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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Vydáno v:2024 IEEE/ACM International Workshop on Natural Language-Based Software Engineering (NLBSE) s. 45 - 48
Hlavní autoři: Gomez-Barrera, Daniel Fernando, Becerra, Luccas Rojas, Roncancio, Juan Pinzon, Almanza, David Ortiz, Arboleda, Juan, Linares-Vasquez, Mario, Manrique, Ruben Francisco
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 20.04.2024
Témata:
Buildings
Computer architecture
Conferences
Data models
Embedding
Few-shot learning
GitHub Issue Classification
Measurement
NLBSE 2024 Competition
Task analysis
Transformers
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Shrnutí: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 repositories. Our primary strategy involved improving embeddings by developing the Classification Few Fit Sentence Transformer (CFFitST), a strategy that fine-tunes embeddings from a base sentence transformer to suit the dataset. We also explored various hypotheses concerning the optimal combination of information input and classification models. As a result, we managed to achieve an average improvement of 2.44 \% over the SetFit baseline.
DOI:10.1145/3643787.3648040