Towards Connecting Requirements with Developer Artifacts in a Local Context

Towards Connecting Requirements with Developer Artifacts in a Local Context

연구 분야: Software Development

학회: International Working Conference on Requirements Engineering: Foundation for Software Quality

[Context and Motivation] To ensure that software is developed as intended, it is important to connect the completion of software development activities with the requirements that they support; yet, establishing traceability links is a costly task. [Problem] Recent work has suggested the use of machine learning and large language models (LLMs) for many software engineering tasks, including traceability. However, there are insufficient publicly available datasets with links between requirements and development artifacts to effectively train third-party LLMs, and uploading proprietary data to be processed by external LLMs may be infeasible due to privacy concerns. [Principal Idea] To address these challenges, we propose a privacy-preserving pipelined approach combining clustering and triplet loss learning (TLL) to create artifact-to-requirements traceability links. Our clustering method efficiently identifies training examples for TLL, enabling local trace computation with minimal training data. [Contribution] In this research preview, we present our pipelined approach with an initial proof of concept and analyze the use of our clustering strategy.