Shedding Light on Software Engineering-specific Metaphors and Idioms
Mia Mohammad Imran, Preetha Chatterjee, and Kostadin Damevski

TL;DR
This paper investigates how figurative language in software engineering communications affects automated analysis tools and explores the potential of fine-tuned large language models to better interpret such language, improving task performance.
Contribution
It provides the first comprehensive study on figurative language in SE, demonstrating how fine-tuning LLMs enhances understanding and automated analysis of developer communications.
Findings
Fine-tuning LLMs improves emotion classification by 6.66%.
Fine-tuning LLMs improves incivility detection by 7.07%.
Fine-tuning LLMs enhances bug report prioritization by 3.71%.
Abstract
Use of figurative language, such as metaphors and idioms, is common in our daily-life communications, and it can also be found in Software Engineering (SE) channels, such as comments on GitHub. Automatically interpreting figurative language is a challenging task, even with modern Large Language Models (LLMs), as it often involves subtle nuances. This is particularly true in the SE domain, where figurative language is frequently used to convey technical concepts, often bearing developer affect (e.g., `spaghetti code'). Surprisingly, there is a lack of studies on how figurative language in SE communications impacts the performance of automatic tools that focus on understanding developer communications, e.g., bug prioritization, incivility detection. Furthermore, it is an open question to what extent state-of-the-art LLMs interpret figurative expressions in domain-specific communication…
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Taxonomy
TopicsSoftware Engineering Research · Software Engineering Techniques and Practices · Topic Modeling
