JITA

JITA Journal of Information Technology and Applications

Vol. 2 No. 1 (2012): JITA - APEIRON

Lev Faynshteyn, Vojislav B. Mišić, Jelena Mišić

Analyzing the Cost and Benefit of Pair Programming Revisited

Original scientific paper

DOI: https://doi.org/10.7251/JIT1201014F

Abstract

Pair programming has received a lot of attention from both industry and academia, but most paper focus on its technical aspects, while its business value has received much less attention.  In this paper, we focus on the business aspects of pair programming, by using a number of software development related met rics, such as pair speed advantage, module breakdown structure  of the software and project value discount rate, and augmenting them by taking into account the cost of change after the initial product release and inherent non-linearity of the discount rate curves. The proposed model allows for a more realistic estimation of the final project value, and the results of System Dynamics simulations demonstrate some useful insights for software development management.

Keywords: Pair Programming, Extreme Programming (XP), System Dynamics, Waterfall, Cost of Change

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.