## 5 Experiment Results and Analysis
### Experiment for the Generalization and Robustness in Noisy Conditions (Answer to RQ4)  
This section demonstrates the utility, robustness, and generalization capabilities of LePB-SA4 RE. We utilized the Social and Productivity datasets, comprising a total of 6206 data entries. The datasets are divided into training, validation, and test sets following the proportions described in Section 4.1. To effectively simulate noisy conditions for a valuable evaluation, two common noise addition strategies are applied: label flipping and random replacement (substituting original sample sentences with nonsensical character sequences) [50; 51]. Each strategy accounts for half of the total noise proportion in the dataset without overlap. For example, in a scenario with 40% noise, 20% comes from label flipping and 20% from random replacement. The experiments conduct ten parallel tests for both the SOTA method and LePB-SA4 RE at each noise level. The average drop rates from the average results of these parallel tests are used to evaluate the generalization of models, while the fluctuations in the curves of the parallel tests are used to assess the robustness. _Analysis of generalization advantages_: Table 7 presents the average results from ten parallel tests, evaluating accuracy and F1-Score. The overall performance of the LePB-SA4 RE model consistently surpasses that of the SOTA method under all noise conditions. Although the performance of both models declines as the noise proportion increases, the rate of decline for our model is consistently lower than that of the SOTA model. According to the relative performance improvement rate measurement formula, the relative improvement in performance decline rates ranges from 9.12% to 11.75% on the benchmark dataset (AWARE) and from 35.37% to 36.60% on the untrained dataset (BFRU). These results demonstrate that the LePB-SA4 RE model offers significant advantages in generalization over the SOTA method in noisy conditions. Figure 7: Performance of LePB-SA4 RE compared to soft prompt method. Analysis of robustness advantages: CIs provide a reliable measure of the variability and consistency of model performance. In the context of deep learning and model evaluation, narrower CIs indicate that the model is less sensitive to noise, thereby confirming its robustness. Figures 8 and 9 illustrate the 95% confidence interval bands (CIBs) for the accuracy and F1-Score of the SOTA method and LePB-SA4 RE across different noise proportions (10%, 20%, 30%, 40%) using the AWARE and BFRU datasets. As the noise proportion increases, the CIBs of both models widen, indicating a decrease in stability. However, the CIBs for the LePB-SA4 RE are consistently 30-50% narrower compared to SOTA across all noise conditions. This effect is particularly evident in the untrained BFRU dataset, where the narrower CIBs of LePB-SA4 RE demonstrate better robustness in maintaining consistent accuracy and F1-Scores. Modern software development demands agile responses to user feedback to remain competitive and responsive in a rapidly evolving market. In practical industrial applications, where data anomalies and noise are common, it is essential to establish models that maintain reliability and accuracy without requiring extensive retraining. The superior generalization and robustness of the LePB-SA4 RE model under noisy conditions allow it to adapt effectively to the inherent diversity, complexity, and evolving nature of user feedback. This enables enterprises to fully leverage app reviews, reducing the time and resources