Biblioteca

A Dataset of Performance Measurements and Alerts from Mozilla (Data Artifact)

The paper introduces a publicly available dataset from Mozilla Firefox that addresses the lack of real-world data for studying performance regressions, combining performance measurements, expert-validated alerts, and rich metadata.

Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing

The paper introduces BLAST, a tool that combines large language models with search-based software testing to automatically generate issue-reproducing tests from issue-patch pairs, addressing the common absence of such tests in practice.

A Comparison of Conversational Models and Humans in Answering Technical Questions: the Firefox Case

The study evaluates Retrieval-Augmented Generation (RAG) with LLMs for assisting Mozilla Firefox developers by comparing human responses, standard LLM, and RAG-enhanced LLM on real developer queries.

Impact of LLM-based review comment generation in practice: A mixed open-/closed-source user study

The study evaluates RevMate, an LLM-based code review assistant, through a large-scale live user study at Mozilla and Ubisoft, analyzing over 587 patch reviews.

Using LLMs to Bridge the Gaps in QA Test Plans at Firefox

The study explores using LLMs to automatically generate test plans for Firefox features, aiming to reduce the manual effort and blind spots in traditional QA planning.

Unveiling the Potential of a Conversational Agent in Developer Support: Insights from Mozilla’s PDF.js Project

This paper presents an investigation into whether AI can be leveraged to assist developers and guide open source community members. It introduces DevMentorAI, an LLM-based tool that uses a RAG approach to answer developer questions, which is then evaluated with a case study on Mozilla's PDF.js proje

Mind the Gap: What Working With Developers on Fuzz Tests Taught Us About Coverage Gaps

Predicting the Impact of Crashes Across Release Channels

SZZ in the time of pull requests

Works for me! cannot reproduce–a large scale empirical study of non-reproducible bugs