Poster: LWE: LDA Refined Word Embeddings for Duplicate Bug Report Detection

Bug reporting is a major part of software maintenance and due to its inherently asynchronous nature, duplicate bug reporting has become fairly common. Detecting duplicate bug reports is an important task in order to avoid the assignment of a same bug to different developers. Earlier approaches have...

Full description

Saved in:
Bibliographic Details
Main Authors: Budhiraja, Amar, Reddy, Raghu, Shrivastava, Manish
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Computer bugs
Duplicate Bug Report Detection
LDA
Machine Learning
Mathematical model
Resource management
Systems architecture
Task analysis
Topic Models
Word Embeddings
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Bug reporting is a major part of software maintenance and due to its inherently asynchronous nature, duplicate bug reporting has become fairly common. Detecting duplicate bug reports is an important task in order to avoid the assignment of a same bug to different developers. Earlier approaches have improved duplicate bug report detection by using the notions of word embeddings, topic models and other machine learning approaches. In this poster, we attempt to combine Latent Dirichlet Allocation (LDA) and word embeddings to leverage the strengths of both approaches for this task. As a first step towards this idea, we present initial analysis and an approach which is able to outperform both word embeddings and LDA for this task. We validate our hypothesis on a real world dataset of Firefox project and show that there is potential in combining both LDA and word embeddings for duplicate bug report detection.
ISSN:2574-1934
DOI:10.1145/3183440.3195078