5 papers
Pre-Training on Software Engineering Texts: Effects on Domain Adaptation and General-Language Understanding
Fabian C. Peña, Steffen Herbold
Generalist and code-focused Language Models (LMs) are increasingly applied to software engineering (SE), yet whether they are optimized for understanding SE textual artifacts (e.g.…
Large Language Models Have Unreliable Understanding of Software Engineering Terminology
Huzaifa Ejaz, Fabian C. Peña, Steffen Herbold
Large Language Models (LLMs) are increasingly used in software engineering (SE), yet there is no systematic study that determines to which degree these LLMs actually understand sta…
SELU: A Software Engineering Language Understanding Benchmark
Fabian C. Peña, Steffen Herbold
Large Language Models (LLMs) have demonstrated remarkable capabilities in code understanding and generation. However, their effectiveness on non-code Software Engineering (SE) task…
Augmenting the Generality and Performance of Large Language Models for Software Engineering
Fabian C. Peña
Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practic…
Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
Fabian C. Peña, Steffen Herbold
This work evaluates Sentence-BERT for a multi-label code comment classification task seeking to maximize the classification performance while controlling efficiency constraints dur…