Skill UI

This guide outlines the rigorous standards for evaluating systems research, particularly for top-tier conferences like ATC. It provides detailed instructions on designing reproducible experiments, establishing fair baselines, utilizing realistic workloads, and reporting critical metrics such as tail latency, variance, and resource cost, ensuring the highest level of scientific rigor.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.

This guide provides comprehensive standards for designing, auditing, and reporting empirical evaluations in database systems research. It emphasizes the principle that evidence must proportionally match the claim, requiring the use of real-world workloads, fair and tuned baselines, thorough measurement of variance and state (warm/cold), and fully reproducible evaluation harnesses. It also outlines the higher bar for 'Experiments & Analysis' papers.

A comprehensive framework for ensuring rigorous scientific reproducibility in systems papers. It details how to establish a full provenance ledger (tracking hardware, software commits, and workloads), manage performance variance through repeated runs and statistical reporting, and implement automated pipelines so that all reported figures can be regenerated from raw data.

This comprehensive guide outlines the full project management lifecycle for submitting research papers to the ICASSP conference (IEEE Signal Processing Society). It details the annual timeline, critical milestones, backward planning heuristics, and best practices across various submission tracks to ensure timely and successful publication.

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 to ensuring reproducibility in radio frequency (RF) physical layer measurements. It details the necessity of maintaining a detailed provenance ledger—recording radio hardware, channel conditions, mobility patterns, and software versions at the time of the run. The goal is not bit-identical results, but documenting variance and methodology so that findings can be validated on different testbeds or later in time, while also addressing legal and ethical data shipping constraints.

This guide outlines advanced methodologies for achieving high reproducibility in networked-systems research. It details capturing crucial provenance ledgers—including topology, traffic traces, system configuration, and run details—to ensure that published results are verifiable and not dependent on ephemeral experimental conditions. It emphasizes characterizing inherent variance rather than demanding absolute determinism, providing a rigorous framework for academic publication.

This guide addresses the stringent requirements for empirical evaluation in compiler and runtime systems, aligning with SIGPLAN's guidelines. It covers critical aspects such as proper warmup discipline, reporting variance and confidence intervals, selecting principled benchmarks, pinning toolchains, and ensuring cross-platform validity. It provides a protocol for generating verifiable measurement logs for academic publications.

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.

A comprehensive guide for designing and auditing high-stakes networking experiments, particularly for top-tier academic submissions like SIGCOMM. It outlines the 'evidence ladder' from microbenchmarks to live deployment, emphasizing the need to select robust baselines, report full performance distributions (percentiles and variance), and accurately map the mechanism's operational break points.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.