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Sun 27 Apr 2025 16:00 - 16:22 at 213 - Session 3: Evaluating and Improving Bot Impact Chair(s): Ahmad Abdellatif

Many Open Source Software (OSS) maintainers experience burnout due to the constant need to support and answer questions from newcomers. This leads to slower response times and unanswered queries, discouraging newcomers and ultimately hindering community growth. Research indicates that conversational agents trained on community data can answer common questions, thus reducing the workload for maintainers. However, developers require real questions from newcomers extracted from an actual project to test the efficacy and guide the development of such chatbots. To support the development of conversational agents for newcomer onboarding, we curated a dataset of varied questions from the JabRef OSS project. This dataset paper details our collection process, describes the data, and offers reflections on future research. We expect that this dataset can foster further research focused on developing chatbots to assist newcomers.

Sun 27 Apr

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:30
Session 3: Evaluating and Improving Bot ImpactBotSE at 213
Chair(s): Ahmad Abdellatif University of Calgary

Ahmad Abdellatif

16:00
22m
Talk
Towards a Newcomers Dataset to Assess Conversational Agent�s Efficacy in Mentoring Newcomers
BotSE
Misan Etchie NAU RESHAPE LAB, Hunter Beach NAU RESHAPE LAB, Katia Romero Felizardo NAU RESHAPE LAB, Igor Steinmacher NAU RESHAPE LAB
16:22
22m
Talk
Bot-Driven Development: From Simple Automation to Autonomous Software Development Bots
BotSE
Christoph Treude Singapore Management University, Chris Poskitt Singapore Management University
Link to publication DOI Pre-print
16:45
22m
Talk
Bridging HCI and AI Research for the Evaluation of Conversational SE Assistants
BotSE
Jonan Richards Radboud University, Mairieli Wessel Radboud University
17:07
22m
Talk
Reducing Alert Fatigue via AI-Assisted Negotiation: A Case for Dependabot
BotSE
Raula Gaikovina Kula The University of Osaka
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