Skill UI

A comprehensive guide for authors preparing an artifact appendix (ae.tex) for major conferences like ASPLOS. Learn how to structure your submission to achieve Available, Functional, and Reproducible badges. Covers managing complex dependencies (hardware, software, data), structuring the repository for evaluators, and optimizing collaboration protocols to ensure successful post-acceptance validation.

This comprehensive guide establishes a rigorous framework for ensuring reproducibility in robot learning research. It details how to document both the "rerunnable half" (software, simulation versions, training configs, seeds) and the "auditable half" (physical hardware specs, sensors, firmware). Adhering to these standards allows experts to verify and rebuild the experimental results.

This skill guides researchers on selecting and positioning their academic paper for the journal *Computer Science*. It helps determine if a topic fits the journal's broad, multi-disciplinary scope, identifies the correct column (e.g., AI, Software), and advises on the appropriate paper type (review, research, application). It also provides strategic guidance on comparing it with other specialized journals, making it ideal for pre-drafting planning.

A detailed model of the ACM/IEEE Design Automation Conference (DAC) research manuscript review process. This guide explains the double-blind, TPC-driven, and single-shot nature of the evaluation, emphasizing the critical balance between genuine novelty and measured Quality-of-Results (QoR). It outlines the high standards for evidence fairness, benchmark credibility, and ablation studies required for acceptance, differentiating DAC's approach from architecture and software venues.

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 package guides researchers on assembling a comprehensive deposit to meet the high standards of Experimental Economics (ESA) journals. It is crucial for academic submissions, ensuring that the materials—including participant instructions, experiment software, raw data, and master analysis code—are complete enough for others to both reproduce the statistical findings and replicate the entire economic experiment.

This guide provides a rigorous framework for designing and auditing empirical studies in software engineering (SE). It emphasizes that evidence must proportionally match the claims made, covering topics like using real subject systems, establishing fair baselines, calculating SE-standard statistics (effect sizes, CIs), and ensuring thorough provenance, especially for LLM-based work. It helps researchers strengthen their research methodology before publication.

A comprehensive guide for authors submitting to top-tier Software Engineering (SE) conferences (like FSE, ICSE). It details how to structure the Related Work section by focusing on the 'delta'—the unique difference—between your work and prior art. It emphasizes covering the full SE literature landscape, maintaining anonymity, and correctly declaring overlaps with concurrent or prior versions.

This guide helps researchers determine the optimal academic venue for their software engineering work. It compares major conferences (FSE, ICSE, ASE, ISSTA) and journals, advising on contribution shape, evidence maturity, and community fit to maximize publication chances and fit the research into the correct academic community.

This comprehensive guide provides a backward-planning workflow for submitting an Empirical Software Engineering (SE) paper to FSE. It manages the entire academic publication lifecycle, from initial venue confirmation and evidence lock to submission, major revision, and final presentation. It outlines critical milestones, deadlines, and potential failure modes to ensure a structured and successful academic publishing process.

A comprehensive guide for revising empirical papers submitted to top-tier Software Engineering conferences (like FSE). This framework emphasizes structuring the paper around a clear SE contribution, treating research questions as contracts, backing every claim with proportional evidence, and rigorously arguing threats to validity, rather than merely listing them. It ensures the paper maintains maximum academic rigor and focus.

This guide outlines the mandatory open data and code requirements for publishing in Global Change Biology (GCB). It details that primary and secondary data, along with supporting code/software, must be archived in public, DOI-minting repositories (e.g., Dryad, Zenodo) prior to acceptance. It emphasizes that 'available on request' is not accepted and provides a structured checklist for ensuring reproducibility and proper metadata submission.