methods, so many research and review the data mining techniques to estimate the true cost of software is done. This paper a survey of the literature of application of data mining techniques in SCE collected form software databases as well as international conferences and journals. Data mining techniques of expert systems were selected according to a set of criteria of work relevancy their internal architecture techniques were reviewed. The techniques are Fuzzy Decision Trees (FDTs), Artificial Neural Networks (ANN), Bayesian Networks (BN) classifiers and etc. Our work addresses the issue of software effort prediction via these methods using historical project data samples. A classification considering the main goal of the methods has been made. For each category, a discussion of the suitability of SCE data mining techniques is presented in required effort and cost prediction. The results of survey conducted indicate that in most cases, these methods very efficacious compare with algorithmic COCOMO models.