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.

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.

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.

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.

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.

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.

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.

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.

Forecasting on Indonesian Coal Production and Future Extraction Cost: A Tool for Formulating Policy on Coal Marketing

Since 2011 Indonesia has become the world’s largest exporter of steam coal. Two supporting factors of Indonesia to be the largest exporter are its enormous production and low operating cost. This paper foresees the production and extraction cost of Indonesian coal in the coming period to evaluate marketing policies and estimate the cost of Indonesian coal supply in domestic market as well as in export market. The production forecasting is carried out by Gompertz curve. Peak production of Indonesian coal is expected to take place in 2026. Moreover, the extraction cost in coal basins which produce high calorific value of coal, in accordance to the operating cost forecasting, is higher than the one with low calorific value of coal due to its higher stripping ratio. Three main basins of Central Sumatra, Tarakan, and Barito basins play major rule in supplying coal for domestic use in short term. And other coal basins such as South Sumatra, Kutai, Bengkulu, and Ombilin basins provide long term supply in the domestic and export markets.

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.

Advanced Strategies to Mobilize Crop Residue to Replace Coal in India

Various published data show the amount of crop residue available annually in India may range from a low of 90 to a high of 180 million tonnes. Different types of crop residue are collected from farmers depending on the geography and crop pattern for instance, in north India rice straw and cotton stalks are collected while in central India soya husk and sugarcane tops are collected. Baling and transporting straw from the field, though appear to be an option for safe disposal, will be feasible only when alternate, effective and economically viable usage methods are identified and facilities and infrastructure for ex-situ management methods are created. One immediate short term use of the residue is to replace 5% - 7% of the 670 million tonnes of coal India currently consumes to generate power. The farmers will benefit from the sale of their excess crop residue. The scheme will reduce pollution due to residue burning practices. Replacing coal will cut the GHG emissions. The challenge is to mobilize the crop residue collection and timely delivery to power plants. The data and calculations in this monogram show that it is economical for the farmer to remove the crop residue from the field quickly by using modern balers, to pelletize the biomass in small-scale distributed pellet plants, to store pellets in the modern steel bins and finally to deliver the pellets to coal plants by using rail transport. The delivered cost is estimated at around Rp 6.78/kg. The Government of India encourages the power plants to pay at least Rp 10/kg for the delivered biomass in the form of pellets. The current monogram analyzes the organization of an efficient supply chain in the State of Haryana India to ensure a sustainable modern enterprise.

Software Effort Prediction Using Ensemble Learning Methods

Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.

Think Home: A Smart Home as Digital Ecosystem

This work brings all new and advanced technology which is proposed for refinement and improvement in the existing electrification system at domestic as well as commercial levels including hotels, commercial complexes, apartments, rented floors and rooms. This advanced module will not only convey means of luxury but will also accomplish real-time energy monitoring and cost es-timation. This developed module will rule out entire re-wiring and will be fruitful at places where installation of a new meter was a problem. The new system after installation will offer means of comfort to the consumer, elderly as well as handicapped and disabled people in operating electric load with ease and comfort. Apart from this, it would also benefit the apartment/hotel owner’s and business personnel who have rented their property or portion of property and face problems in calculating energy bill.

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.

Extreme Weather Loss and Damage Estimation Using a Hybrid Simulation Technique

History has shown that occurrences of extreme weather are becoming more frequent and with greater impact,regardless of one's geographical location.In a risk analysis setting,what will happen,how likely it is to happen,and what are the consequences,are motivating questions searching for answers.To help address these considerations,this study introduced and applied a hybrid simulation model developed for the purpose of improving understanding of the costs of extreme weather events in the form of loss and damage,based on empirical data in the contiguous United States.Model results are encouraging,showing on average a mean cost estimate within 5%of the historical cost.This creates opportunities to improve the accuracy in estimating the expected costs of such events for a specific event type and geographic location.In turn,by having a more credible price point in determining the cost-effectiveness of various infrastructure adaptation strategies,it can help in making the business case for resilience investment.