Abstract
Project Failure is the key target undergoing in today’s scenario which is observed through the software project administrators. An ambiguity of the estimation is the major cause of this dilemma. According to tech project managers, project failure is the most common concern today. This issue arises as a result of measurement inaccuracy. When software's size and significance increase, its complexity makes it even more problematic to reliably estimate the cost of growth. This was the predicament of previous years. Software cost estimation (SCE) is the utmost significant subject in software design in modern times. Real calculation necessitates expense and commitment considerations when developing applications utilizing algorithmic or Artificial Intelligence (AI) techniques. In this paper, numerous predefined approaches for SCE are illuminated and their characteristics will be considered. This paper briefs numerous sessions of SCE models and methods. This study also claims that no particular procedure is superlative for all circumstances, software measures and metrics are also needed to enhance efficiency. This research paper focuses on Artificial Intelligence-based software cost estimation approaches, their implementation domain, and strategies for calculating software cost estimation. There are two kinds of SCE approaches: parametric/algorithmic models and non-parametric/non-algorithmic models. One of the most common algorithmic SCE models is the Constructive Cost Model (COCOMO). Basic COCOMO is ideal for fast, quick, and uneven estimates of the effort expected to manufacture software, but its accuracy is restricted because of the lack of variables to account for cost-driven differences. Non-algorithmic approaches for overcoming the shortcomings of algorithmic models include expert Judgment, machine learning (ML), and price to gain. In checked studies, ML models were implemented to address the shortcomings of parametric estimation models.
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Srivastava, P., Srivastava, N., Agarwal, R., Singh, P. (2022). A Systematic Literature Review on Software Development Estimation Techniques. In: Luhach, A.K., Poonia, R.C., Gao, XZ., Singh Jat, D. (eds) Second International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1235. Springer, Singapore. https://doi.org/10.1007/978-981-16-4641-6_11
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