RTFM: Towards Understanding Source Code using Natural Language Processing

Maximilian Galanis, Vincent Dietrich, Bernd Kast, Michael Fiegert

2020

Abstract

The manual configuration of today’s autonomous systems for new tasks is becoming increasingly difficult due to their complexity. One solution to this problem is to use planning algorithms that can automatically synthesize suitable data processing pipelines for the task at hand and thus simplify the configuration. Planners usually rely on models, which are created manually based on already existing methods. These methods are often provided as part of domain specific code libraries. Therefore, using existing planners on new domains requires the manual creation of models based on the methods provided by other libraries. To facilitate this, we propose a system that generates an abstract semantic model from C++ libraries automatically. The necessary information is extracted from the library using a combination of static source code analysis to analyze its header files and natural language processing (NLP) to analyze its official documentation. We evaluate our approach on the perception domain with two popular libraries: HALCON and OpenCV. We also outline how the extracted models can be used to configure data processing pipelines for the perception domain automatically by using an existing planner.

Paper Citation

in Harvard Style

Galanis M., Dietrich V., Kast B. and Fiegert M. (2020). RTFM: Towards Understanding Source Code using Natural Language Processing.In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-442-8, pages 430-437. DOI: 10.5220/0009826604300437

in Bibtex Style

@conference{icinco20,
author={Maximilian Galanis and Vincent Dietrich and Bernd Kast and Michael Fiegert},
title={RTFM: Towards Understanding Source Code using Natural Language Processing},
booktitle={Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2020},
pages={430-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009826604300437},
isbn={978-989-758-442-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - RTFM: Towards Understanding Source Code using Natural Language Processing
SN - 978-989-758-442-8
AU - Galanis M.
AU - Dietrich V.
AU - Kast B.
AU - Fiegert M.
PY - 2020
SP - 430
EP - 437
DO - 10.5220/0009826604300437