Lessons from the NLBSE 2024 Competition: Towards Building Efficient Models for GitHub Issue Classification

This paper presents the findings of our team's efforts during the "NLBSE 2024" competition, which centered on the multi-class classification of GitHub Issues. The challenge required models with strong few-shot learning capabilities to distinguish between 300 issues from five different...

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Veröffentlicht in:	2024 IEEE/ACM International Workshop on Natural Language-Based Software Engineering (NLBSE) S. 45 - 48
Hauptverfasser:	Gomez-Barrera, Daniel Fernando, Becerra, Luccas Rojas, Roncancio, Juan Pinzon, Almanza, David Ortiz, Arboleda, Juan, Linares-Vasquez, Mario, Manrique, Ruben Francisco
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 20.04.2024
Schlagworte:	Buildings Computer architecture Conferences Data models Embedding Few-shot learning GitHub Issue Classification Measurement NLBSE 2024 Competition Task analysis Transformers
Online-Zugang:	Volltext
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