Testing the Performance of Automated Documentation Generation with Included Inline Comments

Testing the Performance of Automated Documentation Generation with Included Inline Comments

Morkūnas, Balys (TU Delft Electrical Engineering, Mathematics and Computer Science)

Panichella, A. (mentor)
Applis, L.H. (mentor)
Gerritsen, B.H.M. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-24

A number of Machine Learning models utilize source code as training data for automating software development tasks. A common trend is to omit inline comments from source code in order to unify and standardize the examples, even though the additional information can capture important aspects and better explain algorithms. We claim that models, utilizing the supplementary data, are able to produce more fluent translations for Automatic Documentation Generation task. We test this by creating two datasets and measuring the performance difference. The results show that there is a slight improvement in translation accuracy when a dataset contains inline comments, with stop words removed. Further research needs to be done to optimize the preprocessing of data and to more accurately detect the scope of inline comments.

http://resolver.tudelft.nl/uuid:57ce8926-5f14-482c-9aba-e6fa9885ba6d

Student theses

bachelor thesis

© 2022 Balys Morkūnas