Early phase software effort estimation model

2016 ◽  
Author(s):  
Priya Agrawal ◽  
Shraddha Kumar
Keyword(s):  
Early Phase ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model

scholarly journals Causal-Effect Analysis using Bayesian LiNGAM Comparing with Correlation Analysis in Function Point Metrics and Effort

2018 ◽  
Vol 3 (2) ◽  
pp. 90-112 ◽  
Author(s):  
Masanari Kondo ◽  
Osamu Mizuno ◽  
Eun-Hye Choi
Keyword(s):  
Causal Effect ◽  
Task Assignment ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Effect Analysis ◽  
Explanatory Variables ◽  
Function Point ◽  
New Findings ◽  
Non Gaussian

Software effort estimation is a critical task for successful software development, which is necessary for appropriately managing software task assignment and schedule and consequently producing high quality software. Function Point (FP) metrics are commonly used for software effort estimation. To build a good effort estimation model, independent explanatory variables corresponding to FP metrics are required to avoid a multicollinearity problem. For this reason, previous studies have tackled analyzing correlation relationships between FP metrics. However, previous results on the relationships have some inconsistencies. To obtain evidences for such inconsistent results and achieve more effective effort estimation, we propose a novel analysis, which investigates causal-effect relationships between FP metrics and effort. We use an advanced linear non-Gaussian acyclic model called BayesLiNGAM for our causal-effect analysis, and compare the correlation relationships with the causal-effect relationships between FP metrics. In this paper, we report several new findings including the most effective FP metric for effort estimation investigated by our analysis using two datasets.

Duplex output software effort estimation model with self-guided interpretation

2018 ◽  
Vol 94 ◽  
pp. 1-13 ◽  
Author(s):  
Solomon Mensah ◽  
Jacky Keung ◽  
Michael Franklin Bosu ◽  
Kwabena Ebo Bennin
Keyword(s):  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model

scholarly journals Fuzzy Optimization Algorithm for Software Cost Estimation

2020 ◽  
Vol 9 (3) ◽  
pp. 1549-1554
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Cost Estimation ◽  
Fuzzy Optimization ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Software Cost ◽  
Clear Cut ◽  
Linguistic Quantifiers

Estimation of a software cost depends on a probabilistic model and thus it doesn't create precise values. In any case, accessibility of good chronicled information combined with a efficient technique can create improved outcomes. This paper, we have displayed a Software Effort Estimation Model utilizing PSO and Fuzzy Logic. Fuzzy sets have been utilized for displaying uncertainty and imprecision in estimation of effort while PSO has been utilized for tuning parameters. This has been seen from the outcomes that Fuzzy-PSO intelligence gives precise outcomes when compared through its different partners. This system relies upon thinking by linguistic quantifiers and fuzzy logic. This kind of model holds well, when the product plans are communicated by absolute or potentially arithmetical data. Along these lines, this projected methodology improves the old style correlation process that doesn't think about clear cut data. In the fuzzy correlation model, fuzzy sets are used to describe both the clear cut and the arithmetical data.

scholarly journals A Pragmatic Cost Effective Model to Enhance the COCOMO Model in Software Estimation

2020 ◽  
Vol 9 (5) ◽  
pp. 1745-1748
Keyword(s):  
Genetic Algorithm ◽  
Cost Estimation ◽  
Research Work ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Beneficial Result ◽  
Cocomo Model ◽  
The Cost

This research work is aimed at to provide effective cost estimation methodology emphasize on cost effort and time . This paper summarizes the cost effort estimation of most conventionally used models like organic and semi-detached models using an improved version of genetic algorithm that enhances an empirical methodology to reduce the cost factor and time factor in software projects. Constructive cost model(Cocomo model) is broadly used for the fruitful valuation of cost estimation which is based on KLOC method(thousands of lines of code).This method yields beneficial result in case of lines of code method but lacks in terms of concept and logics. The same is estimated directly and is computed using the function point analysis. In the software development lifecycle, the software cost effort estimation is the most demanding process. The accuracy of the estimate in choosing the estimation model is an essential factor. Such conventional software effort estimation techniques fail to compute the accuracy of effort estimation and it is not up to the mark. So here, we tend to propose the cost reduction in the software projects by using the improved version of the known genetic algorithm.

Sign in / Sign up

Export Citation Format

Share Document