Tools & Datasets | REALISE Lab | Concordia University

Tooldependency-management

OSSBridge

Community-based analytics for open-source library maintainers: see how the projects that depend on your library actually use its API, and how well your test suite covers the API methods your community relies on.

Reference paper: Towards Supporting Open Source Library Maintainers with Community-Based Analytics (ICSE 2026)

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Toolmining-repositories

GitHub File History

Track how the files, directories, and file extensions of a GitHub repository evolve over time, using data from the GitHub API.

Reference paper: What's Inside a GitHub Repository? An Empirical Study on the Contents of 10K Projects (ICSME 2026, NIER track)

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Datasetperformance-engineering

Mozilla Performance Measurements and Alerts

One year (May 2023 to May 2024) of Mozilla's performance testing data: 5,655 performance time series, 17,989 performance alerts, and annotations of the resulting bugs, as CSV files with the scripts used to collect them (CC BY 4.0).

Reference paper: A Dataset of Performance Measurements and Alerts from Mozilla (Data Artifact) (ICPE 2025)

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Datasetperformance-engineering

Mozilla Performance Change Point Annotations

174 Mozilla performance time series with change points manually annotated by 11 Mozilla engineers, plus annotation task metadata. A practitioner-validated ground truth for evaluating performance regression and change point detection methods (CC BY 4.0).

Reference paper: Exploring Statistical Change Point Detection Techniques for Performance Anomaly Detection at Mozilla (arXiv 2026)

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Toolperformance-engineering

SpotJMHBugs

A SpotBugs plugin that statically detects bad practices in JMH (Java Microbenchmark Harness) benchmarks, such as unused returned computations, accumulation inside loops, final primitive inputs, per-invocation fixtures, and zero forks.

Reference paper: What's Wrong With My Benchmark Results? Studying Bad Practices in JMH Benchmarks (IEEE TSE 2019)

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Benchmarkperformance-engineering

CollectionsBench

A JMH benchmark suite for comparing Java collection libraries (JDK, Fastutil, Guava, Koloboke, HPPC, Eclipse Collections, Trove, and more) on lists, sets, and maps under a common set of workloads.

Reference paper: Empirical Study of Usage and Performance of Java Collections (ICPE 2017)

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