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Vol. 6 No. 2 (2018): JITA - APEIRON

Ksenija Živković, Ivan Milenković, Dejan Simić

Using open source software for web application security testing

Original scientific paper

DOI: https://doi.org/10.7251/JIT1602086Z

Abstract

Web applications are a standard part of our everyday lives. Their purpose can vary significantly, from e-banking to social networks. However, one thing is similar – users have generally high expectations from different web applications. To assure such high expectations, proper web application testing is necessary. Non-functional testing is an important part of web application testing. As technology advances and requirements become more complex, the importance of non-functional application aspects becomes even greater. It is necessary to identify non-functional requirements of web applications which are important to users, implement those requirements and test them.

Keywords: non-functional testing, web applications, testing tools.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.