## 6 Results
Interestingly, although the SBERT-XG model with Bayesian optimization outperforms the Deep-SE model in 17 projects (APSTUD, CLOV, COMPASS, DM, DNN, FAB, MDL, MESOS, MULE, NEXUS, STL, TDP, TSEB, TIDOC, TIMOB, TISTUD, and XD) and the TF-IDF-SE model in 18 projects (APSTUD, CLI, CLOV, COMPASS, DM, DNN, FAB, MDL, MESOS, MULE, NEXUS, TDP, TDQ, TESB, TIDOC, TIMOB, TISTUB, and XD), it does not perform as well against the Deep-SE model in nine projects (ALOY, CLI, CONFCLOUD, CONSERVER, DEAMON, DURACLOUD, EVG, SERVER, and TDQ) and the TF-IDF-SE model in eight projects (ALOY, CONFCLOUD, CONSERVER, DAEMON, DURACLOUD, EVG, SERVER, and STL). Within these groups, five projects (CONSERVER, DURACLOUD, EVG, SERVER, and TDQ) in comparison with the Deep-SE indicate a large effect size, highlighting a significant difference in performance. One project (ALOY) shows a medium effect size, while the others display small or negligible effect sizes. Against the TF-IDF-SE model, three projects (DURACLOUD, EVG, and SERVER) exhibit a large effect size, two projects (ALOY and CONSERVER) present a medium effect size, and the remainder show small or negligible effect sizes. This detailed comparison underscores the varied effectiveness of LHC-SE across different projects and in relation to various advanced models. **RQ2:**_Does adding other features consisting of a single word or a few words, namely 'issue type' and 'components', to the feature extraction process improve the estimation accuracy of the proposed model?_  
In order to address this research question comprehensively, a comparative analysis was conducted, pitting the **SBERT-XG** model with Bayesian optimization against three variations, each utilizing distinct feature sets. These variations comprised one relying solely on issue type information, another focusing exclusively on components, and a third combining both for the feature set. Prior to the comparison between the **SBERT-XG** model with Bayesian optimization and these three variations, an assessment was made among the three variations themselves to determine which combination yielded more favorable results. Subsequently, the superior combination(s) were evaluated against the **SBERT-XG** model with Bayesian optimization. The analyses, detailed in the table, underscore the robust performance of both the **SBERT-CAT** model with random search; one employing a feature set extracted from titles, descriptions, and types of issues; and another utilizing a feature set from titles, descriptions, issue types, and components of issues. The former demonstrates low MAE and MdAE along with high SA, while the latter exhibits low MAE and high SA. Given their comparable outcomes, both are juxtaposed with the **SBERT-XG** model with Bayesian optimization. Through this comparison, it is noted that the performances of these three models are closely matched. However, the **SBERT-XG** model with Bayesian optimization, when leveraging the feature set extracted from titles and descriptions of issues, showcases superior performance with low MAE and MdAE and high SA. Therefore, the **SBERT-XG** model with Bayesian optimization, when utilizing the feature set from titles and descriptions of issues, was identified as the foremost model for subsequent comparisons with state-of-the-art models. It can be inferred that the integration of 'issue type' and 'components' into the feature extraction process contributes to enhanced performance. This finding underscores the value of incorporating nuanced, context-specific information into the model, thereby enriching the feature set and potentially leading to more accurate estimations. **RQ3:**_Instead of adding 'issue type' and 'components' consisting of s single word and a few words, respectively, to feature extraction process, does adding them to the estimation process improve the estimation accuracy of the proposed model?_  
To explore this aspect, different variations were tested by using 'issue type' and 'components' as independent features in the estimation process.