Browsing by Author "Ayyildiz, Tulin Ercelebi"

Now showing 1 - 6 of 6

Correlations Between Problem Domain and Solution Domain Size Measures for Open Source Software
(2014) Ayyildiz, Tulin Ercelebi; Kocyigit, Altan; https://orcid.org/0000-0002-7372-0223; AAE-1726-2021
Predicting how much effort will be required to complete a software project as early as possible is a very important factor in the success of software development projects. Including function points and its variants, there are several size measures and corresponding measurement methods that can be used for effort estimation. However, in most of the projects, there is limited amount of information available in the early stages and significant effort is spent for size measurement and effort estimation with such methods. This paper analyzes the correlation between the size metrics of conceptual model of the problem domain and the resulting software. For this purpose, we consider open source project management and game software. We apply linear regression and cross validation techniques to investigate the relation between the sizes of problem domain (i.e., conceptual) and solution domain (i.e., design) models. The results reveal a high correlation between the number of conceptual classes in the problem domain model and the number of software classes constituting the corresponding software. The results suggest that it is possible to use problem domain descriptions in the early stages of software development projects to make plausible predictions for the size of the software.
An Empirical Study on Software Test Effort Estimation for Defense Projects
(2022) Cibir, Esra; Ayyildiz, Tulin Ercelebi
Effort estimation of software testing plays a vital role in the effective completion of testing. In particular, software test effort estimation in defense projects is not an easy and simple phenomenon, owing to internal and external factors. This study aims to investigate the relationships between software test metrics used in the industry and software testing efforts. A method for estimating the testing effort is proposed using a novel set of software testing metrics that have not been used in any previously proposed software test effort estimation methods. In this study, 15 completed software projects of a CMMI Level-3 certified defense industry company were analyzed. The results of the empirical study show that the proposed method with the given metrics provides acceptable prediction quality, with Pred (0.25) and Pred (0.30) values equal to 0.867. We obtained plausible results and demonstrated that our newly proposed metrics can be safely used for software test effort estimation.
Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers
(2022) Tekincan, Erdogann Berkay; Ayyildiz, Tulin Ercelebi; Ayyildiz, Nizam
Radar warning receivers are real-time systems used to detect emitted signals by the enemy targets. The conventional method of detecting the signal is to determine the noise floor and differentiate the signals above the noise floor by setting a threshold value. The common methodology for detecting signals in noisy environment is Constant False Alarm Rate (CFAR) detection. In CFAR methodology, threshold level is determined for a specified probability of false alarm. CFAR dictates the signal power to be detected is higher than the noise floor, i.e. signal-to-noise ratio (SNR) should be positive. To detect radar signals for negative SNR values machine learning techniques can be used. It is possible to detect radar signals for negative SNR values by Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). In this study, we evaluated whether LSTM ANN can replace the CFAR algorithm for signal detection in real-time radar receiver systems. We implemented a Field Programmable Gate Array (FPGA) based LSTM ANN architecture, where pulse signal detection could be performed with 94% success rate at -5 dB SNR level. To the best of our knowledge our study is the first where LSTM ANN is implemented on FPGA for radar warning receiver signal detection.
Predicting Diabetes Using Machine Learning Techniques
(2022) Kirgil, Elif Nur Haner; Erkal, Begum; Ayyildiz, Tulin Ercelebi; 0000-0002-7372-0223; JBI-6492-2023
Early diagnosis of diabetes, which can cause death, is very important for the health of the person. In the literature, machine learning techniques are frequently used in diagnosis of many diseases, including diabetes. The aim of the study is to predict diabetes with high accuracy by using machine learning and preprocessing techniques. Pima Indian Diabetes dataset was used in the study. J48 (Decision Tree), Naive Bayes, Support Vector Machine, Logistic Regression, Multilayer Perceptron, K Nearest Neighbor, Logistic Model Tree, and Random Forest were used for classification. Of the preprocessing methods, feature selection, imputing missing values, normalization and standardization are performed. According to the results obtained, the highest accuracy value got with the Random Forest algorithm as 80.869.
Size and Effort Estimation Based on Problem Domain Measures for Object-Oriented Software
(2018) Ayyildiz, Tulin Ercelebi; Kocyigit, Altan; https://orcid.org/0000-0002-7372-0223; AAE-1726-2021
This paper analyzes the correlations between the problem domain measures such as the number of distinct nouns and distinct verbs in the requirements artifacts and the solution domain measures such as the number of software classes and methods in the corresponding object-oriented software. For this purpose, 14 completed software development projects of a CMMI Level-3 certified defense industry company have been analyzed. The observed strong correlation is taken as the indication of linear relationship between the measures and a size estimation model based on linear regression analysis is proposed. Prediction performance of the method is analyzed on the 14 software projects. Moreover, it has been observed that there is a high correlation between the problem domain measures and the development effort. Therefore, the linear regression analysis is also used to estimate the effort in terms of the problem domain measures. The effort estimations are also evaluated and compared with the efforts predicted using the size measured by the COSMIC Function Point (CFP) method. The results show that the proposed method provides more accurate effort estimates compared to the effort estimated by using CFP size measurement.
Using Machine Learning Methods in Early Diagnosis of Breast Cancer
(2021) Erkal, Begum; Ayyildiz, Tulin Ercelebi; https://orcid.org/0000-0002-7372-0223; JBI-6492-2023
Breast cancer is one of the most important health diseases to be treated in the world, and it is a subject that has a wide place in research subjects. In this study, results are provided by using seven different machine learning techniques for the classification of breast cancer. In order to obtain better results, the preprocessing method was applied. As a result, when compared with some studies in the literature, it was observed that the general performance of some of the methods improved. In the experimental results, BayesNet was found to be the best classification method with an accuracy rate of 97.13%.