A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

Cost estimation and control of colorectal cancer screening

Objective:The aim of our study was to estimate the cost of colorectal cancer screening and to provide evidence for the cost control of colorectal cancer screening among general population in rural area of China.Methods:We determined the net cost for colorectal cancer mass-screening in Jiashan County,and evaluated the cost-benefit and cost effectiveness.Results:The compliance rate of primary screening and intensive screening were 84.6% and 78.7%,respectively.In primary screening,the average cost for each individual was 27.2 yuan,and the average cost for identifying one high-risk individual was 180.5 yuan.The mean cost to diagnose one colorectal cancer patient was 42963.3 yuan.As for identification of adenoma,the average cost for each case was 4384.0 yuan.Based on the calculation,the average cost of reducing one colorectal cancer patient was 12768 yuan by conducting the mass-screening protocol.Conclusion:It was beneficial to do the cost-benefit analysis of colorectal cancer screening in area of high incidence.Based on the results of cost-benefit analysis,more efforts should be made to reduce the cost and to improve the efficiency of the colorectal cancer screening.

Software Cost Estimation Approaches: A Survey

The software cost estimation aims to predict the most realistic effort that is required to finish a software project and so it is critical to the success of a software project management. A Software Cost Estimation affects nearly all management activities, including project bidding, resource allocation and project planning. It is affected by a number of factors, such as implementation efficiency, as well as how much the various reviews and studies completed prior to the software development stage cost. Accurate cost estimation will help us to complete the project on time and within budget. Accurate estimation is important because it has led to extensive research into the methods of software cost estimation. Some important software cost estimation methods have been studied in this research work. In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.

A Survey on Software Cost Estimation Techniques

The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.

Support Vector Machine Cost Estimation Model for Road Projects

A cost estimate is one of the most important steps in road project management. There are ranges of factors that mostly affect the final project cost. Many approaches were used to estimate project cost, which took into consideration probable project performance and risks. The aim is to improve the ability of construction managers to predict a parametric cost estimate for road projects using SVM （support vector machine）. The work is based on collecting historical road executed cases. The 12 factors were identified to be the most important factors affecting the cost-estimating model. A total of 70 case studies from historical data were divided randomly into three sets： training set includes 60 cases, cross validation set includes three cases and testing set includes seven cases. The built model was successfully able to predict project cost to the AP （accuracy performance） of 95%.

Manufacturing feature-based cost estimation of cast parts

Seventy percent of new product cost is committed in the design phase. Therefore, the cost needs to be minimized. Incorporating accurate cost estimation capabilities into manufacturing processes can help industries to minimize the cost and optimize the design. The casting process has been employed as a key process to manufacture parts having the advantage of low cost and customized products in foundry industry. This paper presents a cost estimation system for the casting process based on the design features, which incorporates the casting information at the design stage of castings. It aims to deliver the best cost-effective choice for casting design features of a product. Two kinds of knowledge, namely economical knowledge(describing the relation between the casting design considerations and the cost) and technical knowledge(involving the material of the casting features) were structured in this research work. On the basis of the minimum product cost, casting material selection can be carried out in detail. The developed system has been validated through an industrial case study.

An improved activity-based costing model for product cost estimation applied in a complex manufacturing environment

In order to evaluate and estimate me cosEs oi prouuct~ plt, uu^u e vironment properly, an improved activity-based costing （ABC） model is presented. By utilizing the input-output analysis method, the complex consumption relationships in a complex manufacturing environment are first expressed. The consumption characteristics （mainly presented by the activity rates） of all production activities are extracted by solving these relationships. Then with the con- sumption characteristics and operating parameters of these activities, the detailed cost consumption of a product in its manufacturing process is estimated. A case study is finally given based on the compressor products of a manufacturing company, and its effectiveness is shown. As the cost influ- ence of complex consumption relationships is fully considered, the limitation of traditional ABC method is overcome, and therefore a high accuracy in product cost estimation under the complex manufacturing environment can be achieved.

Analyzing the Impact of Different Factors on Software Cost Estimation in Today’s Scenario

Software cost estimation is a main concern of the software industry. However, the fact is also that in today’s scenario, software industries are more interested in other issues like new technologies in the market, shorter development time, skill shortage etc. They are actually deviating from critical issues to routine issues. Today, people expect high quality products at very low costs and same is the goal of software engineering. An accuracy in software cost estimation has a direct impact on company’s reputation and also affects the software investment decisions. Accurate cost estimation can minimize the unnecessary costs and increase the productivity and efficiency of the company. The objective of this paper is to identify the existing methods of software cost estimation prevailing in the market and analyzing some of the important factors impacting the software cost estimation process. In order to achieve the objective, a survey was conducted to find out: ● Nature of projects that companies prefer. ● Impact of training on employees in software cost estimation. ● How many people review the estimated cost? ● How much risk buffer the company keeps for future prospects?

Rational Cost Estimation of Dedicated Software Systems

Dedicated software systems hold a character of individual solutions that entail particular problems with regard to their cost estimation. Thus for many years now objective and reliable approaches to the cost estimation of such systems have been sought out so that they could provide the possibility to make rational investment decisions concerning those systems. The purpose of this paper is to bring in the approach to cost estimation of dedicated software systems that has been recently growing in global popularity, using it as a background for presenting conclusions resulting from practical verification of author’s own model of dedicated software system cost estimation based on that approach.

Limitations of Cost Estimation Using Building Information Modeling in Poland

BIM （building information modeling） is a technological innovation, not only during the design process, but also during the planning and preparation stages of a construction project, as it also supports making investment decisions. An innovation which is comparable, if only slightly less significant, was the transition from using 2D systems to the 3D structural model design. The article outlines the advantages of using BIM in the preparatory stages of a construction project. It also presents benefits which relate to the employment of the BIM system in cost estimation process. The article describes the Zuzia BIM system which uses the BIM model, as this system has just been created in Poland for the purpose of construction cost estimation. The preparation of the bill of quantities is automated in this system and this has been achieved on the basis of data directly obtained from virtual models of buildings, which were carried out thanks to the collaboration of various design sectors. The article authors, using their own experience, present difficulties which can be encountered by cost estimators in Poland when calculating the value of a building with the help of the BIM concept. The article shows the design errors that prevent or hinder takeoff automatic calculation based on BIM model. Design errors shown in the article are for example reinforcement bars have been defined by a designer as elements hollow in the middle or as one element for the whole building, one type of elements assigned as few different or incorrect defining of elements in relation to the type of works.

Cost Control Strategies in the Pre-Construction Period of Highway Construction

Effective cost control in the investment and design phases of highway construction is crucial for managing project expenses.However,current management practices often overlook pre-construction cost management,leading to budget overruns and project delays during later stages.To ensure the smooth execution and cost control of highway construction projects,this paper examines the significance of cost control,evaluates the current state and challenges of highway construction,and proposes strategies for cost management.These strategies aim to establish a robust foundation for cost management in highway projects.

Product Assembly Cost Estimation Based on Artificial Neural Networks

This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks(ANN). It can support the designers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a comparison, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.

A Modified Cost Estimation Formulae for Calculating Manufacturing Cost of Injection Part/Mold

In modern society, a plastic part has its own important position. To the managers and decision makers in the field of plastic component, how to decide the manufacturing cost of the injection part or injection mold as quickly as possible is most valuable. Cost estimation formulae (CEF) are the most common method to evaluate the cost of injection part/mold, which is similar to our ordinary thinking. In this paper, a CEF method used by Dr. Weiyi Hu in Massachusetts University is first discussed. To the problems existed in the formulae, we propose an improved algorithm. This algorithm can be used to estimate the manufacturing cost of injection part/mold accurately in the early design stage.

Estimating Military Aircraft Cost Using Least Squares Support Vector Machines

A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent costestimation process is divided into three steps in the model. In the first step, a cost-drive-factorneeds to be selected, which is significant for cost estimation. In the second step, militaryaircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Thenthe model can be used for new type aircraft cost estimation. Chinese military aircraft costs areestimated in the paper. The results show that the estimated costs by the new model are closer to thetrue costs than that of the traditionally used methods.

Life cycle inventory for base metal ingots production in Japan including mining and mineral processing processes by cost estimating system database

CO2 emission levels of copper and zinc mines from which Japanese smelters import ore concentrates into Japan,were estimated by using a database called MLED. Eleven copper mines selected from data availability of mine site covered 84% of the total imported concentrates. Adding inventories of sea transportation and smelting processes to mine development process,total CO2 emission level for copper and zinc ingots produced in Japan were calculated. The results show that the emission share of mining and mineral processing processes for each mine is indicated around 30%-70% of total emission for ingots,which implies the importance of including the mining activities to the inventory of upper stream products.

Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In order to improve the prediction accuracy of ultra-deep well drilling costs,the item and the dominant factors of drilling costs in Tarim oilfield are analyzed.Then,those factors of drilling costs are separated into categorical variables and numerous variables.Finally,a BP neural networkmodel with drilling costs as the output is established,and hyper-parameters(initial weights and bias)of the BP neural network is optimized by genetic algorithm(GA).Through training and validation of themodel,a reliable prediction model of ultra-deep well drilling costs is achieved.The average relative error between prediction and actual values is 3.26%.Compared with other models,the root mean square error is reduced by 25.38%.The prediction results of the proposed model are reliable,and the model is efficient,which can provide supporting for the drilling costs control and budget planning of ultra-deep wells.

Estimating costs of salvage logging for large-scale burned forest lands: A case study on Turkey's Mediterranean coast

Different forest fires causing different degrees of effects occur in fire-sensitive forests due to various reasons such as climate change.Useful as well as harmful aspects of forest fires are a multi-disciplinary research topic.Geographical information systems(GIS)and remote sensing(RS)methods offer a number of benefits for researchers and operators in the field of forest fire research.The present study analyses timber pricing based on forest contractor demands of post-salvage logging processes.The effect of timber obtained from compartment units on producers’pricing policy was modelled.Sapadere forest fire area(2500 ha)located in Antalya in Turkey was selected as the main study area.Topography parameters(aspect,slope and slope position),stand types(diameter class and crown closure),and burn severity were analyzed together using GIS and R software packages.A multi-linear regression model(R^(2)=0.752)demonstrated that factors that had the most impact on pricing were slope position,aspect,stand age,crown closure and burn severity.This model can be used to estimate salvage logging prices in Calabrian pine(Pinus brutia Ten.)stands with similar parameters.Forest administrators and contractors may readily address the unit price of timber by estimating approximate costs in a given forest area for which they are going to bid.This will help reduce operational planning times of harvesting procedures in burned stands.