Research Hub
Analyzing and documenting internet health and trustworthy AI to help shape a human-centered internet.
Latest Research
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Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing
Jan. 16, 2026Alberto Bacchelli, Marco Castelluccio, Konstantinos KitsiosThe 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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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.
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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 BesbesThe 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.
