标题： CMER：一种上下文感知的方法用于挖掘与伦理关注相关的应用评论 显示英文标题

标题： CMER: A Context-Aware Approach for Mining Ethical Concern-related App Reviews

摘要： 随着移动应用程序在我们日常生活中的日益普及，有关伦理的问题显著增加。 用户在应用评论中表达他们的反馈，经常强调伦理问题，例如隐私和安全。 纳入这些评论已被证明对软件工程的许多领域（例如需求工程、测试等）很有用。 然而，与伦理问题相关的应用评论通常使用特定领域的语言，并且通常被更通用的用户反馈类别（如应用可靠性与可用性）所掩盖。 因此，自动化提取是一项具有挑战性和耗时的工作。 本研究提出了CMER（一种\underline{C}上下文感知方法用于\underline{M}挖掘\underline{E}道德关注相关应用\underline{R}评论），一种结合自然语言推理（NLI）和仅解码器（类似LLaMA）大型语言模型（LLM）的新方法，以大规模提取与道德关注相关应用评论。 在CMER中，NLI通过使用特定领域的假设提供特定领域的上下文意识，而类似LLaMA的LLM则消除了分类任务中对标记数据的需求。 我们通过从超过382K条移动投资类应用评论的数据集中挖掘与隐私和安全相关的评论（PSRs）来评估CMER的有效性。 首先，我们评估了四种NLI模型，并将特定领域的假设结果与通用假设进行比较。 接下来，我们评估了三种LLM在分类任务中的表现。 最后，我们将最佳的NLI和LLM模型（CMER）结合，使用基于关键词的方法提取了2,178条之前研究未注意到的PSRs，从而证明了CMER的有效性。 这些评论可以进一步细化为可操作的需求工件。 显示英文摘要

摘要： With the increasing proliferation of mobile applications in our daily lives, the concerns surrounding ethics have surged significantly. Users communicate their feedback in app reviews, frequently emphasizing ethical concerns, such as privacy and security. Incorporating these reviews has proved to be useful for many areas of software engineering (e.g., requirement engineering, testing, etc.). However, app reviews related to ethical concerns generally use domain-specific language and are typically overshadowed by more generic categories of user feedback, such as app reliability and usability. Thus, making automated extraction a challenging and time-consuming effort. This study proposes CMER (A \underline{C}ontext-Aware Approach for \underline{M}ining \underline{E}thical Concern-related App \underline{R}eviews), a novel approach that combines Natural Language Inference (NLI) and a decoder-only (LLaMA-like) Large Language Model (LLM) to extract ethical concern-related app reviews at scale. In CMER, NLI provides domain-specific context awareness by using domain-specific hypotheses, and the Llama-like LLM eliminates the need for labeled data in the classification task. We evaluated the validity of CMER by mining privacy and security-related reviews (PSRs) from the dataset of more than 382K app reviews of mobile investment apps. First, we evaluated four NLI models and compared the results of domain-specific hypotheses with generic hypotheses. Next, we evaluated three LLMs for the classification task. Finally, we combined the best NLI and LLM models (CMER) and extracted 2,178 additional PSRs overlooked by the previous study using a keyword-based approach, thus demonstrating the effectiveness of CMER. These reviews can be further refined into actionable requirement artifacts.