Sangwan, O.P. (2017) Software Effort Estimation Using Machine Learning Techniques. 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, Noida, India, 12-13 January 2017, 92-98.

has been cited by the following article:

• TITLE: A Survey on Software Cost Estimation Techniques

  AUTHORS: Sai Mohan Reddy Chirra, Hassan Reza

  KEYWORDS: Software Cost Estimation, Classical SCE Models, Algorithmic Models, Non-Algorithmic Models, Learning-Oriented Cost Estimation Techniques

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.12 No.6, June 30, 2019

  ABSTRACT: The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.