Validation of object-oriented software GA metric selection model using domain experts

This study presents validation of object-oriented model to predict its maintainability. The study used metric threshold in its encoding strategy in the implementation of GA Model before being compared with classical model. This empirical validation was then compared with real maintainability data from experts using similar procedures. To understand the overall effect of particular software, linear discriminant analysis which is machine learning statistical method was utilised to evaluate the performance of the metrics. The results pointed out that there is significant relationship when expert’s opinions were used. Experts also indicated the role of inheritance metrics in predicting maintainability of object-oriented software which also highlighted the needs for further empirical investigation on the production of more metrics threshold that give researchers and practitioners an opportunity to work on more metrics

An Approach for the Empirical Validation of Software Complexity Measures

Software metrics are widely accepted tools to control and assure software quality. A large number of software metrics with a variety of content can be found in the literature; however most of them are not adopted in industry as they are seen as irrelevant to needs, as they are unsupported, and the major reason behind this is due to improper empirical validation. This paper tries to identify possible root causes for the improper empirical validation of the software metrics. A practical model for the empirical validation of software metrics is proposed along with root causes. The model is validated by applying it to recently proposed and well known metrics

Baseline Assisted Classification of Heart Rate Variability

Recently, among various analysis methods of physiological signals, automatic analysis of Electrocardiogram (ECG) signals, especially heart rate variability (HRV) has received significant attention in the field of machine learning. Heart rate variability is an important indicator of health prediction and it is applicable to various fields of scientific research. Heart rate variability is based on measuring the differences in time between consecutive heartbeats (also known as RR interval), and the most common measuring techniques are divided into the time domain and frequency domain. In this research study, a classifier based on analysis of HRV signal is developed to classify different activities including sleep, exam, and exercise. The performance of the classifier is improved using a novel feature construction approach named as baseline assisted classifier. ECG data are collected from 39 subjects and RR intervals are derived from ECG data using Firstbeat analysis software to compute HRV metrics. These metrics are utilized as features in a logistic regression, SVM, decision tree, random forest classifiers. Performance of all classifiers is assessed by leave one person out cross-validation technique. Features are derived by statistical time domain method from HRV segmentation during 5-minutes recording. Using a combination of 5-minutes segmentation feature vector and 5-minutes segmentation feature vector of sleep record results in a median area under the receiver operating curve (AUC) of 88% for sleep and 74% for the exam on leave one person out cross-validation test set data by SVM classifier. These results demonstrate that adding a baseline feature vector of sleep data improves the classification accuracy and classification AUC accuracy of almost all classifiers from HRV measures, and tracking of activity can be achieved by measuring the HRV signal