Software configurations play a crucial role in determining the behavior of
software systems. In order to ensure safe and error-free operation, it is
necessary to identify the correct configuration, along with their valid bounds
and rules, which are commonly referred to as software specifications. As
software systems grow in complexity and scale, the number of configurations and
associated specifications required to ensure the correct operation can become
large and prohibitively difficult to manipulate manually. Due to the fast pace
of software development, it is often the case that correct software
specifications are not thoroughly checked or validated within the software
itself. Rather, they are frequently discussed and documented in a variety of
external sources, including software manuals, code comments, and online
discussion forums. Therefore, it is hard for the system administrator to know
the correct specifications of configurations due to the lack of clarity,
organization, and a centralized unified source to look at. To address this
challenge, we propose SpecSyn a framework that leverages a state-of-the-art
large language model to automatically synthesize software specifications from
natural language sources. Our approach formulates software specification
synthesis as a sequence-to-sequence learning problem and investigates the
extraction of specifications from large contextual texts. This is the first
work that uses a large language model for end-to-end specification synthesis
from natural language texts. Empirical results demonstrate that our system
outperforms prior the state-of-the-art specification synthesis tool by 21% in
terms of F1 score and can find specifications from single as well as multiple
sentences.

本文提出了一个能从自然语言来源中自动生成软件规格说明的框架 SpecSyn，并证明其性能明显更强于之前的同类工具。