Abstract
Source Code Authorship Attribution is a problem that is lately studied more often due improvements in Deep Learning techniques. Among existing solutions, two common issues are inability to add new authors without retraining and lack of interpretability. We address both these problem. In our experiments, we were able to correctly classify 75% of authors for diferent programming languages. Additionally, we applied techniques of explainable AI (XAI) and found that our model seems to pay attention to distinctive features of source code.
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