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The condenSE project focuses on improving the sustainability of code language models by refining training data quality while reducing its volume. This approach aims to lower energy consumption and carbon emissions in software engineering tasks like bug fixing and code writing, aligning with environmental goals.
Large language models (LLMs) have gained widespread attention and user adoption.
These models, when trained on source code from platforms like GitHub, acquire a deep understanding of both the semantic and syntactic structures of code (i.e., code language models or CLMs).
This understanding has paved the way for significant advancements in software engineering, offering developers valuable assistance in labor-intensive tasks like bug fixing and code writing.
While CLMs offer tremendous assistance…
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