Library
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A Dataset of Performance Measurements and Alerts from Mozilla (Data Artifact)
Jan. 16, 2026Diego Elias Costa, Suhaib Mujahid, Marco Castelluccio, Gregory Mierzwinski, Mohamed Bilel BesbesOpen sourceThe 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.
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Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing
Jan. 16, 2026Alberto Bacchelli, Marco Castelluccio, Konstantinos KitsiosGenerative AI / Open sourceThe 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.
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A Comparison of Conversational Models and Humans in Answering Technical Questions: the Firefox Case
Jan. 16, 2026Marco Castelluccio, Daniel Coutinho, Anita Sarma, Marco Gerosa, Caio Barbosa, Alessandro Garcia, Igor Steinmacher, Joao CorreiaGenerative AI / Open sourceThe 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.
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Impact of LLM-based review comment generation in practice: A mixed open-/closed-source user study
Jan. 16, 2026Suhaib Mujahid, Marco Castelluccio, Doriane Olewicki, Benjamin Mah, Leuson Da Silva, Arezou Amini, Sarra Habchi, Bram Adams, Foutse KhomhGenerative AI / Open sourceThe 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.
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Using LLMs to Bridge the Gaps in QA Test Plans at Firefox
Jan. 16, 2026Suhaib Mujahid, Marco Castelluccio, John Pangas, Ahmad AbdellatifGenerative AI / Open sourceThe 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.
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Unveiling the Potential of a Conversational Agent in Developer Support: Insights from Mozilla’s PDF.js Project
July 10, 2024Marco CastelluccioOpen sourceThis 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
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Mind the Gap: What Working With Developers on Fuzz Tests Taught Us About Coverage Gaps
April 24, 2024Christian Holler, Jason Kratzer, Andy Zaidman, Alberto Bacchelli, Marco Castelluccio, Carolin BrandtPrivacy, security & tracking -
Predicting the Impact of Crashes Across Release Channels
Jan. 24, 2024Diego Elias Costa, Suhaib Mujahid, Marco CastelluccioPrivacy, security & tracking -
SZZ in the time of pull requests
Sept. 7, 2022Alberto Bacchelli, Enrico Fregnan, Calixte Denizet, Fernando Petrulio, Gul Calikli, Emma Humphries, Marco Castelluccio, David Ackermann, Sylvestre Ledru -
Works for me! cannot reproduce–a large scale empirical study of non-reproducible bugs
May 30, 2022Mohammad Masudur Rahman, Marco Castelluccio, Foutse KhomhPrivacy, security & tracking
