## 2 Related Work  
Research on effort and size estimation in ASD has a rich history spanning several decades. These endeavors have been meticulously identified and categorized using a classification framework proposed by various studies [26] and with visual representations serving as guidelines [36]. Leveraging insights from these comprehensive studies, techniques for estimating effort or size in ASD can be broadly classified into three distinctive groups: expert-based estimation methods, data-based estimation methods, and combination-based estimation methods. The latter category integrates both expert-based and data-based estimation methods for a more nuanced and comprehensive estimation process. Several studies in the literature have employed expert-based estimation methods for forecasting effort or size in ASD. These methods heavily depend upon expert judgment, drawing upon the wealth of experience and insights possessed by knowledgeable individuals. Among the widely used expert-based estimation methods are planning poker,expert judgment, and wideband Delphi. To estimate effort or size in the context of ASD, some studies [27; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] utilized planning poker, while expert judgment was preferred in others [39; 43; 53; 54; 55; 56; 57; 58]. Additionally, wideband Delphi was applied in studies [37; 48; 49; 59] to estimate effort or size in ASD. While expert-based models harness the wealth of experience from seasoned professionals, they pose inherent challenges marked by subjectivity and potential biases. To counter these limitations, data-based estimation methods have emerged, presenting notable advantages in ASD. Leveraging quantitative patterns from historical data, these methods introduce objectivity by minimizing individual subjectivity and biases. Their consistency in delivering results, scalability for handling large datasets, and adaptability to evolving project conditions make them invaluable tools. The automation potential, especially in ML and neural network (NN)-based approaches, reduces the reliance on constant expert involvement--a contrast to expert-based methods, which are often dependent upon ongoing expert input for accurate estimations. Data-based approaches further provide the benefit of reduced bias, being less influenced by individual opinions and experiences. The incorporation of quantitative metrics enhances the precision and thoroughness of data-based estimation methods. However, the choice between data-based and expert-based methods relies on contextual factors, available data, and the specific requirements of the estimation task, with some scenarios favoring a hybrid approach. Among data-based estimation methods, ML techniques such as k-nearest neighbor (KNN), random forest (RF), decision tree (DT), support vector machine (SVM), stochastic gradient boosting (SGB), and naive Bayes (NB) have been extensively explored [27; 28; 29; 30; 40; 42; 54; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88  
of characters, presence of keywords (the 15 most frequent terms with their probabilities), and priorities from user stories collected from two industrial Agile projects. The first project comprised 1325 user stories in a customized version of extreme programming (XP), while the second project included 13 user stories in XP. These features were used to train algorithmic models, such as SVM, linear regression (LR), and radial basis function (RBF). For model evaluation, they employed leave-one-out (LOOV) cross-validation [36], and various metrics, including magnitude of relative error (MRE), prediction at level k (PRED(k)), magnitude of error relative (MER), and standard deviation (SD) [102]. Comparisons were made with their previous study [103], which focused on estimation based on design metrics and developers' subjective estimates.