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.

A software cost model with maintenance and risk costs for safety-critical systems

According to the consequences of software failures, software faults remaining in safety-critical systems can be classified into two sets： common faults and fatal faults. Common faults cause slight loss when they are activated. A fatal fault can lead to significant loss, and even damage the safety-crltical system entirely when it is activated. A software reliability growth model for safety-critical systems is developed based on G - 0 model. And a software cost model is proposed too. The cost model considers maintenance and risk costs due to software failures. The optimal release policies are discussed to minimize the total software cost. A numerical exampie is provided to illustrate how to use the results we obtained.

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.

Investigation on Relationship between Cost and Software Reliability

Based on the fact that the software development cost is an important factorto control the whole project, we discuss the relationship between the software development cost andsoftware reliability according to the empirieal data collected from the development process. Byevolutionary modeling we get an empirical model of the relationship between cost and softwarereliability, and validate the estimate results with the empirical data.

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.

Measuring Effectiveness of COCOMO I and COCOMO II Using a Case Study

There are several software estimation models such as Line of Code, Function Point and COnstructive COst MOdel （COCOMO）. The original COCOMO model is one of the most widely practiced and popular among the software development community because of its flexible usage. It is a suite of models i.e., COnstructive Cost MOdel I and COnstructive Cost MOdel II. in this paper, we are evaluating the both models, to find out the level of efficiency they present and how they can be tailored to the needs of modem software development projects. We are applying COCOMO models on a case study of an e-commerce application that is built using Hyper Text Markup Language （HTML） and JavaScript. We will also shed light on the different components of each model, and how their Cost Drivers effect on the accuracy of cost estimations for software development projects.