Research Hub
Analyzing and documenting internet health and trustworthy AI to help shape a human-centered internet.
Latest Research
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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 CorreiaThe 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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Using LLMs to Bridge the Gaps in QA Test Plans at Firefox
Jan. 16, 2026Suhaib Mujahid, Marco Castelluccio, John Pangas, Ahmad AbdellatifThe 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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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 KhomhThe 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.
