## 1 Introduction
In summary, traditional FT methods with fixed parameters often rely heavily on input features and fail to fully exploit the potential of PLMs in low-resource, noisy app review data. While the dynamism of PT methods offers performance improvements, it also introduces risks related to reduced generalization and robustness. To address these challenges, this study proposes a Lexicon-enhanced and Prompt-tuning Based ABSA model for evolving Requirements Elicitation from app reviews, named LePB-SA4 RE. This model leverages the BERT architecture and adopts a Hard-prompt template specifically designed for app reviews, anchoring the learning process to a pre-defined language structure to maximize learning efficiency while maintaining stability. The template and samples are combined as the input of the BERT model, and the built-in BertForMaskedLM (B4 MLM) classifier is used to analyze sentiments and aspects via cloze-style ([MASK] tokens) prediction tasks. Subsequently, to mitigate the inflexibility of the Hard-prompt approach, the model introduces a modulation layer that integrates a sentiment lexicon with a dynamic activation function. The lexicon consists of 300 thousand token-sentiment polarity pairs and is embedded into the model as additional semantic information. Sentiment-oriented dynamic parameters are applied to the weight of the activation function, allowing the model to adaptively adjust its sensitivity to sentiment feature inputs and contexts during PT. To demonstrate the effectiveness, robustness, and generalization of LePB-SA4 RE in evolving requirements elicitation from app reviews, the following research questions (RQs) are proposed to guide the experiments:  
**RQ1**: Compared to current methods, has the performance of LePB-SA4 RE improved? **RQ2**: Has the designed prompt template positively impacted the model in evolving requirements elicitation from app reviews? **RQ3**: As the scale of training data decreases, does LePB-SA4 RE have advantages on the other untrained app review datasets? **RQ4**: What advantages does LePB-SA4 RE offer in terms of robustness and generalization compared to state-of-the-art (SOTA) methods in noisy conditions? **RQ5**: What are the potential mechanisms behind the performance differences between different historical BERT checkpoints and PT methods in terms of requirement extraction? Rationale: **RQ1** explores the effectiveness of LePB-SA4 RE compared to SOTA models, validating the hypothesis that integrating PT with a lexicon-enhanced dynamic modulation layer improves performance. **RQ2** focuses on validating the hypothesis that the proposed design strategy for the prompt template has significant advantages in evolving requirements elicitation. Furthermore, it examines the impact of integrating the lexicon-enhanced dynamic modulation layer with diverse prompt templates on the performance of the BERT. **RQ3** is to evaluate the performance and generalization of the LePB-SA4 RE in low-resource scenarios, highlighting its potential in real-world, data-limited environments. **RQ4** seeks to assess the robustness and generalization of LePB-SA4 RE in noisy conditions, emphasizing its practical applicability in handling noisy app review data. **RQ5** compares the performance of diverse BERT models based on the PT method under the cross-task of ABSA for requirements elicitation. The different results of empirical study reveal the potential model mechanisms, providing valuable insights for future research. Additionally, the findings further validate the effectiveness of our proposed framework. The main contributions of this study are as follows:  
1. **Novel ABSA Model:** This study introduces LePB-SA4 RE, the first model to combine the PT+PLM paradigm with a lexicon-enhanced dynamic modulation layer for evolving requirements elicitation from app reviews. This approach overcomes the limitations of conventional FT methods in low-resource and noisy scenarios of app reviews, demonstrating superior performance and application potential.