Auto-labelling of Bug Report using Natural Language Processing

TL;DR

This paper presents an NLP-based method for automatically labeling bug reports by leveraging report attributes and deep learning, significantly improving duplicate detection accuracy in bug tracking systems.

Contribution

It introduces a novel combination of NLP techniques, a custom data transformer, and a deep neural network for more accurate duplicate bug report retrieval.

Findings

Achieves 70% recall@5 in duplicate bug report retrieval

Effectively utilizes structured and unstructured bug report attributes

Demonstrates high accuracy on large bug report datasets

Abstract

The exercise of detecting similar bug reports in bug tracking systems is known as duplicate bug report detection. Having prior knowledge of a bug report's existence reduces efforts put into debugging problems and identifying the root cause. Rule and Query-based solutions recommend a long list of potential similar bug reports with no clear ranking. In addition, triage engineers are less motivated to spend time going through an extensive list. Consequently, this deters the use of duplicate bug report retrieval solutions. In this paper, we have proposed a solution using a combination of NLP techniques. Our approach considers unstructured and structured attributes of a bug report like summary, description and severity, impacted products, platforms, categories, etc. It uses a custom data transformer, a deep neural network, and a non-generalizing machine learning method to retrieve existing identical bug reports. We have performed numerous experiments with significant data sources containing thousands of bug reports and showcased that the proposed solution achieves a high retrieval accuracy of 70% for recall@5.