artifact_detection A tool for NLP tasks on textual bug reports. Automated classification of text into natural language (e.g. English in the contained datasets), and non-natural language text portions (e.g. stack traces, code snippets, log outputs, file listings, urls,) on a line by line basis. This repo contains the Python implementation of a machine learning classifier model, basic scripts for automated trainingset creation from GitHub issue tickets. Further, a scikit-learn transformer implementation wrapping pretrained models ready to be used as preprocessing step. Datasets consist of issue tickets and documentation files mined from C++, Java, JavaScript, PHP, and Python projects hosted on GitHub. Detailed discussion of this model can be found in "Detecting non-natural language artifacts for de-noising bug reports" - Hirsch T. and Hofer B. (in review). This is project is also available on GitHub: https://github.com/AmadeusBugProject/artifact_detection