{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:41:43Z","timestamp":1790113303725,"version":"4.0.1"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004063","name":"Knut and Alice Wallenberg Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002835","name":"Chalmers tekniska h\u00f6gskola","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002835","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Systems and Software"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.jss.2026.112961","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T06:47:16Z","timestamp":1779518836000},"page":"112961","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["GateLens: A reasoning-enhanced LLM agent for automotive software release analytics"],"prefix":"10.1016","volume":"240","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5130-5520","authenticated-orcid":false,"given":"Arsham Gholamzadeh","family":"Khoee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5179-4205","authenticated-orcid":false,"given":"Robert","family":"Feldt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3620-8589","authenticated-orcid":false,"given":"Dhasarathy","family":"Parthasarathy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3221-7517","authenticated-orcid":false,"given":"Yinan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jss.2026.112961_b1","doi-asserted-by":"crossref","first-page":"76930","DOI":"10.52202\/079017-2447","article-title":"Many-shot in-context learning","volume":"37","author":"Agarwal","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jss.2026.112961_b2","series-title":"Program synthesis with large language models","author":"Austin","year":"2021"},{"key":"10.1016\/j.jss.2026.112961_b3","unstructured":"Boudewijn, A.T.P., Ferraris, A.F., Panfilo, D., Cocca, V., Zinutti, S., De Schepper, K., Chauvenet, C.R., 2023. Privacy measurements in tabular synthetic data: State of the art and future research directions. In: NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI."},{"key":"10.1016\/j.jss.2026.112961_b4","series-title":"Why tabular foundation models should be a research priority","author":"van Breugel","year":"2024"},{"issue":"3","key":"10.1016\/j.jss.2026.112961_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3641289","article-title":"A survey on evaluation of large language models","volume":"15","author":"Chang","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.jss.2026.112961_b6","series-title":"A theoretical understanding of chain-of-thought: Coherent reasoning and error-aware demonstration","author":"Cui","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b7","series-title":"Stepwise perplexity-guided refinement for efficient chain-of-thought reasoning in large language models","author":"Cui","year":"2025"},{"key":"10.1016\/j.jss.2026.112961_b8","doi-asserted-by":"crossref","first-page":"29807","DOI":"10.52202\/079017-0938","article-title":"UQE: A query engine for unstructured databases","volume":"37","author":"Dai","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jss.2026.112961_b9","series-title":"Table pre-training: A survey on model architectures, pre-training objectives, and downstream tasks","author":"Dong","year":"2022"},{"key":"10.1016\/j.jss.2026.112961_b10","series-title":"C3: Zero-shot text-to-sql with chatgpt","author":"Dong","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b11","series-title":"Large language models (LLMs) on tabular data: Prediction, generation, and understanding-a survey","author":"Fang","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b12","series-title":"Retrieval-augmented generation for large language models: A survey","author":"Gao","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b13","series-title":"Blendsql: A scalable dialect for unifying hybrid question answering in relational algebra","author":"Glenn","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b14","series-title":"Parameter-efficient fine-tuning for large models: A comprehensive survey","author":"Han","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b15","unstructured":"Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X., Sontag, D., TabLLM: Few-shot Classification of Tabular Data with Large Language Models. http:\/\/dx.doi.org\/10.48550\/arXiv.2210.10723."},{"key":"10.1016\/j.jss.2026.112961_b16","series-title":"Fewer is more: Boosting llm reasoning with reinforced context pruning","author":"Huang","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b17","series-title":"Tab-cot: Zero-shot tabular chain of thought","author":"Jin","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b18","series-title":"IFIP International Conference on Testing Software and Systems","first-page":"30","article-title":"Gonogo: An efficient LLM-based multi-agent system for streamlining automotive software release decision-making","author":"Khoee","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b19","series-title":"Decomposed prompting: A modular approach for solving complex tasks","author":"Khot","year":"2022"},{"key":"10.1016\/j.jss.2026.112961_b20","series-title":"2023 38th IEEE\/ACM International Conference on Automated Software Engineering","first-page":"1737","article-title":"On automated assistants for software development: the role of llms","author":"Leung","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b21","unstructured":"Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., Wang, B., Qin, B., Geng, R., Huo, N., Zhou, X., Ma, C., Li, G., Chang, K.C.C., Huang, F., Cheng, R., Li, Y., Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs. http:\/\/dx.doi.org\/10.48550\/arXiv.2305.03111."},{"key":"10.1016\/j.jss.2026.112961_b22","series-title":"Chain of code: Reasoning with a language model-augmented code emulator","author":"Li","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b23","series-title":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"365","article-title":"Data-efficient fine-tuning for LLM-based recommendation","author":"Lin","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b24","series-title":"Lost in the middle: How language models use long contexts","author":"Liu","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b25","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.3390\/app14031046","article-title":"An empirical study of the code generation of safety-critical software using llms","volume":"14","author":"Liu","year":"2024","journal-title":"Appl. Sci."},{"issue":"5","key":"10.1016\/j.jss.2026.112961_b26","article-title":"Out-of-scope intent detection on a knowledge-based chatbot.","volume":"14","author":"Manik","year":"2021","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"10.1016\/j.jss.2026.112961_b27","doi-asserted-by":"crossref","first-page":"180","DOI":"10.3390\/fi16060180","article-title":"Using chatgpt in software requirements engineering: a comprehensive review","volume":"16","author":"Marques","year":"2024","journal-title":"Futur. Internet"},{"key":"10.1016\/j.jss.2026.112961_b28","series-title":"Agentic ai needs a systems theory","author":"Miehling","year":"2025"},{"key":"10.1016\/j.jss.2026.112961_b29","series-title":"Exploring the impact of table-to-text methods on augmenting llm-based question answering with domain hybrid data","author":"Min","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b30","series-title":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","first-page":"10471","article-title":"Learning relational decomposition of queries for question answering from tables","author":"Mouravieff","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b31","series-title":"Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning","author":"Pan","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b32","series-title":"The ultimate guide to fine-tuning llms from basics to breakthroughs: An exhaustive review of technologies, research, best practices, applied research challenges and opportunities","author":"Parthasarathy","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b33","series-title":"Suri: Multi-constraint instruction following for long-form text generation","author":"Pham","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b34","series-title":"Taskweaver: A code-first agent framework","author":"Qiao","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b35","doi-asserted-by":"crossref","unstructured":"Qin, C., Zhang, A., Chen, C., Dagar, A., Ye, W., 2024. In-context learning with iterative demonstration selection. In: Findings of the Association for Computational Linguistics: EMNLP 2024. pp. 7441\u20137455.","DOI":"10.18653\/v1\/2024.findings-emnlp.438"},{"key":"10.1016\/j.jss.2026.112961_b36","series-title":"A systematic survey of prompt engineering in large language models: Techniques and applications","author":"Sahoo","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b37","doi-asserted-by":"crossref","first-page":"29106","DOI":"10.52202\/079017-0917","article-title":"Chain of thoughtlessness? an analysis of cot in planning","volume":"37","author":"Stechly","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jss.2026.112961_b38","series-title":"Exploring chain-of-thought style prompting for text-to-sql","author":"Tai","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b39","doi-asserted-by":"crossref","first-page":"74952","DOI":"10.52202\/075280-3275","article-title":"Language models don\u2019t always say what they think: Unfaithful explanations in chain-of-thought prompting","volume":"36","author":"Turpin","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jss.2026.112961_b40","series-title":"Fine tuning LLM for enterprise: Practical guidelines and recommendations","author":"VM","year":"2024"},{"issue":"6","key":"10.1016\/j.jss.2026.112961_b41","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-40231-1","article-title":"A survey on large language model based autonomous agents","volume":"18","author":"Wang","year":"2024","journal-title":"Front. Comput. Sci."},{"key":"10.1016\/j.jss.2026.112961_b42","series-title":"Self-consistency improves chain of thought reasoning in language models","author":"Wang","year":"2022"},{"key":"10.1016\/j.jss.2026.112961_b43","series-title":"Chain-of-table: Evolving tables in the reasoning chain for table understanding","author":"Wang","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b44","doi-asserted-by":"crossref","first-page":"24824","DOI":"10.52202\/068431-1800","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jss.2026.112961_b45","series-title":"Autogen: Enabling next-gen llm applications via multi-agent conversation framework","author":"Wu","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b46","series-title":"DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure","author":"Ye","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b47","series-title":"Igniting language intelligence: The hitchhiker\u2019s guide from chain-of-thought reasoning to language agents","author":"Zhang","year":"2023"},{"key":"10.1016\/j.jss.2026.112961_b48","doi-asserted-by":"crossref","unstructured":"Zhang, T., Yue, X., Li, Y., Sun, H., 2024. Tablellama: Towards open large generalist models for tables. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). pp. 6024\u20136044.","DOI":"10.18653\/v1\/2024.naacl-long.335"},{"key":"10.1016\/j.jss.2026.112961_b49","series-title":"Retrieval-augmented generation for ai-generated content: A survey","author":"Zhao","year":"2024"},{"key":"10.1016\/j.jss.2026.112961_b50","doi-asserted-by":"crossref","first-page":"123846","DOI":"10.52202\/079017-3936","article-title":"Can language models perform robust reasoning in chain-of-thought prompting with noisy rationales?","volume":"37","author":"Zhou","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Journal of Systems and Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0164121226001949?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0164121226001949?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T03:49:42Z","timestamp":1783136982000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0164121226001949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":50,"alternative-id":["S0164121226001949"],"URL":"https:\/\/doi.org\/10.1016\/j.jss.2026.112961","relation":{},"ISSN":["0164-1212"],"issn-type":[{"value":"0164-1212","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"GateLens: A reasoning-enhanced LLM agent for automotive software release analytics","name":"articletitle","label":"Article Title"},{"value":"Journal of Systems and Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jss.2026.112961","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"112961"}}