Next-Generation Bug Reporting: Enhancing Development with AI Automation

In today's Agile and DevOps-driven software development landscape, rapid and accurate bug reporting is critical. This paper presents a next-generation automation tool powered by large language models and machine learning, innovating the bug reporting process. The tool automates various phases of bug reporting, such as failure detection, severity assessment, duplicate detection, and report generation. By addressing the limitations of manual bug reporting such as inconsistency, scalability challenges, and time inefficiencies, the proposed solution enhances the software testing workflow. Initial findings demonstrate significant time savings, reduced manual errors, and improved collaboration between testers and developers. This work establishes a foundation for fully automated bug reporting, poised to accelerate software development cycles and maintain high-quality standards.

Authors

Institutions

Publication Details

Published
2025-02-20
DOI
https://doi.org/10.1109/icsc64553.2025.10968932
Citations
2
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
8.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Next-Generation Bug Reporting: Enhancing Development with AI Automation

Aryan Jadon, Avinash Patil
2 citations
Scientific Computing and Data Management
8.34
article

Next-Generation Bug Reporting: Enhancing Development with AI Automation

Aryan Jadon, Avinash Patil
article en
2 citations

Abstract

In today's Agile and DevOps-driven software development landscape, rapid and accurate bug reporting is critical. This paper presents a next-generation automation tool powered by large language models and machine learning, innovating the bug reporting process. The tool automates various phases of bug reporting, such as failure detection, severity assessment, duplicate detection, and report generation. By addressing the limitations of manual bug reporting such as inconsistency, scalability challenges, and time inefficiencies, the proposed solution enhances the software testing workflow. Initial findings demonstrate significant time savings, reduced manual errors, and improved collaboration between testers and developers. This work establishes a foundation for fully automated bug reporting, poised to accelerate software development cycles and maintain high-quality standards.

Juniper Networks (United States) (US)
Openalex Percentile: Top 3%
Scientific Computing and Data Management
8.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.