## 1 Introduction
This introduces a notable challenge, as it opens the door to biases, hindering the team's ability to provide precise estimations. Consequently, this challenge of inaccurate estimations contributes to instances in which the entirety of the identified user stories or issues is not completed within each sprint, leading to budget and schedule overruns in the project. Given these challenges, there is a significant opportunity for research into automated techniques to estimate SPs with the aim of reducing the inaccuracies tied to expert judgment. Moreover, this pursuit is crucial, particularly for an Agile team, as it aims to foster consistent estimations across the entire life cycle of the project. There are studies proposing model-based SP estimation using ML techniques, utilizing historical data with features designed to characterize a software project, but excluding SPs, issues, or user stories. These models solely predict the SP required to complete the entire software project rather than individual user stories or issues [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]. Few studies in the literature [25, 26, 27, 28, 29, 30, 31] specifically concentrate on estimating the SP of user stories or issues based on the similarities between their definitions, written in natural language, and those of previously completed user stories or issues. In addition, the literature shows that software engineering data for effort estimation has substantial variability. This is due to the inherent complexity and dynamic nature of software development processes [32, 33]. This variability is influenced by diverse factors, including a wide range of project characteristics such as size, scope, and technologies used, as well as the dynamic nature of team composition, changing requirements, technological advancements, human factors, and external influences. Additionally, variations in data collection methods contribute to the observed variability. Managing and comprehending this variability is crucial for precise SEE [34]. To mitigate this variability, a publicly available dataset [35] from 26 Agile projects, crafted by [PERSON] et al. for SP estimation, was utilized in the proposed model. A decision making system is introduced by the proposed model to empower Scrum teams, enabling them to predict the SPs of user stories or the issues they define. The distinctive innovation of the proposed method lies in transforming unstructured data into a structured form using robust and powerful transformers. To achieve this, the potent DL technique SBERT was employed. Furthermore, GBTs, known for their efficacy in handling such data, were utilized to forecast the SPs of user stories or issues. To assess the efficiency of the proposed model, a rigorous 10-fold cross-validation technique and the validation data were employed. MAE and MdAE were chosen as the primary evaluation metrics, reflecting the average magnitude of errors in predictions without considering their direction. Additionally, SA, which is derived from MAE and MdAE, was used. It provides a normalized measure of the model's precision across diverse project scales. This comprehensive evaluation framework aimed to provide a robust understanding of the model's performance across various dimensions. In a rigorous evaluation across 26 Agile projects, the model utilized SBERT's advanced transformers to convert unstructured data written in natural language from user story titles and descriptions into structured form. It yielded the lowest MAE and MdAE values, ranging between 2.10 and 2.25 for MAE and 1.80 and 2.00 for MdAE across 10-fold cross-validation. This process further enriched the dataset by numerically encoding 'issue types' with label encoding and 'components' with multiple one-hot encoding techniques. The prepared dataset was then used to train the light gradient-boosting machine (LightGBM) algorithm, a variant of GBTs, which is named as **SBERT-LGBMTC**.