Class point approach for software effort estimation using various support vector regression kernel methods

Accurate software effort estimation is one of the key factors to a successful project by making a better software project plan. To improve the estimation accuracy of software effort, many studies usually aimed at proposing novel effort estimation methods or combining several approaches of the existing effort estimation methods. However, those researches did not consider the distribution of historical software project data which is an important part impacting to the effort estimation accuracy. In this paper, to improve effort estimation accuracy by least squares regression, we propose a data partitioning method by the accuracy measures, MRE and MER which are usually used to measure the effort estimation accuracy. Furthermore, the empirical experimentations are performed by using two industry data sets (the ISBSG Release 9 and the Bank data set which consists of the project data performed in a bank in Korea).

Over the past ten couple of years, there is a variety of effort models proposed by academicians and practitioners at early stage of software development life cycle. Some addressed that efforts could be predicted using Lines of Codes(LOC) and COCOMO, others emphasized that it could be made using Function Point Analysis(FPA) or others. The study seeks to develop a model that estimates software effort by studying and analyzing small and medium scale application software. To develop such a model, 50 completed software projects are collected from a software company. With the sample data, design team factors are identified and extracted. By applying them to simple regression analyses, a prediction of software of effort estimates with accuracy of MMRE = 9% was constructed. The results give several benefits. First, the estimation problems are minimized due to the simple procedure used in identifying those factors. Second, the predicted software projects are only limited to a specific environment rather than being based upon industry environment. We believe the accuracy of effort estimates can be improved. According to the results analyzed, the work shows that it is possible to build up simple and useful prediction model based on data extracted at the early stage of software development life cycle. We hope this model can provide valuable ideas and suggestions for project designers for planning and controlling software projects in near future.

The web projects that are completed on time and within budget ascertain a commendable position in the rapidly growing economic web development market.Web Effort Estimation (WEE) estimates the time t will take to develop a web application in person-hours or months Expert Opinion algorithmic models, e.g., Constructive Cost Model (COCOMO), and machine learning are the primarily used effort estimating techniques.As current effort estimating techniques face many shortcomings, accurate effort prediction has become a challenging task.To improve prediction accuracy, this work proposes a hybrid approach based on Machine Learning.This approach is validated through an empirical evaluation of the International Software Benchmark Software Group, ISBSG Release 19 dataset.The ISBSG R19 dataset is first pre-processed using machine learning-based linear regression.Secondly, Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest regression (RFR), and Ridge Regression (RR) techniques are employed to predict the web effort.The performance of the examined models is evaluated using two commonly used evaluation metrics, Mean Magnitude Relative Error (MMRE) and Prediction accuracy at level 25%, i.e., Pred(25).Then, the statistical significance of effort predicting model producing the highest accuracy and lowest error rates is verified using the Mann-Whitney U test.The performance of the proposed models is also compared with the existing effort estimation models.The results show that the Ridge regressionbased model produces exceptionally improved prediction accuracy for web projects in this work.

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 effort involved in developing a software product plays an important role in determining the success or failure. In the context of developing software using object oriented methodologies, traditional methods and metrics were extended to help managers in effort estimation activity. Software project managers require a reliable approach for effort estimation. It is especially important during the early stage of the software development life cycle. In this paper, the main goal is to estimate the cost of various software projects using class point approach and optimize the parameters using six types of adaptive regression techniques such as multi-layer perceptron, multivariate adaptive regression splines, projection pursuit regression, constrained topological mapping, K nearest neighbor regression and radial basis function network to achieve better accuracy. Also a comparative analysis of software effort estimation using these various adaptive regression techniques has been provided. By estimating the effort required to develop software projects accurately, we can have softwares with acceptable quality within budget and on planned schedules.

Rotor-to-stator impact-rub of rotor is a kind of common fault of steam turbine. Traditional method of fault modeling, such as statistical theory and artificial neural network, usually gets a non-linear model because of the complexity of turbine system. Based on the need of analysis for impact-rub degree and fault trend, Support Vector Regression (SVR) arithmetic is imported and used for time series analysis and prediction. SVR suggests a best tradeoff between complexity of model and learning ability to establish a linear model in high-dimension feature space. The model built by SVR is able to reflect the implicit mechanism in the data set of time series. But SVR arithmetic has longer running time and large need of memory, so an improved arithmetic of SVR was imported, which uses smooth method and advances operation capability of standard SVR method, and is very fit for the time series of small sample size. Simulation indicates that smooth SVR method has higher speed, better precision and generalization ability than neutral network, and is effective to analyze the trend of rotor-to-stator impact-rub fault for steam turbine, which has guiding significance for forecasting fault and maintenance of steam turbine.

Soft Computing Techniques (SCT) are extensively used to estimate Labyrinth Weir's (LW) flow-rate. Due to the multiplicity of these techniques, identifying the most competent SCT is indispensable. This study aims to estimate the flow-rate of a sharp-crest triangular LW as a function of its side leg angle α and total head ratio (H/P) through several SCTs such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), Support Vector Regression, and Radial Basis Function Neural Network. Additionally, these SCTs' potential combinations with Firefly Optimization Algorithm (FA) and Particle Swarm Optimization (PSO) are also investigated and used for estimation. The conducted experimental studies on LW examined a wide range of H/P in some limited α values. Correspondingly, all the proposed models and techniques are incapable of estimating the flow rate for intermediate α values without interpolation. Therefore, SCT's Interpolation accuracy is of the utmost importance. Besides the standard evaluation in the testing stage, a novel approach is utilized to evaluate the SCT's accuracy in the interpolation task. The SCTs are evaluated based on several statistical criteria, the Taylor diagram, Kruskal-Wallis, and Mann-Whitney tests. It is concluded that the competence of an SCT in the testing stage cannot guarantee its accuracy in the interpolation task. Subsequently, ANFIS-PSO and MLP-FA show the highest accuracy in the testing stage and interpolation task, respectively. Eventually, according to a systematic investigation in the implemented diagnostic test results, two rankings are presented for the applied SCTs based on their performance in the testing stage and their interpolation accuracy.

Effort estimations are a crucial aspect of software development. The tasks should be completed before the start of any software project. Accurate estimations increase the chances of project success, and inaccurate information can lead to severe issues. This study systematically reviewed the literature on effort-estimating models from 2015-2024, identifying 69 relevant studies from various publications to compile information on various software work estimation models. This review aims to analyze the models proposed in the literature and their classification, the metrics used for accuracy measurement, the leading model that has been chiefly applied for effort estimation, and the benchmark datasets available. The study utilized 542 relevant articles on software development, cost, effort, prediction, estimation, and modelling techniques in the search strategy. After 194 selections, the authors chose 69 articles to understand ML applications in SEE comprehensively. The researchers used a scoring system to assess each study’s responses (from 0 to 5 points) to their research questions. This helped them identify credible studies with higher scores for a comprehensive review aligned with its objectives. The data extraction process identified 91% (63) of 69 studies as either highly or somewhat relevant, demonstrating a successful search strategy for analysis. The literature review on SEE indicates a growing preference for ML-based models in 59% of selected studies. 17% of the studies chosen favor hybrid models to overcome software development challenges. We qualitatively analyzed all the literature on software effort estimation using expert judgment, formal estimation techniques, ML-based techniques, and hybrid techniques. We discovered that researchers have frequently used ML-based models to estimate software effort and are currently in the lead. This study also explores the application of feature importance and selection in machine learning models for Software Effort Estimation (SEE) using popular algorithms like support Vector Machine (SVM), AdaBoost (AB), Gradient Boost (GB), and Random Forest (RF) with six benchmark datasets like CHINA, COCOMO-NASA2, COCOMO, COCOMO81, DESHARNAIS, and KITCHENHAM. We analyze the dataset descriptions and feature importance of the dataset analysis using ML models for choosing crucial play attributes in SEE.

Voluntary effort combined with functional electrical stimulation (FES) can improve post-stroke motor recovery, yet it is important for the user to actively engage in the rehabilitation task rather than reduce effort during FES assistance, which is an undesired phenomenon known as 'slacking'. To prevent slacking, EMG (electromyography)-controlled FES devices provide assistance as a function of voluntary paretic limb effort that is estimated from paretic muscle EMG using filters that remove stimulation-related responses (m-Waves). However, existing effort estimation methods do not compensate for the reduction of paretic muscle EMG due to interaction of voluntary action potentials with stimulus generated action potentials in the same motor axons, known as 'occlusion'. This phenomenon can lead to undesirable underestimation of voluntary effort during FES applications. In this study, we recruited five people without stroke to investigate the hypothesis that occlusion can be measured using a novel finger extension force tracking task and be used to improve voluntary effort estimation. We evaluated 1) the accuracy of voluntary effort estimation with and without occlusion correction and 2) characterized the effect of different m-Wave filters (Gram-Schmidt and comb filters) on effort estimation accuracy. Results indicated that occlusion correction can significantly improve accuracy of voluntary effort estimated from EMG and that the choice of m-Wave filters can significantly affect effort estimation accuracy.Clinical relevance- This study aims to improve EMG-based effort estimation of functional electric stimulation (FES) rehabilitation participants after stroke. This is critical for 'slacking' reducing FES applications, which have a potential to enhance the efficacy of FES therapy.

Problem statement: The precision and reliability of the effort estimation is very important for the competitiveness of software companies. The uncertainty at the input level of the Constructive Cost Model (COCOMO) yields uncertainty at the output, which leads to gross estimation error in the effort estimation. Fuzzy logic-based cost estimation models are more appropriate when vague and imprecise information was to be accounted for and was used in this research to improve the effort estimation accuracy. This study proposed to extend the COCOMO by incorporating the concept of fuzziness into the measurements of size. The main objective of this research was to investigate the role of size in improving the effort estimation accuracy by characterizing the size of the project using trapezoidal function which gave superior transition from one interval to another. Approach: The methodology adopted in this study was use of fuzzy sets rather than classical intervals in the COCOMO. Using fuzzy sets, size of a software project can be specified by distribution of its possible values and these fuzzy sets were represented by membership functions. Though, Triangular membership functions (TAMF) was used in the literature to represent the size, but it was not appropriate to clear the vagueness in the project size. Therefore, to get a smoother transition in the membership function, the size of the project, its associated linguistic values were represented by trapezoidal shaped MF and rules. Results: After analyzing the results attained by means of applying COCOMO, triangular and trapezoidal MF models to the COCOMO dataset, it had been found that proposed model was performing better than ordinal COCOMO and trapezoidal function was performing better than triangular function, as it demonstrated a smoother transition in its intervals and the achieved results were closer to the actual effort. The relative error for COCOMO using trapezoidal function is lower than that of the error obtained using TAMF. Conclusion: From the experimental results, it was concluded that, by fuzzifying the project size using TPMF, the accuracy of effort estimation can be improved and the estimated effort can be very close to the actual effort.

In the life cycle of software product development, the software effort estimation (SEE) has always been a critical activity. The researchers have proposed numerous estimation methods since the inception of software engineering as a research area. The diversity of estimation approaches is very high and increasing, but it has been interpreted that no single technique performs consistently for each project and environment. Multi-criteria decision-making (MCDM) approach generates more credible estimates, which is subjected to expert’s experience. In this paper, a hybrid model has been developed to combine MCDM (for handling uncertainty) and machine learning algorithm (for handling imprecision) approach to predict the effort more accurately. Fuzzy analytic hierarchy process (FAHP) has been used effectively for feature ranking. Ranks generated from FAHP have been integrated into weighted kernel least square support vector machine for effort estimation. The model developed has been empirically validated on data repositories available for SEE. The combination of weights generated by FAHP and the radial basis function (RBF) kernel has resulted in more accurate effort estimates in comparison with bee colony optimisation and basic RBF kernel-based model.

Which one is more important in daily runoff forecasting using data driven models: Input data, model type, preprocessing or data length?

The current paper presents an investigation of the accuracy of soft-computing techniques in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia from 1946 to 2012 are used as a case study. Despite a number of mathematical functions having been proposed for modeling precipitation estimation, the models still have disadvantages such as being very demanding in terms of calculation time. Soft computing can be used as an alternative to the analytical approach, as it offers advantages such as no required knowledge of internal system parameters, compact solutions for multivariable problems, and fast calculation. Because precipitation prediction is a crucial problem, a process which simulates precipitation with two soft-computing techniques was constructed and presented in this paper, namely, adaptive neurofuzzy inference (ANFIS) and support vector regression (SVR). In the current study, polynomial, linear, and radial basis function (RBF) are applied as the kernel function of the SVR to estimate the probability of precipitation. The performance of the proposed optimizers is confirmed with the simulation results. The SVR results are also compared with the ANFIS results. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved with the ANFIS approach compared to SVR estimation. The simulation results verify the effectiveness of the proposed optimization strategies.

A Survey: To Explore the Factors Behind Inaccuracy in Cost Effort Estimation and Providing Improvements and Suggestions

Software cost estimation is the process of predicting the effort required to develop a software system. The basic input for the software cost estimation is coding size and set of cost drivers, the output is Effort in terms of Person-Months (PM’s). Here, the use of support vector regression (SVR) has been proposed for the estimation of software project effort. We have used the COCOMO dataset and our results are compared to Intermediate COCOMO as well as to MOPSO model results for this dataset. It has been observed from the simulation that SVR outperforms other estimating techniques. This paper provides a comparative study on support vector regression (SVR), Intermediate COCOMO and Multiple Objective Particle Swarm Optimization (MOPSO) model for estimation of software project effort. We have analyzed in terms of accuracy and Error rate. Here, data mining tool Weka is used for simulation