User Reviews as a Source for Usability Requirements: A Precursor Study on Using Large Language Models

• URL: http://arxiv.org/abs/2605.12657v1
• Date: Tue, 12 May 2026 19:05:04 GMT
• Title: User Reviews as a Source for Usability Requirements: A Precursor Study on Using Large Language Models
• Abstract summary: This paper provides a dataset of 300 user reviews containing usability-relevant aspects from three different types of apps.<n>We also analyze if the performance of LLMs in understanding user reviews is comparable to the performance of human raters.
• Score: 1.6498033620778052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is known that user-centered approaches to requirements engineering in general lead to a better suited product for the end-users. LLM4RE provides promising approaches to support the requirements elicitation process (e.g. classification of requirements). Previous approaches focus on Machine-Learning (ML) or Deep-Learning (DL) aspects, which require intensive training with a large amount of manually labeled data. LLMs, on the other hand, are pre-trained on large amounts of user-generated text data, enabling a user-centric workflow to analyze requirements. In this paper, we explore the possibility of exploiting the improved natural language understanding of LLMs, rather than strict ML classification, together with the mass extraction of user reviews to analyze if the performance of LLMs in understanding user reviews is comparable to the performance of human raters. This enables a quick and cheap workflow for development teams to gather and process their userś requirements. This paper provides three major contributions: (1) We provide a completely coded dataset of 300 user reviews containing usability-relevant aspects from three different types of apps, that were labeled by two human raters and by an LLM. (2) We build an initial prompt, based on two prompt engineering iterations and specifically developed coding guidelines derived from the 10 Nielsen Usability Heuristics, for LLMs to filter usability relevant user reviews. (3) We determine that LLMs are generally able to recognize usability as a non-functional requirement in user reviews, in terms of their F-score, but the performance and reliability is strongly dependent on the prompt.

Related papers

• A Human-Centered Workflow for Using Large Language Models in Content Analysis [0.0]
  This paper conceptualizes Large Language Models as universal text processing machines.<n>It presents a comprehensive workflow for employing LLMs in three qualitative and quantitative content analysis tasks.
  arXiv  Detail & Related papers  (2026-02-27T14:32:29Z)
• Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs [66.63911043019294]
  Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them.<n>This paper focuses on the use of LLM techniques to prepare data for diverse downstream tasks.<n>We introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning, standardization, error processing, imputation, data integration, and data enrichment.
  arXiv  Detail & Related papers  (2026-01-22T12:02:45Z)
• Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation [2.3650193864974978]
  Large language models (LLMs) can perform recommendation tasks by taking prompts written in natural language as input.<n>This paper focuses on a single-user setting, where no information from other users is used.
  arXiv  Detail & Related papers  (2025-07-17T20:26:00Z)
• SCAN: Structured Capability Assessment and Navigation for LLMs [54.54085382131134]
  textbfSCAN (Structured Capability Assessment and Navigation) is a practical framework that enables detailed characterization of Large Language Models.<n>SCAN incorporates four key components:.<n>TaxBuilder, which extracts capability-indicating tags from queries to construct a hierarchical taxonomy;.<n>RealMix, a query synthesis and filtering mechanism that ensures sufficient evaluation data for each capability tag;.<n>A PC$2$-based (Pre-Comparison-derived Criteria) LLM-as-a-Judge approach achieves significantly higher accuracy compared to classic LLM-as-a-Judge method
  arXiv  Detail & Related papers  (2025-05-10T16:52:40Z)
• Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale [53.059480071818136]
  Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks.<n> PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories.<n>We evaluate LLM chatbots' ability to identify the most suitable response according to the current state of the user's profile.
  arXiv  Detail & Related papers  (2025-04-19T08:16:10Z)
• UQABench: Evaluating User Embedding for Prompting LLMs in Personalized Question Answering [39.79275025010785]
  name is a benchmark designed to evaluate the effectiveness of user embeddings in prompting large language models for personalization.<n>We conduct extensive experiments on various state-of-the-art methods for modeling user embeddings.
  arXiv  Detail & Related papers  (2025-02-26T14:34:00Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Using Large Language Models to Understand Telecom Standards [35.343893798039765]
  Large Language Models (LLMs) may provide faster access to relevant information. We evaluate the capability of state-of-art LLMs to be used as Question Answering (QA) assistants. Results show that LLMs can be used as a credible reference tool on telecom technical documents.
  arXiv  Detail & Related papers  (2024-04-02T09:54:51Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• KoLA: Carefully Benchmarking World Knowledge of Large Language Models [87.96683299084788]
  We construct a Knowledge-oriented LLM Assessment benchmark (KoLA) We mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering $19$ tasks. We use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, to evaluate the capacity to handle unseen data and evolving knowledge.
  arXiv  Detail & Related papers  (2023-06-15T17:20:46Z)
• How Effective are Large Language Models in Generating Software Specifications? [14.170320751508502]
  Large Language Models (LLMs) have been successfully applied to numerous Software Engineering (SE) tasks.<n>We conduct the first empirical study to evaluate the capabilities of LLMs for generating software specifications from software comments or documentation.
  arXiv  Detail & Related papers  (2023-06-06T00:28:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.