Early Size Estimation using Machine Learning
- Resource Type
- Conference
- Authors
- Manisha; Rishi, Rahul
- Source
- 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom) Computing for Sustainable Global Development (INDIACom), 2021 8th International Conference on. :757-762 Mar, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Maximum likelihood estimation
Computational modeling
Machine learning
Predictive models
Size measurement
Market research
Software
Early Size Estimation Effort Estimation
Machine Learning
Performance Measures
- Language
The process of size estimation of a project is one of the key activities that forms the indispensable part of any project development process. This broadly involves the evaluation of the number of codes and packages that can only be analyzed once the project is completed. The presented paper aims at the size estimation through predictive analysis using Machine Learning (ML) architecture to offer best possible size and effort estimation to complete a project. The article also evaluates the existing research in the field of size estimation discussing the research trends inspired with the ML techniques as size prediction models. Finally, it presents comprehensive survey of the possible prospective of ML approaches for size estimation along with the detailed information in terms of strengths, limitations, evaluation metrics implemented for evaluation along with the datasets involved in the studies.