R-SE2: A Regression Model for Software Effort Estimation Using GPT-3 Embeddings

R-SE2: A Regression Model for Software Effort Estimation Using GPT-3 Embeddings | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 October 2025 V1 Latest version Share on R-SE2: A Regression Model for Software Effort Estimation Using GPT-3 Embeddings Authors : Eliane Maria de Bortoli Fávero 0000-0002-8229-0296 [email protected] , Gabriel Junges Baratto , Dalcimar Casanova , and Jefferson Tales Oliva Authors Info & Affiliations https://doi.org/10.22541/au.175932561.13497466/v1 194 views 131 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Software effort estimation by analogy (ABSEE) from requirements texts continues to present significant challenges, either in obtaining consistent historical data or inadequately representing contexts to infer effort. Large Language Models (LLMs) can contextualize the textual representation, significantly improving the results obtained in research in this area. Objective: This paper proposes the R-SE2 approach, which evaluates the performance of a regression model based on GPT-3 embeddings for software effort estimation, GPT-3 using exclusively textual software requirements. Method: The GPT-3 LLM was applied to textual features of user stories without fine-tuning. The generated representation was used in a deep learning architecture with a linear output. Results: The results indicate that R-SE2 outperforms the baseline. We highlight the results obtained by applying the proposed model in a single repository containing different projects, where the MAE value is 3.67 and the standard deviation is only 0.12, representing a 14% improvement over the baseline. Conclusion: The results were positive, confirming that LLMs without fine-tuning can be used to estimate ABSEE based on requirements texts. Among the method’s main advantages are its reliability, generalizability, speed, and low computational cost, allowing the inference of effort estimates for new and existing requirements. Supplementary Material File (wiley_r_se2__a_regression_model_for_software_effort_estimation_using_gpt_3_embeddings.pdf) Download 379.30 KB Information & Authors Information Version history V1 Version 1 01 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords gpt-3 llm nlp regression model software effort estimation Authors Affiliations Eliane Maria de Bortoli Fávero 0000-0002-8229-0296 [email protected] Universidade Tecnologica Federal do Parana View all articles by this author Gabriel Junges Baratto Universidade Tecnologica Federal do Parana View all articles by this author Dalcimar Casanova Universidade Tecnologica Federal do Parana View all articles by this author Jefferson Tales Oliva Universidade Tecnologica Federal do Parana View all articles by this author Metrics & Citations Metrics Article Usage 194 views 131 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Eliane Maria de Bortoli Fávero, Gabriel Junges Baratto, Dalcimar Casanova, et al. R-SE2: A Regression Model for Software Effort Estimation Using GPT-3 Embeddings. Authorea . 01 October 2025. DOI: https://doi.org/10.22541/au.175932561.13497466/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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