Automatic detection of constraints in software documentation

Software documentation is an important resource when maintaining and evolving software, as it supports developers in program understanding. To keep it up to date, developers need to verify that all the constraints affected by a change in source code are consistently described in the documentation. The process of detecting all the constraints in the documentation and cross-checking the constraints in the source code is time-consuming. An approach capable of automatically identifying software constraints in documentation could facilitate the process of detecting constraints, which are necessary to cross-check documentation and source code. In this thesis, we explore different machine learning algorithms to build binary classification models that assign sentences extracted from software documentation to one of two categories: constraints and non-constraints. The models are trained on a data set that consists of 368 manually tagged sentences from four open-source software systems. We evaluate the performance of the different models (Decision tree, Naive Bayes, Support Vector Machine, Fine-tuned BERT) based on precision, recall and F1-score. Our best model (i.e., a decision tree featuring bigrams) was able to achieve 74.0% precision, 83.8% recall and an F1-score of 0.79. This suggests that our results are promising and that it is possible to build machine learning based models for the automatic detection of constraints in the software documentation.

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2021

Authors dc:creator

• Ghosh, Joy, author
• Moreno Cubillos, Laura, advisor
• Ghosh, Sudipto, committee member
• Vijayasarathy, Leo, committee member

Statement dc:rights

• Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.02541

OAI identifier oai:identifier

oai:mountainscholar.org:10217/234207