The Bench conference encompasses a wide range of topics in benchmarks, datasets, metrics, indexes, measurement, evaluation, optimization, supporting methods and tools, and other best practices in computer science, medicine, finance, education, management, etc. Bench's multidisciplinary and interdisciplinary emphasis provides an ideal environment for developers and researchers from different areas and communities to discuss practical and theoretical work. The topics of interest include, but are not limited to the following:

Topics

    Evaluation theory and methodology
        Formal specification of evaluation requirements
        Development of evaluation models
        Design and implementation of evaluation systems
        Analysis of evaluation risk
        Cost modeling for evaluations
        Accuracy modeling for evaluations
        Evaluation traceability
        Identification and establishment of evaluation conditions
        Equivalent evaluation conditions
        Design of experiments
        Statistical analysis techniques for evaluations
        Methodologies and techniques for eliminating confounding factors in evaluations
        Analytical modeling techniques and validation of models
        Simulation and emulation-based modeling techniques and validation of models
        Development of methodologies, metrics, abstractions, and algorithms specifically tailored for evaluations

    The engineering of evaluation
        Benchmark design and implementation
        Benchmark traceability
        Establishing least equivalent evaluation conditions
        Index design, implementation
        Scale design, implementation
        Evaluation standard design and implementations
        Evaluation and benchmark practice
        Tools for evaluations
        Real-world evaluation systems
        Testbed

    Data set
        Explicit or implicit problem definition deduced from the data set
        Detailed descriptions of research or industry datasets, including the methods used to collect the data and technical analyses supporting the quality of the measurements
        Analyses or meta-analyses of existing data
        Systems, technologies, and techniques that advance data sharing and reuse to support reproducible research
        Tools that generate large-scale data while preserving their original characteristics
        Evaluating the rigor and quality of the experiments used to generate the data and the completeness of the data description

    Benchmarking
        Summary and review of state-of-the-art and state-of-the-practice
        Searching and summarizing industry best practice
        Evaluation and optimization of industry practice
        Retrospective of industry practice
        Characterizing and optimizing real-world applications and systems
        Evaluations of state-of-the-art solutions in the real-world setting

    Measurement and testing
        Workload characterization
        Instrumentation, sampling, tracing, and profiling of large-scale, real-world applications and systems
        Collection and analysis of measurement and testing data that yield new insights
        Measurement and testing-based modeling (e.g., workloads, scaling behavior, and assessment of performance bottlenecks)
        Methods and tools to monitor and visualize measurement and testing data
        Systems and algorithms that build on measurement and testing-based findings
        Reappraisal of previous empirical measurements and measurement-based conclusions
        Reappraisal of previous empirical testing and testing-based conclusions