SELF-EVALUATION METHODOLOGY OF PROGRAMMERS' COMPETITIVENESS IN THE CONTEXT OF LIFELONG EDUCATION AND PROFESSIONAL SELF-DEVELOPMENT

Contemporary social sciences, including pedagogy and psychology, carry out researches in the field of competitiveness. The more competitive each member of the society is, the more competitive is the society as such. The aim of research was: experimentally approbate the developed methodology for evaluating the competitiveness of programmers. Research methods were: survey as pedagogical experiment, projective method for data obtaining; Wilcoxon test for data processing. The results of the  pedagogical experiment testify that: during the pedagogical experiment,  research participants changed their competitiveness self-assessment. Therefore it is very important to know self-evaluation indicators of professional development because it significant impacts both: programmers notions about professional self-development and competitiveness as result of this development, changing competitiveness self-assessment as well. During the experiment, the participants of the research gained new experience of reflection, reflecting on their professional development, including professionalism, career, in past, present and future, and this reflection experience can serve as a basis for programmer's professional development and adequate competitiveness self-assessment. The experimentally approbated self-assessment methodology of professional development and competitiveness is valid and can be used in further research.

Code authorship attribution using content-based and non-content-based features

Machine learning approaches are widely used in natural language analysis. Previous research has shown that similar techniques can be applied in the analysis of computer programming (artificial) languages. In this thesis, we focus on identifying the authors of computer programs by using machine learning techniques. We extend these techniques to determine which features capture the writing style of authors in the classification of a computer program according to the author's identity. We then propose a novel approach for computer program author identification. In this method, program features from the text documents are combined with authors' sociological features (gender and region) to develop the classification model. Several experiments have been conducted on two datasets composed of computer programs written in C++, and the results are encouraging. According to the experimental results, the author's identity can be predicted with a $75\%$ accuracy rate