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