Bug Reports with AI Powered S2R Extraction

Introduction

Bug reports are crucial in identifying and correcting faults during software development. However, many reports still lack clear Steps to Reproduce (S2Rs), making replicating bugs challenging. Missing or ambiguous S2Rs lead to delayed resolutions, unresolved issues, and increased developer workload.

AI-powered solutions are transforming the analysis of bug reports, and AstroBR is leading the way. Utilizing GPT-4 and graph-based user interface analysis, AstroBR effectively extracts, maps, and enhances S2Rs, making reproducing bugs more accurate and efficient.

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The Role of Bug Reports in Software Development

A well-crafted bug report should include the following key elements:

Observed Behavior (OB): What occurs when the bug is encountered?
Expected Behavior (EB): What should occur under normal circumstances?
Steps to Reproduce (S2Rs): A detailed list of actions to replicate the bug.

Ineffectively written bug reports can result in delays, misunderstandings, and unresolved issues. Extracting the Steps to Reproduce can be particularly challenging, as users often describe problems differently.

AstroBR

AstroBR is an advanced AI tool designed to identify and extract Software-to-Requirements (S2Rs) by leveraging the capabilities of GPT-4. It not only categorizes these S2Rs but also maps them to actual Graphical User Interface (GUI) interactions through a sophisticated execution model. Additionally, AstroBR assesses the quality of the identified S2Rs, ensuring that they meet specific standards. To enhance usability, it also suggests any missing steps necessary for a more comprehensive understanding or implementation of the requirements.

How It Works?

AstroBR works through a four-step process:

S2R Extraction – Uses GPT-4 to classify and extract S2Rs.
Dynamic GUI Mapping – Creates an execution model linking S2Rs to GUI actions.
Quality Evaluation – It identifies unclear, incorrect, or missing steps.
Feedback & Improvement – It produces formatted reports with actionable insights.

Comparison with Traditional Bug Report Analysis

table

Key Features of AstroBR

LLM-Powered S2R Extraction
- Uses GPT-4 to classify S2Rs from bug reports.
Graph-Based UI Analysis
- Builds a directed graph of app interactions.
Automated S2R Quality Evaluation
- Detects Correct Steps (CS), Ambiguous Steps (AS), Vocabulary Mismatch (VM), and Missing Steps (MS).
Advanced Bug Report Feedback
- Provides formatted feedback to improve reports and produce better debugging results.

How Effective is AstroBR?

AstroBR demonstrates significant superiority over Euler, a leading bug analysis tool, showcasing its capabilities with remarkable statistics. It achieves a 25.2% enhancement in the accuracy of quality annotations, ensuring that identified issues are correctly labeled and prioritized. Additionally, AstroBR excels in detecting missing steps, showcasing a staggering 71.4% improvement in this area, which greatly contributes to a more comprehensive and reliable bug analysis process.

Future of AI in Bug Reporting

What’s Next for AstroBR?

Supporting gestures, voice inputs, and complex interactions.
Integrating with real-time bug reporting tools.
Expanding AI-powered debugging for faster software fixes.

The Bigger Picture

AI-driven bug analysis will:

Reduce manual effort in debugging.
Improve bug report accuracy.
Accelerate software releases.

Conclusion

AstroBR is one of the significant advancements in AI-based bug report analysis related to LLM-based enhancement of quality bug reproduction steps and dynamic GUI analysis.

Through actionable insights and automation of missing steps’ detection, it has improved debugging efficiency while reducing delays to be more precise at fixes.

Drop a query if you have any questions regarding AstroBR and we will get back to you quickly.

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About CloudThat

CloudThat is an award-winning company and the first in India to offer cloud training and consulting services worldwide. As a Microsoft Solutions Partner, AWS Advanced Tier Training Partner, and Google Cloud Platform Partner, CloudThat has empowered over 850,000 professionals through 600+ cloud certifications winning global recognition for its training excellence including 20 MCT Trainers in Microsoft’s Global Top 100 and an impressive 12 awards in the last 8 years. CloudThat specializes in Cloud Migration, Data Platforms, DevOps, IoT, and cutting-edge technologies like Gen AI & AI/ML. It has delivered over 500 consulting projects for 250+ organizations in 30+ countries as it continues to empower professionals and enterprises to thrive in the digital-first world.

FAQs

1. What is AstroBR?

ANS: – AstroBR (language understanding and assessment of the steps to reproduce in Bug Reports) is an AI-powered tool that improves bug reports by extracting, mapping, and assessing Steps to Reproduce (S2Rs). It leverages GPT-4 and GUI analysis to ensure more accurate bug reproduction.

2. Why are Steps to Reproduce (S2Rs) important in bug reports?

ANS: – S2Rs provide developers with a clear, sequential guide to reproduce and fix bugs. Missing or unclear S2Rs lead to delays, unresolved issues, and increased debugging effort.

WRITTEN BY Abhishek Mishra

Abhishek Mishra works as an Associate Architect at CloudThat. He is a 4X AWS-certified professional, focusing on NLP and data science. Abhishek is pursuing a Master’s in Artificial Intelligence at IU International University of Applied Sciences. At AutomationEdge, he has worked on NLP models using BERT, GPT, and Rasa, and has contributed to computer vision projects with YOLO and TensorFlow. He is skilled in Python, Django, Streamlit, and PostgreSQL, and he builds data pipelines and tools.