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From the 1 of 7 linked papers with an AI index.

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20242026
most citedData Augmentation for Improving Emotion Recognition in Software Engineering Communication

4 citations · 5 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.SE2026

A Low-Cost Human-in-the-Loop Investigation of Toxicity on GitHub at Scale

Rahat Rizvi Rahman, Mia Mohammad Imran, Kostadin Damevski

The paper introduces a human-in-the-loop workflow that combines a small local LLM with a lightweight Random Forest validator to efficiently label toxicity in over 124,000 GitHub is…

cs.SE20261 cited

Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub

Ramtin Ehsani, Sakshi Pathak, Shriya Rawal +3

AI coding agents are now submitting pull requests (PRs) to software projects, acting not just as assistants but as autonomous contributors. As these agentic contributions are rapid…

cs.SE2025

Toxicity Ahead: Forecasting Conversational Derailment on GitHub

Mia Mohammad Imran, Robert Zita, Rahat Rizvi Rahman +2

Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires…

cs.SE20254 cited

Data Augmentation for Improving Emotion Recognition in Software Engineering Communication

Mia Mohammad Imran, Yashasvi Jain, Preetha Chatterjee +1

Emotions (e.g., Joy, Anger) are prevalent in daily software engineering (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficienc…

cs.SE2024

Incivility in Open Source Projects: A Comprehensive Annotated Dataset of Locked GitHub Issue Threads

Ramtin Ehsani, Mia Mohammad Imran, Robert Zita +2

In the dynamic landscape of open source software (OSS) development, understanding and addressing incivility within issue discussions is crucial for fostering healthy and productive…

cs.SE2024

Emotion Classification In Software Engineering Texts: A Comparative Analysis of Pre-trained Transformers Language Models

Mia Mohammad Imran

Emotion recognition in software engineering texts is critical for understanding developer expressions and improving collaboration. This paper presents a comparative analysis of sta…