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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>\n                    Serverless computing is a popular cloud computing paradigm that enables developers to build applications at the function level, known as serverless applications. The Serverless Application Model (AWS SAM) is the most widely adopted configuration schema. However, misconfigurations pose a significant challenge due to the complexity of serverless configurations and the limitations of traditional data-driven techniques. Recent advancements in Large Language Models (LLMs), pre-trained on large-scale public data, offer promising potential for identifying and explaining misconfigurations. In this article, we present\n                    <jats:italic toggle=\"yes\">SlsDetector<\/jats:italic>\n                    , the first framework that harnesses the capabilities of LLMs to perform static misconfiguration detection in serverless applications.\n                    <jats:italic toggle=\"yes\">SlsDetector<\/jats:italic>\n                    utilizes effective prompt engineering with zero-shot prompting to identify configuration issues. It designs multi-dimensional constraints aligned with serverless configuration characteristics and leverages the Chain of Thought technique to enhance LLM inferences, alongside generating structured responses. We evaluate\n                    <jats:italic toggle=\"yes\">SlsDetector<\/jats:italic>\n                    on a curated dataset of 110 configuration files, which includes correct configurations, real-world misconfigurations, and intentionally injected errors. Our results show that\n                    <jats:italic toggle=\"yes\">SlsDetector<\/jats:italic>\n                    , based on ChatGPT-4o (one of the most representative LLMs), achieves a precision of 72.88%, recall of 88.18%, and F1-score of 79.75%, outperforming state-of-the-art data-driven methods by 53.82, 17.40, and 49.72 percentage points, respectively. We further investigate the generalization capability of\n                    <jats:italic toggle=\"yes\">SlsDetector<\/jats:italic>\n                    across recent LLMs, including Llama 3.1 (405B) Instruct Turbo, Gemini 1.5 Pro, and DeepSeek V3, with consistently high effectiveness.\n                  <\/jats:p>","DOI":"10.1145\/3745766","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T08:13:01Z","timestamp":1750666381000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["LLM-Based Misconfiguration Detection for AWS Serverless Computing"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3023-1005","authenticated-orcid":false,"given":"Jinfeng","family":"Wen","sequence":"first","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4765-1893","authenticated-orcid":false,"given":"Zhenpeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0038-825X","authenticated-orcid":false,"given":"Zixi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-442X","authenticated-orcid":false,"given":"Federica","family":"Sarro","sequence":"additional","affiliation":[{"name":"Computer Science, University College London, London, United Kingdom of Great Britain and Northern Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1766-2444","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"Advanced Institute of Big Data, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7947-7826","authenticated-orcid":false,"given":"Haodi","family":"Ping","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7245-1298","authenticated-orcid":false,"given":"Shangguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2024. 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