论文标题
端到端的理由重建
End-to-End Rationale Reconstruction
论文作者
论文摘要
设计决策背后的逻辑（称为Design Rationale）非常有价值。过去，研究人员试图自动提取和利用此信息，但先前的技术仅适用于特定环境，并且在端到端的端到端理由信息提取管道上的进展不足。在这里，我们概述了通往这样的管道的道路，该管道利用了几种机器学习（ML）和自然语言处理（NLP）技术。我们提出的与上下文无关的方法（称为kantara）产生了决定及其理由的知识图表示，这考虑了其历史的演变和可追溯性。我们还提出了验证机制，以确保提取信息的正确性和开发过程的连贯性。我们对来自Linux内核的小示例进行了对我们提出的方法的初步评估，该示例显示了有希望的结果。
The logic behind design decisions, called design rationale, is very valuable. In the past, researchers have tried to automatically extract and exploit this information, but prior techniques are only applicable to specific contexts and there is insufficient progress on an end-to-end rationale information extraction pipeline. Here we outline a path towards such a pipeline that leverages several Machine Learning (ML) and Natural Language Processing (NLP) techniques. Our proposed context-independent approach, called Kantara, produces a knowledge graph representation of decisions and of their rationales, which considers their historical evolution and traceability. We also propose validation mechanisms to ensure the correctness of the extracted information and the coherence of the development process. We conducted a preliminary evaluation of our proposed approach on a small example sourced from the Linux Kernel, which shows promising results.
