COMPARATIVE ANALYSIS OF SOFTWARE EFFORT ESTIMATION USING DATA MINING TECHNIQUE AND FEATURE SELECTION

Software development involves several interrelated factors that influence development efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time and budget control in software development. Software Effort Estimation or software cost/effort estimation can help a software development company to overcome difficulties experienced in estimating software development efforts. This study aims to compare the Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree Random Forest (DTRF) to calculate estimated cost/effort software. Then these five approaches will be tested on a dataset of software development projects as many as 10 dataset projects. So that it can produce new knowledge about what machine learning and non-machine learning methods are the most accurate for estimating software business. As well as knowing between the selection between using Particle Swarm Optimization (PSO) for attributes selection and without PSO, which one can increase the accuracy for software business estimation. The data mining algorithm used to calculate the most optimal software effort estimate is the Linear Regression algorithm with an average RMSE value of 1603,024 for the 10 datasets tested. Then using the PSO feature selection can increase the accuracy or reduce the RMSE average value to 1552,999. The result indicates that, compared with the original regression linear model, the accuracy or error rate of software effort estimation has increased by 3.12% by applying PSO feature selectio

Software Effort Estimation using Neuro Fuzzy Inference System: Past and Present

Most important reason for project failure is poor effort estimation. Software development effort estimation is needed for assigning appropriate team members for development, allocating resources for software development, binding etc. Inaccurate software estimation may lead to delay in project, over-budget or cancellation of the project. But the effort estimation models are not very efficient. In this paper, we are analyzing the new approach for estimation i.e. Neuro Fuzzy Inference System (NFIS). It is a mixture model that consolidates the components of artificial neural network with fuzzy logic for giving a better estimation

Numerical Simulation and Design of Ensemble Learning Based Improved Software Development Effort Estimation System

This research paper proposes a novel approach to improving software development effort estimation by integrating ensemble learning algorithms with numerical simulation techniques. The objective of this study is to design an ensemble learning-based software development effort estimation system that leverages the strengths of multiple algorithms to enhance accuracy and reliability. The proposed system combines the power of ensemble learning, which involves aggregating predictions from multiple models, with numerical simulation techniques that enable the modelling and analysis of complex software development processes. A diverse set of software development projects is collected, encompassing various domains, sizes, and complexities. Ensemble learning algorithms such as Random Forest, Gradient Boosting, Bagging, and AdaBoost are selected for their ability to capture different aspects of the data and produce robust predictions. The proposed system architecture is presented, illustrating the flow of data and components. A model training and evaluation pipeline is developed, enabling the integration of ensemble learning and numerical simulation modules. The system combines the predictions generated by the ensemble models with the simulation results to produce more accurate and reliable effort estimates. The experimental setup involves a comprehensive evaluation of the proposed system. A real-world dataset comprising historical project data is utilized, and various performance metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), are employed to assess the effectiveness of the system. The results and analysis demonstrate that the ensemble learning-based effort estimation system outperforms traditional techniques, showcasing its potential to enhance project planning and resource allocation

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Applying Absolute Residuals as Evaluation Criterion for Estimating the Development Time of Software Projects by Means of a Neuro-Fuzzy Approach

In the software development field, software practitioners expend between 30% and 40% more effort than is predicted. Accordingly, researchers have proposed new models for estimating the development effort such that the estimations of these models are close to actual ones. In this study, an application based on a new neuro-fuzzy system (NFS) is analyzed. The NFS accuracy was compared to that of a statistical multiple linear regression (MLR) model. The criterion for evaluating the accuracy of estimation models has mainly been the Magnitude of Relative Error (MRE), however, it was recently found that MRE is asymmetric, and the use of Absolute Residuals (AR) has been proposed, therefore, in this study, the accuracy results of the NFS and MLR were based on AR. After a statistical paired t-test was performed, results showed that accuracy of the New-NFS is statistically better than that of the MLR at the 99% confidence level. It can be concluded that a new-NFS could be used for predicting the effort of software development projects when they have been individually developed on a disciplined process.In the software development field, software practitioners expend between 30% and 40% more effort than is predicted. Accordingly, researchers have proposed new models for estimating the development effort such that the estimations of these models are close to actual ones. In this study, an application based on a new neuro-fuzzy system (NFS) is analyzed. The NFS accuracy was compared to that of a statistical multiple linear regression (MLR) model. The criterion for evaluating the accuracy of estimation models has mainly been the Magnitude of Relative Error (MRE), however, it was recently found that MRE is asymmetric, and the use of Absolute Residuals (AR) has been proposed, therefore, in this study, the accuracy results of the NFS and MLR were based on AR. After a statistical paired t-test was performed, results showed that accuracy of the New-NFS is statistically better than that of the MLR at the 99% confidence level. It can be concluded that a new-NFS could be used for predicting the effort of software development projects when they have been individually developed on a disciplined process

A novel framework using deep auto-encoders based linear model for data classification

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes. Keywords: deep sparse auto-encoders, medical diagnosis, linear model, data classification, PSO algorithmpublishedVersio

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure