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This guide provides comprehensive standards for designing, executing, and reporting systems experiments for top-tier academic venues (like ICDE). It emphasizes methodological rigor, including mandatory ablation studies, reporting variance and tail latency (p99), demonstrating scaling trends (curves over points), disclosing cost trade-offs, and ensuring full hardware and software reproducibility to withstand adversarial review.

A comprehensive guide for authors targeting the IEEE CASE conference. This tool helps assess manuscript fit by focusing on automation science, manufacturing, logistics, and cyber-physical systems. It guides authors on how to re-frame algorithmic designs with practical operational evidence, structure the contribution, and anticipate reviewer expectations for top-tier automation venues.

A comprehensive guide for designing and auditing rigorous experiments for sensing systems and information processing claims. It emphasizes the necessity of measuring performance on real hardware (not simulation), accurately instrumenting ground truth, quantifying energy consumption, latency, and physical footprint, and establishing fair, skeptical baselines. This ensures scientific rigor and reproducibility in research submissions.

This comprehensive guide provides best practices and an audit checklist for designing robust, real-world experiments in mobile and wireless communication systems. It emphasizes moving beyond simulations, detailing necessary RF setups, quantifiable energy metrics, and reporting statistical distributions to ensure high academic credibility.

This comprehensive guide outlines best practices for designing rigorous and convincing technical evaluations for networked systems. It structures the process by requiring experiments to answer specific questions, advising authors to climb the evidence ladder from synthetic benchmarks to real-world deployment. Key concepts include reporting tail latencies (p99), establishing strong baselines, and maintaining meticulous provenance records to ensure reproducibility.

This comprehensive guide outlines the rigorous methodology required for designing high-impact evaluations of parallel systems and algorithms for top-tier conferences. It stresses that contributions must demonstrate the 'twin bar': both concurrency correctness (e.g., linearizability) and measurable scalability (e.g., strong vs. weak scaling). It covers best practices for establishing strong baselines, managing measurement hygiene (variance, pinning), and anticipating advanced reviewer scrutiny in HPC.

This guide provides a comprehensive audit checklist for designing and evaluating sensor-based systems (SenSys). It mandates rigorous, real-world measurements across five core axes: energy efficiency, on-device latency distribution, ground truth accuracy, deployment realism, and embedded AI footprint. It ensures that research results are based on measured hardware performance, not simulations or idealized baselines.

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

A comprehensive guide for designing and presenting highly rigorous technical evaluations for database or system papers. It covers establishing baseline necessity, proving mechanism causality through ablation, testing scalability across data sizes, and mandating the disclosure of loss cases to satisfy skeptical academic reviewers.