SoTaNa: The Open-Source Software Development Assistant
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Abstract (arXiv)
Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an effective software development assistant. However, existing large language models represented by ChatGPT suffer from limited accessibility, including training data and model weights. Although other large open-source models like LLaMA have shown promise, they still struggle with understanding human intent. In this paper, we present SoTaNa, an open-source software development assistant. SoTaNa utilizes ChatGPT to generate high-quality instruction-based data for the domain of software engineering and employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA. We evaluate the effectiveness of our in answering Stack Overflow questions and demonstrate its capabilities. Additionally, we discuss its capabilities in code summarization and generation, as well as the impact of varying the volume of generated data on model performance. Notably, SoTaNa can run on a single GPU, making it accessible to a broader range of researchers. Our code, model weights, and data are public at urlhttps://github.com/DeepSoftwareAnalytics/SoTaNa.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 28 of its 58 references that are in the map. First posted on arXiv 25 Aug 2023.
Builds on
28 of 58 references.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 4.
8 papers.
- 2024Agents in Software Engineering: Survey, Landscape, and Visioncited by 60
- 2023AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMscited by 22
- 2025Autoformalization in the Era of Large Language Models: A Surveycited by 21
- 2024Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Modelscited by 15
- 2026AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completioncited by 7
- 2026ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generationcited by 3
- 2026DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoningcited by 3
- 2026PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agentscited by 2
- 2026PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents21 Jul 2026
- 2026DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning28 Jan 2026
- 2026AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion27 Jan 2026
- 2026ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation14 Jan 2026
- 2025Autoformalization in the Era of Large Language Models: A Survey29 May 2025
- 2024Agents in Software Engineering: Survey, Landscape, and Vision13 Sep 2024
- 2024Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models29 Jun 2024
- 2023AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs5 Nov 2023