Loading...
Please wait, while we are loading the content...
An Early Stage Effort Estimation from Enhanced use Case Point Analysis using Adaptive Neuro Fuzzy Inference System
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rao, Ch. Prasada Sripada Rama Sree |
| Copyright Year | 2018 |
| Abstract | In the software development life cycle (SDLC), communication and planning phases are very crucial to develop the software product successfully. In Communication phase, requirements elicitation is done and in planning phase, effort estimation is done. Software project is termed to be successful if it is delivered to the customer within schedule, within budget, and with high quality. The previous reports clearly show that around 60-70% of the projects have failed due to over budget and over schedule. Software effort estimation in the early stage is very essential for the software managers to go for a better business case. In the business case, there exists a contract between the customer and manager based on cost, functionality, delivery deadline and quality constraints. One of the extreme approaches which have been widely used in the past two decades for the prediction of size, effort and cost is Use Case Points (UCP). Use case points are calculated from the Use Case diagrams to estimate the software effort in the early stages of SDLC. An improved version of UCP available in the literature is Enhanced Use Case Points. In this paper, a novel method of predicting the software effort from the Enhanced Use Case Points using Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed. The anticipated model was assessed based on VAF, MMRE, MMER, MSE and PRED using two datasets available. Software Development Effort estimated using the proposed approach is compared with the Karner’s Method. For all performance measures, the proposed approach produced more accurate results. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.ijert.org/research/an-early-stage-effort-estimation-from-enhanced-use-case-point-analysis-using-adaptive-neuro-fuzzy-inference-system-IJERTCONV4IS34022.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
