# Identifying non-natural language artifacts in bug reports

arXiv: https://arxiv.org/abs/2110.01336 · HTML: https://mlsanity.com/paper/2110.01336/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging](https://mlsanity.com/paper/1801.01275/) (2018)
- [Natural Language or Not (NLoN) - A Package for Software Engineering Text Analysis Pipeline](https://mlsanity.com/paper/1803.07292/) (2018)
- [A large-scale, in-depth analysis of developers' personalities in the Apache ecosystem](https://mlsanity.com/paper/1905.13062/) (2019)

## Built on by (most cited first, 1 of 1)

- [Auto-labelling of Bug Report using Natural Language Processing](https://mlsanity.com/paper/2212.06334/) (2022)

## Abstract (arXiv)

Bug reports are a popular target for natural language processing (NLP). However, bug reports often contain artifacts such as code snippets, log outputs and stack traces. These artifacts not only inflate the bug reports with noise, but often constitute a real problem for the NLP approach at hand and have to be removed. In this paper, we present a machine learning based approach to classify content into natural language and artifacts at line level implemented in Python. We show how data from GitHub issue trackers can be used for automated training set generation, and present a custom preprocessing approach for bug reports. Our model scores at 0.95 ROC-AUC and 0.93 F1 against our manually annotated validation set, and classifies 10k lines in 0.72 seconds. We cross evaluated our model against a foreign dataset and a foreign R model for the same task. The Python implementation of our model and our datasets are made publicly available under an open source license.