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Statistical Analysis of Accident proneness of Drivers

Journal of Traffic and Transportation Engineering 2024年第2期12卷 81-91页

作者： S.V.Gerus Kotelnikov Institute of Radioengineering and Electronics(Fryazino branch)of the RA Moscow Oblast 141190 Russia

A mathematical model describing the risks of road accidents has been built on the basis of statistical data of drivers’ accident rate. It has been revealed that drivers can be divided by the degree of their accident proneness into four categories with sharply differing probabilities of road accidents. It has been shown that there is a possibility of classification of drivers in accordance with specified categories.

关键词： Accident probability driver category classification proneness

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Static Analysis and Code Complexity Metrics as Early Indicators of Software Defects

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Journal of Software Engineering and Applications 2018年第4期11卷 153-166页

作者： Safa Omri Pascal Montag Carsten Sinz Daimler AG Boeblingen Germany Karlsruhe Institute of Technology Karlsruhe Germany

Software is an important part of automotive product development, and it is commonly known that software quality assurance consumes considerable effort in safety-critical embedded software development. Increasing the effectiveness and efficiency of this effort thus becomes more and more important. Identifying problematic code areas which are most likely to fail and therefore require most of the quality assurance attention is required. This article presents an exploratory study investigating whether the faults detected by static analysis tools combined with code complexity metrics can be used as software quality indicators and to build pre-release fault prediction models. The combination of code complexity metrics with static analysis fault density was used to predict the pre-release fault density with an accuracy of 78.3%. This combination was also used to separate high and low quality components with a classification accuracy of 79%.

关键词： Static Analysis Tools Complexity Metrics Software Quality Assurance Statistical Methods Fault proneness

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Personality Traits and Facebook Use: The Combined/Interactive Effect of Extraversion, Neuroticism and Conscientiousness

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Social Networking 2014年第5期3卷 211-219页

作者： Louise La Sala Jason Skues Sharon Grant Department of Psychological Sciences and Statistics Swinburne University of Technology Melbourne Australia

This study examined the influence of Big Five personality traits on Facebook usage and examined the interactions of traits in this context based on Torgersen’s (1995) typological approach. The effect of self-esteem, narcissism, loneliness, shyness and boredom proneness on Facebook usage was also investigated. The sample included both student (N = 190) and (N = 184) non-student samples. Narcissism was the strongest predictor of time spent on Facebook per day for both students and non-students. Narcissism was also the strongest predictor of number of daily logins for non-students, however, agreeableness was the strongest predictor of logins for students. Extraversion was the strongest predictor of number of Facebook friends for both students and non- students, however the interaction of Extraversion and Neuroticism was also a predictor of Facebook friends for students, and the interaction of Extraversion and Conscientiousness for non-students. Future research should consider the combined effect of personality traits on overall Facebook use.

关键词： Facebook Personality Five Factor Model Psychological Characteristics Narcissism Boredom proneness

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