Comparison and Analysis of Different Software Cost Estimation Methods

Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.

Effort Estimation is a very important activity for planning and scheduling of software project life cycle in order to deliver the product on time and within budget. Machine learning techniques are proving very useful to accurately predict software effort values. This paper presents a review of various machine-learning techniques using in estimation of software project effort namely Artificial Neural Network, Fuzzy logic, Analogy estimation etc. Machine learning techniques consistently predicting accurate results because of its learning natures form previously completed projects. This paper summarizes that each technique has its own features and behave differently according to environment so no technique can be preferred over each other.

Software Project Effort Estimation - (further only SPEE), is an essential step in a software project; related to approximating Development Effort before development is completed, and is an important software development activity. Its accuracy has a significant effect on a project´s success. The major intent of this paper is to review existing Software Project Effort Estimation – (further only SPEE), exhaustively by exploring Regression Models for modern SPEEs.

Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning for machine learning algorithms (MLA) to improve the accuracy of effort estimates. In other contexts random search (RS) has shown similar results to grid search, while being less computationally-expensive. In this paper, we investigate to what extent the random search hyper-parameter tuning approach affects the accuracy and stability of support vector regression (SVR) in SEE. Results were compared to those obtained from ridge regression models and grid search-tuned models. A case study with four data sets extracted from the ISBSG 2018 repository shows that random search exhibits similar performance to grid search, rendering it an attractive alternative technique for hyper-parameter tuning. RS-tuned SVR achieved an increase of 0.227 standardized accuracy (SA) with respect to default hyper-parameters. In addition, random search improved prediction stability of SVR models to a minimum ratio of 0.840. The analysis showed that RS-tuned SVR attained performance equivalent to GS-tuned SVR. Future work includes extending this research to cover other hyper-parameter tuning approaches and machine learning algorithms, as well as using additional data sets.

The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA). Although meticulous experimental studies are needed to determine a rock’s CWA, predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR) models were created: a traditional SVR, two ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR). Results show that all models have acceptable performance in predicting the CWA with R2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods.

In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model. The proposed approach uses PSO to search the optimal parameters for model selections in the hope of improving the performance of SVR. In order to evaluate the performance of the proposed approach, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) closing cash index is used as the illustrative example. Experimental results show that the proposed model outperforms the traditional SVR model and provides an alternative in financial time series forecasting.

Use of support vector regression in structural optimization: Application to vehicle crashworthiness design

It is necessary to develop general and efficient models for the representation of vapor-liquid equilibria (VLE) for the binary mixtures containing R1234ze(E) which will be new alternatives to conventional refrigerants. This work investigates the applicability of four machine learning models, including K-nearest neighbor (KNN), support vector regression (SVR), random forests (RF), and multi-layer perceptron (MLP), in representing VLE for 10 binary mixtures. The accuracy, stability, computational complexity and extrapolation ability of these machine learning models are analyzed and compared with two thermodynamic models including Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR). The results show the KNN, RF and MLP models are not suitable to accurately represent the experimental pressure and vapor-phase mole fraction. The SVR model is the most accurate machine learning model in representing the experimental pressure with a mean absolute relative error of 0.71%, basically as accurate as the PR and SVR models. Meanwhile, the SVR model describes the experimental vapor-phase mole fraction more accurately with a mean absolute error of 0.0049 compared to the PR and SVR models. In addition, the SVR model has a low running-time cost and a certain extent of extrapolation ability.

A stacking ensemble model for predicting soil organic carbon content based on visible and near-infrared spectroscopy

Prediction of TVB-N content in eggs based on electronic nose

A method to determine the norm vacuum of duplex pressure condenser based on the support vector regression (SVR) model was proposed, The norm vacuum of condenser is usually obtained from the characteristic curve given by the manufactory. But the method is only fit for the condition that both of the flow of cooling water and exhaust steam of steam turbine are maintained at designed valves. In this paper, the factors which affected the norm vacuum of condenser were analyzed, and then a new method was studied with SVR model's parameters optimized by genetic algorithm (GA). Data from duplex pressure condenser of a 600MW unit were validated by the proposed method. The results show that the SVR model has high prediction accuracy and very strong stability. The studying results fall foundations for diagnosing the faults of low vacuum for condenser.

SummaryInformation technology companies currently use data mining techniques in different areas with the goal of increasing the quality of decision‐making and to improve their business performance. The study described in this paper uses a data mining approach to produce an effort estimation of a software development process. It is based on data collected in a Croatian information technology company. The study examined 27 software projects with a total effort exceeding 42 000 work hours. The presented model employs a modified Cross‐Industry Standard Process for Data Mining, where prior to model creation, additional clustering of projects is performed. The results generated by the proposed approach generally had a smaller effort estimation error than the results of human experts. The applied approach has proved that sound results can be gained through the use of data mining within the studied area. As a result, it would be wise to use such estimates as additional input in the decision‐making process.

Effort estimation is a long faced problem, but, in spite of the amount of research spent in this field, it still remains an open issue in the software engineering community. In two previous works the authors proposed the an approach named Dynamic Calibration for effort estimation of software projects. In this paper they present a tool named SPEED that implements the Dynamic Calibration approach.

Nonlinear QSAR models with high-dimensional descriptor selection and SVR improve toxicity prediction and evaluation of phenols on Photobacterium phosphoreum

Effort estimation is the process of predicting a software's likely cost and development time. Estimating software development effort is still a complex problem that receives a lot of research attention. The accuracy with which the effort necessary for software development is estimated, and it is a significant success element in software project management. The estimated effort should be roughly equal to the actual effort in a good software estimation model. Accurate estimation enables managers to allocate resources for the planning and coordination of all activities. In this paper the effort estimation for small size projects involving technical and analytical aspects of engineering intent. The estimation of time resources is done in three steps firstly Identification of seven novel traits of software (LOC, N&C, CGPA, R, CC, AC, FP). Secondly PCA was implemented to decide the significant parameters among the software features. Lastly linear regression and polynomial regression models were developed using the significant features predicted by the PCA. The result of evaluation of the two models are encouraging with the minimum RMSE of 32-204 minutes and maximum regression coefficients(r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 0.95-0.98