Objectives: The present study is undertaken to survey the Software development cost estimation techniques. This study will provide guidelines and for researchers and practitioners of software engineering. Methods/Analysis: The study was undertaken by planning, conducting and reporting the literature review (LR) for the years 1991-2016. Findings: The study revealed that several SDCE models have been introduced. The reason for the evolution of software cost estimation models may be the changing nature of software complexity, i.e., one cannot exactly predict the cost for the whole project. Not only conventional empirical and quantitative methods but several data mining and machine learning techniques are also used for improved results. However, it is revealed that from quantitative to empirical all SDCE models can be used alone or hybrid with robust ML or DM techniques to estimate the software development exertion.
Keywords
COCOMO, Data Mining Techniques, Machine Learning Techniques, Software Development Cost Estimation.
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