Damjan Angelovski
ABSTRACT
As the demand for software engineers rises, so does the demand for their education. With the increasing number of students, educators struggle to keep up. We aim to ease their burden by providing a new tool for semiautomatic source code assessment, named DEMAx. It analyzes C/C++ source codes and their test case results and with the help of machine learning, provides information on the likelihood that a submission should be manually assessed.
In this paper we present a tool with the focus on the new improvements of our previous work that include direct static analysis of non-compiling code and ranking metrics of the source codes. At the end, we present the results of the improved model on the testing data, which are solid ground for the use of our tool.
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