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

Landslides are a significant natural disaster threat in Indonesia, including Samarinda City. This study aims to evaluate the classification accuracy of landslides using the Naïve Bayes algorithm, combined with the Synthetic Minority Oversampling Technique (SMOTE) for class imbalance and Particle Swarm Optimization (PSO) for feature Selection and parameter optimization. Data were obtained from BPBD and BMKG Samarinda for 2022-2024, evaluated using 10-Fold Cross Validation and Confusion Matrix. The results show that SMOTE did not always improve model accuracy. Naïve Bayes without SMOTE achieved an average accuracy of 87.18%, while with SMOTE, it decreased to 70.55%. This indicates that SMOTE’s impact depends on the dataset. PSO, used for feature Selection and parameter optimization, gave mixed results. Naïve Bayes + PSO Feature Selection achieved an average accuracy of 68.25%, lower than the base model with SMOTE. Naïve Bayes + PSO Optimization achieved the best accuracy at 71.03%, though the improvement was minimal. The combination of Naïve Bayes + PSO Feature Selection + PSO Optimization had the lowest accuracy at 68.08%. This study emphasizes that the use of PSO for feature Selection and optimization needs careful consideration, especially when combined with SMOTE, as its effect on model performance is not always optimal.

In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.

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

Software cost and time estimation is the process of estimating the cost and time required to develop a software system. Software cost and time estimation supports the planning and tracking of software projects. Effectively controlling the expensive investment of software development is one of the important issues in software project management. Estimating software development cost with high precision is still a great challenge for project managers, because it allows for considerable financial and strategic planning. Software cost estimation refers to the predictions of the likely amount of effort, time, and staffing levels required to build a software system. A very helpful form of cost estimation is the one made at an early stage during a project, when the costing of the project is proposed for approval. However, estimates at the early stages of the development are the most difficult to obtain. In this paper a novel Constructive Cost Model (COCOMO) based on soft computing approach is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.

In this paper, a novel classifier based on Robert’s similarity measure is introduced for emotion detection using electroencephalogram (EEG) signals. Traditional machine learning classifiers machine learning such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF), often struggle to accurately capture both linear and nonlinear patterns in EEG signals and face limitations in handling high-dimensional datasets. The proposed classifier addresses these challenges by segmenting EEG signals into block sizes categorized as small (1 to 10 samples), medium (20 to 100 samples), and large (200 to 1,000 samples), demonstrating particularly strong performance with medium and large block sizes to capture essential features. Integration of Particle Swarm Optimization (PSO) for feature selection, with Robert’s similarity as the fitness function, effectively refines the feature set, boosting classification accuracy and computational efficiency. Evaluation on an EEG brainwave dataset demonstrated that the method achieved an accuracy of 98.75% with feature selection, compared to 94.04% without it in emotional state detection. The results demonstrate that the proposed classifier is a valuable tool for diverse fields, including healthcare by detecting patient stress, education by assessing student engagement, customer service by monitoring satisfaction, and smart environments by enabling adaptive responses. Furthermore, the classifier has potential for broader industrial applications, such as improving workplace productivity by monitoring employee stress and enhancing safety in autonomous vehicle systems, making it a versatile solution for emotionally-aware systems across multiple domains.

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.

Software cost estimation is a process of planning, risk analysis, and decision making for project management in software development. Cost of project development encompasses a software project’s effort and development time. One popular model of software cost estimation is constructive cost model (COCOMO) model, which is a mathematical model proposed by Boehm, used for estimate the software effort and development time. The objective of this paper is to improve the basic COCOMO model’s coefficients for modern programming languages like Python, R, C++, etc. Many techniques were presented in the past for effort and time estimation using machine learning. But all these techniques were trained and tested for older programming languages. In order to improve the accuracy of COCOMO for modern programming languages, six Python projects have been considered and genetic algorithm (GA) is applied in these projects to define new values for basic COCOMO coefficients and the development time is calculated for Python projects. The time estimated using GA coefficients is compared with the original COCOMO and actual time. Using mean magnitude relative error, the error from the original COCOMO time is 54.49% and error from GA time is 21.23%.

Purpose The study aims to investigate the application of the data element market in software project management, focusing on improving effort estimation by addressing challenges faced by traditional methods. Design/methodology/approach This study proposes a solution based on feature selection, utilizing the data element market and reinforcement learning-based algorithms to enhance the accuracy of software effort estimation. It explores the application of the MARLFS algorithm, customizing improvements to the algorithm and reward function. Findings This study demonstrates that the proposed approach achieves more precise estimation compared to traditional methods, leveraging feature selection to guide project management in software development. Originality/value This study contributes to the field by offering a novel approach that com-bines the data element market, machine learning, and feature selection to improve software effort estimation, addressing limitations of traditional methods and providing insights for future research in project management.

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.

Breast cancer has been one of the leading causes of death among women in the world. Early detection of this disease can save patient’s lives and reduce mortality. Due to the large number of features involved in the diagnosis of this disease, the breast cancer diagnosis process can be time consuming. To reduce cost and time and improving accuracy of breast cancer diagnosis, this paper propose a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods for selection the most effective features for breast cancer diagnosis among all features. In order to evaluate the efficiency of the proposed feature selection method, it was tested on three most common breast cancer datasets available in the University of California, Irvine (UCI) repository named: Coimbra dataset (CD), Wisconsin Diagnostic Breast Cancer dataset (WDBC) and Wisconsin Prognostic Breast Cancer dataset (WPBC). In the Coimbra dataset with all its 9 features and without PSO feature selection algorithm the highest obtained accuracy was 87% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 91% and the number of features was reduced from 9 to 4. In the WDBC dataset with all its 30 features and without PSO feature selection algorithm the highest obtained accuracy was 99% by Random Forest method, while with PSO feature selection algorithm the accuracy reached to 100% and the number of features was reduced from 30 to 19. In the WPBC dataset with all its 33 features and without PSO feature selection algorithm the highest obtained accuracy was 94% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 96% and the number of features was reduced from 33 to 17. The results of this paper indicated that the proposed feature selection algorithm based on PSO algorithm can improve the accuracy of breast cancer diagnosis. While it has selected fewer and more effective features than the total number of features in the original datasets.

Estimation of Software Development Effort from Requirements Based Complexity

This study focuses on the visitors of Pohon Cinta beach tourist area. This beach is one of the potential tourism objects in Pohuwato Regency. The main problem that frequently occurs is that many visitors cannot directly convey their impression when visiting and enjoying the beauty of the Pohon Cinta beach. The government needs to know the level of visitor satisfaction to attempt to improve and develop the Pohon Cinta beach tourist attraction. Thus, to solve the problem above, a method that can help predict visitor satisfaction is needed. This study aims to measure visitor satisfaction through predictions using the Backpropagation algorithm and PSO feature selection to assist the government in developing tourism potential in Pohuwato Regency. The method used is the backpropagation algorithm for prediction and Particle Swarm Optimization which is considered effective in overcoming optimization problems. This algorithm is considered capable of solving problems in the backpropagation algorithm. The accuracy value of the backpropagation algorithm model is 84.67%, the accuracy value of the PSO-based backpropagation algorithm model is 85.00%, and the difference in accuracy is 0.33. The results of the application of the Backpropagation algorithm and Particle Swarm Optimization can increase the predictive accuracy value of visitor satisfaction at the Cinta Tree Beach tourist attraction.

The detection of fraudulent activities in credit cards transactions presents a significant challenge due to the constantly changing and unpredictable tactics used by fraudsters, who take advantage of technological advancements to evade security measures and cause substantial financial harm. In this paper, we suggested a machine learning based methodology to detect fraud in credit cards. The suggested method contains four key phases, including data normalization, data preprocessing, feature selection, classification. For classification artificial neural network, decision tree, logistic regression, naive bayes, random forest while for feature selection particle swarm optimization is employed. With the use of a dataset created from European cardholders, the suggested method was tested. The experimental results show that the suggested method beats the other machine learning techniques and can successfully classify frauds with a high detection rate.

Women who have recovered from breast cancer (BC) always fear its recurrence. The fact that they have endured the painstaking treatment makes recurrence their greatest fear. However, with current advancements in technology, early recurrence prediction can help patients receive treatment earlier. The availability of extensive data and advanced methods make accurate and fast prediction possible. This research aims to compare the accuracy of a few existing data mining algorithms in predicting BC recurrence. It embeds a particle swarm optimization as feature selection into three renowned classifiers, namely, naive Bayes, K-nearest neighbor, and fast decision tree learner, with the objective of increasing the accuracy level of the prediction model.