- author = "Mayank Jha and Richa Jha",
- title = "Comparing the Effort Estimated By Different Models",
- booktitle = "2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)",
- year = "2020",
- pages = "1148--1154",
- address = "Coimbatore, India",
- month = "6-7 " # mar,
- keywords = "genetic algorithms, genetic programming, SBSE, software effort estimation, software testing, Cobb-Douglas function, Neuro Fuzzy Approach, Genetic programming method, STLC",
- isbn13 = "978-1-7281-5198-4",
-
DOI = "
doi:10.1109/ICACCS48705.2020.9074165",
- size = "7 pages",
- abstract = "Management of project software starts with a collection of activities referred to as project planning procedure. A companys team must decide the work to be done, the resources to be reorganized and a time from beginning of the calculation until project starts. Following completion of these activities, the program team will set up a set of projects that will assign program development tasks, identify key milestones, identify responsibilities for each task and identify related dependencies among participants that may have a significant impact on progress. There is usually no full precise estimation process, but in this research we have tried to find the best programming methods to find the best estimate of programming. Effort estimation is one of greatest objection of STLC. It is platform for planning, estimating and preparing effort for project. This paper demonstrates model with a purpose of depicting bias variation and an accuracy of the technology of an enterprise test attempt estimates concluding the function of Cobb-Douglas (CDF), Neuro fuzzy approach, and Genetic methods. The purpose of this review is to present an analysis of principles to minimize software costs and to explain how these concepts are applied to general system divisions. We deliver simple algorithms namely-Cobb Douglas, Genetic Algorithms, and Adaptive Neuro Fuzzy Approach to decide which algorithm is best suited to finding the best estimates as accurate as possible. The best outcomes they have been found in Neuro Fuzzy Approach. The Neuro Fuzzy has highest accuracy to be found, but the Genetic Algorithm was better than Fuzzy Logic, the worst compared to Cobb Douglas and Genetic Algorithms.",
-
notes = "Citicorp Services India Limited, Pune, India.
Also known as \cite{9074165}",
