[서평]「The Unified Modeling Language User Guide」

[서평]「Component Software」 - Beyond Object-Oriented Programming -

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

Metrics and Models in Software Quality Engineering

Software testing is any activity aimed at evaluating an attribute or capability of a program or system and determining that it meets its required results. Although crucial to software quality and widely deployed by programmers and testers, software testing still remains an art, due to limited understanding of the principles of software. The difficulty in software testing stems from the complexity of software: we can not completely test a program with moderate complexity. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Testing can be used as a generic metric as well. Correctness testing and reliability testing are two major areas of testing. Software testing is a trade-off between budget, time and quality.

Software testing

Positive and Negative emotions are experienced by the majority of individuals in their day-to-day life. It is important to control access of negative emotions because it may lead to several chronic health issues like depression and anxiety. The purpose of this research work is to develop a portable brainwave driven system for recognizing positive, negative, and neutral emotions. This research considers the classification of four negative class of emotions using genres sadness, disgust, angry, and surprise along with the classification of three basic class of emotions i.e., positive, negative, and neutral. This paper introduces a long short term memory deep learning (LSTM) network to recognize emotions using EEG signals. The primary goal of this approach is to assess the classification performance of the LSTM model. The secondary goal is to assess the human behavior of different age groups and gender. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), LIB-Support Vector Machine (LIB-SVM), and LSTM based deep learning model for classification. The analysis shows that, for four class of emotions LSTM based deep learning model provides classification accuracy as 83.12%, 86.94%, 91.67%, and 94.12% for 50–50, 60–40, 70–30, and 10-fold cross-validations. For three class of emotions LSTM based deep learning model provides classification accuracy as 81.33%, 85.41%, 89.44%, and 92.66% for 50–50, 60–40, 70–30, and 10-fold cross-validation. The generalizability and reliability of this approach are evaluated by applying our approach to publicly available EEG datasets DEAP and SEED. In compliance with the self-reported feelings, brain signals of 18–25 years of age group provided the highest emotional identification. The results show that among genders, females are more emotionally active as compared to males. These results affirmed the potential use of our method for recognizing positive, negative, and neutral emotions.

A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system

Stakeholder satisfaction is the main motive of the software industry. Consumer and producer satisfaction means achieving a software-quality product. There are so many techniques, methods, and models are present for the software-quality prediction. Computational intelligence is also playing a crucial role in the prediction of quality characteristics. This paper gives a new optimal mathematical model for the prediction of the degree of stakeholder satisfaction (Q). Optimal models validate the real data using the relationship impacts of various quality attributes. It uses the equations of constraints. The optimal model gives the maximum and minimum values for Q. Constraints constitutes of software quality characteristics. In the given case study example, the idle value of Q is 30 but using optimal model it gives the maximum optimal value of Q = 22.788020075 for xLAB IT consulting services on their project In-Reg Molecule Registration using the MATLAB. It means if there is any change in the value of any software quality characteristics, then it will decrease the value of Q. It proves that the given result is an optimal solution.

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An Optimization Model for Software Quality Prediction With Case Study Analysis Using MATLAB

Software engineering is the process of developing software by utilizing applications of computer engineering. In the present day, predicting the reliability of the software system become a recent issue and an attractive issue for the research area in the field of software engineering. Different techniques have been applied to estimate and predict the reliability of a system. To make new software from the beginning is a difficult task. Component-Based Software Engineering (CBSE) helps in minimizing these efforts in making new software because it utilizes factors like reusability, component dependency, and component interaction that results in decreasing complexity of the system. Soft computing may be applied to estimate reliability. A new model is proposed to estimate the reliability of Component-based Software (CBS) using series and parallel reliability models and later on, the proposed component-based software reliability model is evaluated using two soft computing techniques- Fuzzy Logic and PSO. The experimental results conclude that the proposed reliability model has a lower error rate in predicting CBSE reliability as compared to reliability prediction utilizing fuzzy logic and PSO.

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A New Model for Predicting Component-Based Software Reliability Using Soft Computing

In the smart cities, objects can smartly communicate with the people through Internet of Things (IoT). It will make smart cities a greener place by detecting pollution through IoT and environmental sensors. In order to maintain the sustainability of green place in smart cities, the emerging technology, i.e. Green IoT automatically and intelligently makes smart cities sustainable in a collaborative manner. Governments and a lot of organizations around the world are doing a lot of efforts to campaign the importance of the reduction of energy consumption and carbon production as well as emphasize on the Green IoT for smart cities. Various IoT related smart cities architectures are already presented in literature. But this work presents the concept of the “Green IoT” to create a green environment which will apprehend the idea of energy saving in smart cities. In this chapter, design of Green IoT architecture is proposed for smart cites with the focus to reduce energy consumption at each stage and ensure realization of IoT toward green. This proposed Green IoT architecture is based on the cloud based system which reduces the hardware consumption.

Design of Cloud-Based Green IoT Architecture for Smart Cities

Regression testing is one of the types of maintenance testing and it is performed in case of bug-fixing or whenever there is any new functionality incorporate in the dynamic environment of software...

Pradeep Tomar

Papers

A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system

An Optimization Model for Software Quality Prediction With Case Study Analysis Using MATLAB

A New Model for Predicting Component-Based Software Reliability Using Soft Computing

Design of Cloud-Based Green IoT Architecture for Smart Cities

Hybridization of Cuckoo-ACO algorithm for test case prioritization