## 6 Results
First, 'issue type' and 'components' were added separately and jointly to the feature set extracted from the titles and descriptions of issues as additional features. Second, components were included in the feature set extracted from titles, descriptions, and types of issues. Last, issue type was incorporated into the feature set extracted from titles, descriptions, and components of issues. As a result, five distinct variations emerged. To address this research question effectively, these five variations were initially compared with each other, and then the variation(s) yielding better performance was compared to the **SBERT-XG** model with Bayesian optimization utilizing the feature set extracted from the titles and descriptions of issues. When these five variations were compared among themselves, it was observed that the **SBERT-LGBM** with manual tuning performs well in three variations, while **SBERT-CAT** with Bayesian optimization and **SBERT-XG** with Bayesian optimization excel in one variation each. These five variations score very closely to one another, indicating that adding 'issue type' and 'components' in the feature extraction process contributes to improved performance. However, the **SBERT-XG** model with Bayesian optimization, utilizing the feature set extracted from the titles and descriptions of issues, and **SBERT-LGBM** with manual tuning, utilizing the feature set extracted from titles, descriptions, and both 'issue type' and 'components' features (named **SBERT-LGBMTC**), were selected for further comparison with other models due to their superior performance when compared with the other four variations. According to Table 4, 22 out of 26 projects' SA values are positive, indicating that the **SBERT-LGBMTC** model outperforms random guessing in 22 projects. The remaining four projects (CLI, CONFLOUD, CONFSERVER, and EVG) have negative SA values, suggesting that **SBERT-LGBMTC** underperforms random guessing in four projects, and the improvement is statistically significant. To further validate these findings, [PERSON]'s \(d\) was employed to measure the effect sizes between the **SBERT-LGBMTC** model and the mean estimator. In 18 projects (ALOY, APSTUD, CLOV, DEAMON, DM, DNN, FAB, MDL, MESOS, MUE, SERVER, STL, TDP, TDQ, TESB, TIDOC, TIMOB, and XD), the **SBERT-LGBMTC** model outperforms the mean estimator, but it underperforms in wight projects (CLI, COMPASS, CONFLOUD, CONFSERVER, DURACLOUD, EVG, NEXUS, and TISTUD). Of these eight projects, only for two are the absolute errors between the **SBERT-LGBMTC** model and the mean estimator statistically significant, exhibiting large effect sizes. The effect sizes of two projects are medium, and those of the remaining projects are small and negligible. Similarly, the **SBERT-LGBMTC** model outperforms the median estimator in 16 projects (APSTUD,CLOV, DEAMON, DM, DNN, MDL, MESOS, MUE, NEXUS, STL, TDP, TDQ, TESB, TIDOC, TIMOB, and XD) while underestimating in 10 projects (ALOY, CLI, COMPASS, CONFCLOUD, CONFSERVER, DURACLOUD, EVG, FAB, SERVER, and TISTUD). Of these 10 projects, three projects exhibit a large effect size, one project shows a medium effect size, and the remaining display small/negligible effect sizes. The improvement in these three projects is deemed statistically significant. Upon conducting a comprehensive analysis of the **SBERT-LGBMTC** model against four state-of-the-art models, it was revealed that the **SBERT-LGBMTC** model notably surpasses the performance of all of these models in 17 different projects. Among the nine other projects evaluated, three projects demonstrate substantial improvement with a large effect size, indicating a statistically significant enhancement when utilizing the **SBERT-LGBMTC** model. Furthermore, one of these projects exhibits a medium effect size, while the rest show only small or negligible effect sizes. Moreover, the comparative performances of the **SBERT-LGBMTC** and **SBERT-XG** models were rigorously analyzed across 26 projects.