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.

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.

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%.

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.

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.

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.

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.

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.

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.

Method for Estimating of Construction Cost of a Building Based on Previous Experiences

In a real estate project, the estimated cost of construction and the revenues generally represent together the most important values of its feasibility study. When a decision of undertaking a project is made, often there are few definitions of what is about to be constructed, and frequently not enough to ensure the accuracy of the estimated costs. Considering a global tendency on reducing margins of return over the real estate markets, slight variations of the construction cost can jeopardize the success of the whole real estate enterprise and even the financial stability of the builder or of the developer. This article aims at presenting a method of estimating the building construction costs applicable at the stage of feasibility studies, being able to provide acceptable errors.

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

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.

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.

A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.

Exploring ERP Adoption Cost Factors

Due to their limited resources, budgets and their high sensitivity to costs, when Small and Medium Enterprises （SMEs） take the first step into implementing an Enterprise Resource Planning （ERP） system, they need to think about many things, foremost the cost of adoption. Literature suggests that most ERP implementations fail due to inaccurate and optimistic budget and schedule estimations, as well as, anticipating indirect costs beforehand is problematic. With the deficiency of a clear model of cost factors for ERP adoptions, ERP adoptions face high risks of failure. Failures could be caused by several factors, but the scope of this research is focused on identifying, exploring, and validating a comprehensive list of ERP adoption cost factors. This could aid SMEs in visualizing the different expected costs, and would consequently assist in better future cost management and estimations. There has been plenty of research in ERP; however, a clear gap in ERP cost identification, management, and estimation exists. This paper focuses on identifying direct and indirect cost factors that influence total costs in the ERP adoption process. The paper presents a cost list that has been developed through literature and an ERP expert panel. Furthermore, this study validates the costs list through interviews with different stakeholders within ERP adoption projects in Egypt.

A Hybrid Associative Classification Model for Software Development Effort Estimation

A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the development effort are divided into groups based on the distribution of their values in the available dataset. The linguistic terms are identified for the divided groups using fuzzy functions, and the parameters are fuzzified. The fuzzified parameters then adopt associative classification for generating association rules. The association rules depict the parameters influencing the software development effort. As the number of parameters that influence the effort is more, a large number of rules get generated and can reduce the complexity, the generated rules are filtered with respect to the metrics, support and confidence, which measures the strength of the rule. Genetic algorithm is then employed for selecting set of rules with high quality to improve the accuracy of the model. The datasets such as Nasa93, Cocomo81, Desharnais, Maxwell, and Finnish-v2 are used for evaluating the proposed model, and various evaluation metrics such as Mean Magnitude of Relative Error, Mean Absolute Residuals, Shepperd and MacDonell’s Standardized Accuracy, Enhanced Standardized Accuracy and Effect Size are adopted to substantiate the effectiveness of the proposed methods. The results infer that the accuracy of the model is influenced by the metrics support, confidence, and the number of association rules considered for effort prediction.

Intelligent Cost Modeling Based on Soft Computing for Avionics Systems

In parametric cost estimating, objections to using statistical Cost Estimating Relationships （CERs） and parametric models include problems of low statistical significance due to limited data points, biases in the underlying data, and lack of robustness. Soft Computing （SC） technologies are used for building intelligent cost models. The SC models are systemically evaluated based on their training and prediction of the historical cost data of airborne avionics systems. Results indicating the strengths and weakness of each model are presented. In general, the intelligent cost models have higher prediction precision, better data adaptability, and stronger self-learning capability than the regression CERs.

Cost Estimate and Input Energy of Floor Systems in Low Seismic Regions

Reinforced concrete(RC)as a material is most commonly used for buildings construction.Several floor systems are available following the structural and architectural requirements.The current research study provides cost and input energy comparisons of RC office buildings of different floor systems.Conventional solid,ribbed,flat plate and flat slab systems are considered in the study.Building models in three-dimensional using extended threedimensional analysis of building systems(ETABS)and in two-dimensional using slab analysis by the finite element(SAFE)are developed for analysis purposes.Analysis and design using both software packages and manual calculations are employed to obtain the optimum sections and reinforcements to fit cities of low seismic intensities for all the considered building systems.Two ground motion records of low peak ground acceleration(PGA)levels are used to excite the models to measure the input energies.Uniformat cost estimating system is adopted to categorize building components according to 12 divisions.Also,Microsoft(MS)Project is utilized to identify the construction cost and duration of each building system.The study shows that floor system significantly causes changes in the input energy to structures.In addition,the slight increase in the PGA increases the amount of input energy particularly flat plate system.Estimated cost of the flat plate system that the flat slab system is of higher value as compared to ribbed and conventional slab systems.The use of drop panels increases this value as well.Moreover,the estimated cost of the ribbed slab system exceeds that of conventional system.

Streamlining Takeoff and Estimating with BIM:A Case Study of a Construction Cost Estimating Course

Since the construction industry has been adopting Building Information Modeling(BIM)as the standard practice for design,engineering,and fabrication in the recent decade,many Construction Management(CM)programs at U.S.universities have started to introduce BIM for cost estimating in their curriculum.Although considered as the fifth dimension beyond 3D and schedule,BIM for cost estimating in many cases is still used merely as an alternative model-based quantity takeoff method to the traditional plan-based approach.The disconnection between automated quantity takeoff and cost estimating,however,still exists,and the benefits of the BIM process in a project life cycle can not be fully understood by CM students without realizing its impact in the preconstruction phase.To bridge these gaps in a CM curriculum,an Advanced Cost Estimating course for CM programs has been developed that focuses on integrating BIM in both the takeoff and estimating process.The new course streamlines the connection between model-based quantity takeoff and cost estimating with the help of a combination of multiple construction software programs.Through the integration between the software,quantity data from a BIM model can be seamlessly transferred to a construction cost database for bid pricing and reporting.This paper presents the development of the new Advanced Cost Estimating course as a case study,including its objectives,layout,and assessment methods,and provides empirical and valuable insights on how to integrate BIM in a cost estimating course for a CM curriculum.

Important Factors for Construction Project Cost Estimating Using ANN

Cost estimation has its proven importance as one of essential factors for project success. The aim of this research is to predict the early project cost using neural network. Early project cost represents a key component in business unit decisions. The most important factors influencing on the parametric cost estimation in construction building projects in Gaza Strip were defined and investigated. A questionnaire survey and relative index ranking technique were used to conclude the most important factors. Fourteen most effective factors were identified. One hundred and six case studies from real executed construction project in Gaza Strip were collected for training and testing the model. The cases were prepared to be used in cost estimate neural networks model. Eighty percent of case studies were used to train and test the model. The remaining 20% was used for model verification. The results revealed the ability to the model to predict cost estimate to an acceptable degree of accuracy. The minimum squares error with 0.005 in training stage and 0.021 in testing stage were recorded.