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.

Enhancing Software Effort Estimation:A Hybrid Model Combining LSTM and Random Forest

Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.

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.

Analogy-based software effort estimation using multi-objective feature selection

The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper- based feature selection method, multi-objective feature selection for analogy-based software effort estimation （MASE）, is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE （mean magnitude of relative error）, MdMRE （median magnitude of relative error）, PRED （ 0. 25 ）, and SA （ standardized accuracy） performance metrics.

Real-Time Frequency Estimation of Complex GMSK Signal of Green Communications Devices

Parameter estimation of signals of universal software radio peripheral （USRP） devices is crucial to solve the problem of phase offsets of received signals in distributed beamforming. For systems that will utilize the closed loop feedback algorithm where the receiver needs to send the received signal strength （RSS） values periodically to the beamforming node so as to take advantage of energy conservation, the frequency and phase of these signals should be estimated before smoothening by nonlinear filters. This article presents the estimation of the frequency offsets of a Gaussian minimum shift keying （GMSK） signal from N210 USRP devices in real time by using the Radix-2 fast Fourier transform （FFT） algorithm in GNURadio. For these green communications devices, most of the needed hardware parts have been software defined, thereby reducing the supposed energy consumption. The frequency offsets from reference carrier frequencies of 900 MHz and 2.4 GHz are less than 3 kHz each before the estimation, but the average offsets are 45 Hz and 100 Hz after the estimation, respectively. The high offset value experienced with the 2.4 GHz carrier was due to consistent interference from devices on that same frequency.

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.

Multi-instance learning for software quality estimation in object-oriented systems:a case study

We investigate a problem of object-oriented (OO) software quality estimation from a multi-instance (MI) perspective. In detail,each set of classes that have an inheritance relation,named 'class hierarchy',is regarded as a bag,while each class in the set is regarded as an instance. The learning task in this study is to estimate the label of unseen bags,i.e.,the fault-proneness of untested class hierarchies. A fault-prone class hierarchy contains at least one fault-prone (negative) class,while a non-fault-prone (positive) one has no negative class. Based on the modification records (MRs) of the previous project releases and OO software metrics,the fault-proneness of an untested class hierarchy can be predicted. Several selected MI learning algorithms were evalu-ated on five datasets collected from an industrial software project. Among the MI learning algorithms investigated in the ex-periments,the kernel method using a dedicated MI-kernel was better than the others in accurately and correctly predicting the fault-proneness of the class hierarchies. In addition,when compared to a supervised support vector machine (SVM) algorithm,the MI-kernel method still had a competitive performance with much less cost.

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.