A Dataset of End-User Reviews from the Apple App Store for Software Evolution Research

This dataset provides a comprehensive collection of 158,910 end-user reviews from 84 applications across 17 distinct categories on the Apple App Store (AAS). The dataset was created to address the need for rich, contextual user feedback for research in software engineering, particularly in the areas of requirements engineering, user experience analysis, and software evolution.

The data was systematically collected using a custom Python script that leverages the `app_store_scraper` library. Each record in the dataset includes the review title, the full review text, and the user's star rating (1-5), offering a multi-faceted view of user needs, reported issues, and overall sentiment.

This resource is intended for researchers, software developers, and educators. It is particularly useful for training and validating machine learning models for natural language processing tasks, performing sentiment analysis, identifying software issues, and extracting functional and non-functional requirements. The dataset's focus on detailed, structured reviews from the AAS makes it a valuable asset for data-driven software improvement and innovation.