# Large Language Models Based Automatic Synthesis of Software Specifications

arXiv: https://arxiv.org/abs/2304.09181 · HTML: https://mlsanity.com/paper/2304.09181/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 68 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [DEAR: A Novel Deep Learning-based Approach for Automated Program Repair](https://mlsanity.com/paper/2205.01859/) (2022)
- [Learning Fitness Functions for Machine Programming](https://mlsanity.com/paper/1908.08783/) (2019)
- [Synthesizing Programs with Continuous Optimization](https://mlsanity.com/paper/2211.00828/) (2022)
- [Automated Identification of Security-Relevant Configuration Settings Using NLP](https://mlsanity.com/paper/2209.08853/) (2022)

## Built on by (most cited first, 13 of 13)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [Can GPT-4 Replicate Empirical Software Engineering Research?](https://mlsanity.com/paper/2310.01727/) (2023)
- [The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study](https://mlsanity.com/paper/2507.03156/) (2025)
- [The importance of visual modelling languages in generative software engineering](https://mlsanity.com/paper/2411.17976/) (2024)
- [Learning-Based Automatic Synthesis of Software Code and Configuration](https://mlsanity.com/paper/2305.15642/) (2023)
- [Variability-Driven User-Story Generation using LLM and Triadic Concept Analysis](https://mlsanity.com/paper/2504.08666/) (2025)
- [Working Document -- Formalising Software Requirements with Large Language Models](https://mlsanity.com/paper/2506.14627/) (2025)
- [Neurosymbolic Auditing of Natural-Language Software Requirements](https://mlsanity.com/paper/2605.13817/) (2026)
- [Automated Synthesis of Cloud Emulators](https://mlsanity.com/paper/2608.23842/) (2026)
- [SkillForge: Compositional Skill Synthesis with Verification-in-the-Loop for Generating Formally Verified Dafny Programs](https://mlsanity.com/paper/2608.29841/) (2026)
- [Relationally Guided Use Case Modeling with LLMs](https://mlsanity.com/paper/2609.18291/) (2026)

## Abstract (arXiv)

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.