Automatically Augmenting GitHub Issues with Informative User Reviews
Development teams for mobile applications can receive thousands of user reviews daily. At the same time, these developers use different communication channels, such as the GitHub issue tracker. Although GitHub issues are accessible and manageable for developers, their content often differs starkly from what users write in app reviews. Issues may lack steps to reproduce bugs or insights that justify the priority of new feature requests. The sheer volume of user reviews for a popular app, combined with their heterogeneity and varying quality, makes manual integration into issue trackers unfeasible.
We present an approach that automatically augments GitHub issues with informative user reviews to bridge the gap between user feedback and developer-managed issues. Using a state-of-the-art large language model (LLM), our approach automatically retrieves user reviews with high semantic textual similarity (STS) to the issue content and suggests reviews that augment developers’ understanding of the issue. In this paper, we present large-scale quantitative and qualitative analyses to assess the feasibility of enriching development workflows with user-written information. Using over 37,000 issues and 750,000 reviews from 19 popular Free/Libre/Open Source Software (FLOSS) mobile applications, our approach augments 3,017 (8%) issues with 7,287 (1%) potentially informative reviews. In addition to providing insights into user-reported bugs and feature requests, the information from these matches points toward a novel and promising way to leverage user reviews for concerted app evolution.
Wed 10 SepDisplayed time zone: Auckland, Wellington change
10:30 - 12:00 | Session 1 - DocumentationResearch Papers Track / Industry Track / Registered Reports at Case Room 3 260-055 Chair(s): Ashkan Sami Edinburgh Napier University | ||
10:30 15m | APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation Research Papers Track Chengran Yang Singapore Management University, Singapore, Jiakun Liu Harbin Institute of Technology, Bowen Xu North Carolina State University, Christoph Treude Singapore Management University, Yunbo Lyu Singapore Management University, Junda He Singapore Management University, Ming Li Nanjing University, David Lo Singapore Management University | ||
10:45 15m | Automatically Augmenting GitHub Issues with Informative User Reviews Research Papers Track Arthur Pilone University of São Paulo, Marco Raglianti Software Institute - USI, Lugano, Michele Lanza Software Institute - USI, Lugano, Fabio Kon University of São Paulo, Paulo Meirelles University of São Paulo Pre-print | ||
11:00 15m | Can LLMs Update API Documentation? Research Papers Track Seonah Lee Gyeongsang National University, Jueun Heo , Katherine R. Dearstyne University of Notre Dame | ||
11:15 15m | RMGenie: An LLM-Based Agent Framework for Open Source Software README Generation Research Papers Track Xing Cui Institute of Software, Chinese Academy of Sciences, Jingzheng Wu Institute of Software, The Chinese Academy of Sciences, Zhiyuan Li , Tianyue Luo (Institute of Software Chinese Academy of Sciences), Xiang Ling Institute of Software, Chinese Academy of Sciences | ||
11:30 15m | Requirements Ambiguity Detection and Explanation with LLMs: An Industrial Study Industry Track Sarmad Bashir RISE Research Institutes of Sweden, Alessio Ferrari Consiglio Nazionale delle Ricerche (CNR) and University College Dublin (UCD), Muhammad Abbas Khan RISE Research Institutes of Sweden, Per Erik Strandberg Westermo Network Technologies AB, Zulqarnain Haider Alstom Rail AB, Sweden, Mehrdad Saadatmand RISE Research Institutes of Sweden, Markus Bohlin Mälardalen University Pre-print | ||
11:45 10m | Learning From the Best: What Makes Popular Hugging Face Models? A Registered Report Registered Reports | ||