Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Radek Silhavy"'
Publikováno v:
IEEE Access, Vol 12, Pp 67170-67188 (2024)
This study presents a comprehensive analysis of enhancing software effort estimation accuracy using a Self-Organizing Migration Algorithm (SOMA)-optimized Constructive Cost Model (COCOMO). By conducting a comparative study of traditional COCOMO model
Externí odkaz:
https://doaj.org/article/d150b8f23db44ad2a37ca8f9fa4124fb
Autor:
Petr Silhavy, Radek Silhavy
Publikováno v:
IEEE Access, Vol 11, Pp 126335-126351 (2023)
This study embarks on an in-depth analysis of the performance of various kernel functions, namely uniform, epanechnikov, triangular, and gaussian, in window-based and spectral clustering-based models. Employing seven distinct datasets, our approach e
Externí odkaz:
https://doaj.org/article/73d52d416f544995903277269532fc1a
Publikováno v:
IEEE Access, Vol 11, Pp 60590-60604 (2023)
This study focuses on improving the accuracy of effort estimation by employing ensemble, deep learning, and transfer learning techniques. An ensemble approach is utilized, incorporating XGBoost, Random Forest, and Histogram Gradient Boost as generato
Externí odkaz:
https://doaj.org/article/350a034aac3046fbbf099c195c371cd0
Publikováno v:
IEEE Access, Vol 10, Pp 2963-2986 (2022)
Context: Effort estimation is one of the essential phases that must be accurately predicted in the early stage of software project development. Currently, solving problems that affect the estimation accuracy of Use Case Points-based methods is still
Externí odkaz:
https://doaj.org/article/40de232ebc5d41e4961ee851124b8138
Publikováno v:
IEEE Access, Vol 10, Pp 83249-83264 (2022)
Introduction: The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be de
Externí odkaz:
https://doaj.org/article/33ec5aab4db345c8a859f5bf0c4dfffe
Publikováno v:
IEEE Access, Vol 10, Pp 112187-112198 (2022)
This study compares the performance of Pytorch-based Deep Learning, Multiple Perceptron Neural Networks with Multiple Linear Regression in terms of software effort estimations based on function point analysis. This study investigates Adjusted Functio
Externí odkaz:
https://doaj.org/article/c528212b71f9484c8b2308dbed1517c0
Publikováno v:
Mathematics, Vol 10, Iss 24, p 4649 (2022)
The prediction level at x (PRED(x)) and mean magnitude of relative error (MMRE) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, t
Externí odkaz:
https://doaj.org/article/afd9aebb35824694bdaae698bd8fa15b
Autor:
Hoc Huynh Thai, Petr Silhavy, Sandeep Kumar Dey, Sinh Duc Hoang, Zdenka Prokopova, Radek Silhavy
Publikováno v:
Information, Vol 14, Iss 1, p 11 (2022)
Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidi
Externí odkaz:
https://doaj.org/article/f18d4855fb294f388985d026596ff17a
Publikováno v:
IEEE Access, Vol 7, Pp 9618-9626 (2019)
This paper proposes a new software development effort estimation model. The new model's design is based on the function point analysis, categorical variable segmentation (CVS), and stepwise regression. The stepwise regression method is used for the c
Externí odkaz:
https://doaj.org/article/f5fa2e0f736d47cfb21cacc08ceb12cc
A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation
Publikováno v:
Computers, Vol 11, Iss 2, p 15 (2022)
Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwi
Externí odkaz:
https://doaj.org/article/88e1e9f023ed489baa9cbe7564a4c0de