scholarly journals Development Cycle Modeling: Resource Estimation

2020 ◽  
Vol 10 (14) ◽  
pp. 5013
Author(s):  
Samuel Denard ◽  
Atila Ertas ◽  
Susan Mengel ◽  
Stephen Ekwaro-Osire
Keyword(s):  
Design Theory ◽  
System Development ◽  
Cost Model ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Resource Estimation ◽  
Quantitative Calculation ◽  
Development Cycle ◽  
Wide Range ◽  
Project Estimation

This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values.

Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy

2021 ◽  
Vol 9 (2) ◽  
pp. 139
Author(s):  
Alifia Puspaningrum ◽  
Fachrul Pralienka Bani Muhammad ◽  
Esti Mulyani
Keyword(s):  
Search Algorithm ◽  
Cost Model ◽  
Cuckoo Search ◽  
Estimation Accuracy ◽  
Flower Pollination Algorithm ◽  
Metaheuristic Algorithm ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Flower Pollination ◽  
Cocomo Ii

Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value.  In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

The Design and Implementation of Code Generation Based on J2EE in the Development of JBPM Workflow System

2012 ◽  
Vol 263-266 ◽  
pp. 1961-1968
Author(s):  
Yong Chao Song ◽  
Bu Dan Wu ◽  
Jun Liang Chen
Keyword(s):  
Software Development ◽  
Code Generation ◽  
System Development ◽  
Source Code ◽  
Design And Implementation ◽  
Workflow System ◽  
Development Cycle ◽  
The Cost ◽  
Static Form

According to the feature of the JBPM workflow system development, the target code generated is determined by analyzing the process of JBPM workflow development and the architecture of J2EE. The code generation tool generates code by parsing the static form source code and loading the code generation template. The code generation tool greatly shortens the JBPM workflow system development cycle and reduces the cost of software development which has the good practicality and scalability.

scholarly journals Prototype of the Near-Infrared Spectroscopy Expert System for Particleboard Identification

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Anna Sandak ◽  
Jakub Sandak ◽  
Dominika Janiszewska ◽  
Salim Hiziroglu ◽  
Marta Petrillo ◽  
...  
Keyword(s):  
Quality Control ◽  
Expert System ◽  
Near Infrared ◽  
System Development ◽  
Nir Spectroscopy ◽  
Original System ◽  
Adhesive System ◽  
Type Composition ◽  
Wide Range ◽  
Panel Type

The overall goal of this work was to develop a prototype expert system assisting quality control and traceability of particleboard panels on the production floor. Four different types of particleboards manufactured at the laboratory scale and in industrial plants were evaluated. The material differed in terms of panel type, composition, and adhesive system. NIR spectroscopy was employed as a pioneer tool for the development of a two-level expert system suitable for classification and traceability of investigated samples. A portable, commercially available NIR spectrometer was used for nondestructive measurements of particleboard panels. Twenty-five batches of particleboards, each containing at least three independent replicas, was used for the original system development and assessment of its performance. Four alternative chemometric methods (PLS-DA, kNN, SIMCA, and SVM) were used for spectroscopic data classification. The models were developed for panel recognition at two levels differing in terms of their generality. In the first stage, four among twenty-four tested combinations resulted in 100% correct classification. Discrimination precision with PLS-DA and SVMC was high (>99%), even without any spectra preprocessing. SNV preprocessed spectra and SVMC algorithm were used at the second stage for panel batch classification. Panels manufactured by two producers were 100% correctly classified, industrial panels produced by different manufacturing plants were classified with 98.9% success, and the experimental panels manufactured in the laboratory were classified with 63.7% success. Implementation of NIR spectroscopy for wood-based product traceability and quality control may have a great impact due to the high versatility of the production and wide range of particleboards utilization.

scholarly journals Software Quality Assesment using COCOMO-II Metrics with ABC and NN

2020 ◽  
Vol 9 (3) ◽  
pp. 2982-2988
Keyword(s):  
Cost Model ◽  
Small Variation ◽  
Effort Estimation ◽  
Quality Estimation ◽  
Bee Colony ◽  
Proposed Model ◽  
Software Development System ◽  
Model Author ◽  
Key Aspects ◽  
Cocomo Ii

Time, cost and quality predictions are the key aspects of any software development system. Loses that result due to wrong estimations may lead to irresistible damage. It is observed that a badly estimated project always results into a bad quality output as the efforts are put in the wrong direction. In the present study, author proposed ABC-COCOMO-II as a new model and tried to enhance the extent of accuracy in effort quality assessment through effort estimation. In the proposed model author combined the strengths of COCOMO-II (Constructive Cost Model) with the Artificial Bee Colony (ABC) and Neural Network (NN). In the present work, ABC algorithm is used to select the best solution, NN is used for the classification purpose to improve the quality estimation using COCOMO-II. The results are compared and evaluated with the pre-existing effort estimation models. The simulation results had shown that the proposed combination outperformed in terms of quality estimation with small variation of 5-10% in comparison to the actual effort, which further leads to betterment of the quality. More than 90% projects results into high quality output for the proposed algorithmic architecture.

scholarly journals UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING

2018 ◽  
Vol XLII-1 ◽  
pp. 107-114 ◽  
Author(s):  
C. Cortes ◽  
M. Shahbazi ◽  
P. Ménard
Keyword(s):  
Particle Filtering ◽  
System Development ◽  
Low Cost ◽  
Base Station ◽  
Dual Frequency ◽  
Body Frame ◽  
Integrated Sensor ◽  
Sensor Orientation ◽  
Wide Range ◽  
Gnss Measurements

<p><strong>Abstract.</strong> In the last decade, applications of unmanned aerial vehicles (UAVs), as remote-sensing platforms, have extensively been investigated for fine-scale mapping, modeling and monitoring of the environment. In few recent years, integration of 3D laser scanners and cameras onboard UAVs has also received considerable attention as these two sensors provide complementary spatial/spectral information of the environment. Since lidar performs range and bearing measurements in its body-frame, precise GNSS/INS data are required to directly geo-reference the lidar measurements in an object-fixed coordinate system. However, such data comes at the price of tactical-grade inertial navigation sensors enabled with dual-frequency RTK-GNSS receivers, which also necessitates having access to a base station and proper post-processing software. Therefore, such UAV systems equipped with lidar and camera (UAV-LiCam Systems) are too expensive to be accessible to a wide range of users. Hence, new solutions must be developed to eliminate the need for costly navigation sensors. In this paper, a two-fold solution is proposed based on an in-house developed, low-cost system: 1) a multi-sensor self-calibration approach for calibrating the Li-Cam system based on planar and cylindrical multi-directional features; 2) an integrated sensor orientation method for georeferencing based on unscented particle filtering which compensates for time-variant IMU errors and eliminates the need for GNSS measurements.</p>

Advantages and risks of crowdfunding as a model of financing startups and community development projects

2021 ◽  
Vol 2021 (4) ◽  
pp. 84-98
Author(s):  
Vitalii RYSIN ◽  
Keyword(s):  
Community Development ◽  
System Development ◽  
Low Cost ◽  
Public Awareness ◽  
Development Projects ◽  
The Public ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Capital Access

Crowdfunding as a tool for alternative financing has emerged relatively recently and is of limited use in Ukraine today. At the same time, it has significant potential, which can contribute to the implementation of a wide range of projects that for various reasons are not of interest to traditional lenders or investors. The aim of the article is to determine the benefits of crowdfunding for its participants, the peculiarities of the implementation of certain types of crowdfunding and identify risks that may be generated by them, as well as develop practical recommendations for crowdfunding campaigns by entrepreneurs and authors of community development projects. The article identifies the benefits of crowdfunding for project authors (low cost of capital, access to information and potential investors) and investors (clarity, low risks, access to new products, the ability to support creative ideas), substantiates the role of crowdfunding platforms in realizing the benefits of crowdfunding. The advantages and disadvantages of using certain types of crowdfunding are described. Recommendations for planning and implementation of the main stages of crowdfunding campaigns - idea development, target audience determination, research, communication, project budgeting, reward system development, campaign schedule development – are developed. The factors of choosing a crowdfunding platform for hosting the project are determined. The possibility of using crowdfunding for collective financing of socio-cultural projects within the public budgets of the united territorial communities is shown. The risks of using crowdfunding for project authors and potential investors are identified. Those risks are primarily related to realistic expectations and proper preparation for the fundraising campaign by project authors, as well as the lack of guarantees for investors in the event of problems or bankruptcy of the crowdfunding platform. The author highlights that the growth of public awareness about the possibilities of implementing social or business initiatives through crowdfunding platforms will contribute to the development of platforms, improvement of technological equipment, and expansion of their range of services.

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