Skill UI

A comprehensive guide for designing, auditing, and reporting security experiments for top-tier conferences like ACM CCS. It emphasizes moving beyond simple benchmarks by mapping every security claim to undeniable evidence, including exploit runs on named targets, overhead measurements, and evaluation against adaptive attackers. Used for rigorous academic research defense.

A comprehensive guide for academic researchers on designing, auditing, and reporting rigorous studies (experiments, field deployments, surveys, etc.) for Human-Computer Interaction (HCI) papers. It details best practices for quantitative power analysis, qualitative thematic analysis, ethical reporting, and mixed-methods integration to ensure research claims are strongly supported by evidence.

A comprehensive guide for designing and auditing empirical research in CSCW. It emphasizes methodological pluralism, defining distinct standards of rigor for various techniques like ethnography, trace analysis, surveys, and mixed methods. The guide mandates pre-design planning, explicitly addressing ethical concerns, unit of analysis, and aggregation logic before data collection.

This guide provides a comprehensive framework for designing rigorous, falsifiable system evaluations for academic research papers. It details best practices across four critical evidence layers: end-to-end performance, mechanism decomposition, operational cost, and boundary testing. Learn how to ensure workload realism, maintain baseline fairness, structure your experiment matrix, and avoid common pitfalls to meet top-tier systems research standards.

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 designing, executing, and reporting high-stakes research studies in Software Engineering (SE), tailored to meet the stringent standards of top academic conferences like ICSE. It covers best practices for evidence-claim matching, advanced statistical rigor (non-parametric tests, effect sizes), handling AI model ablations, and implementing threats-driven design to ensure findings are robust, valid, and maximally reproducible.

This guide provides rigorous standards for designing, validating, and reporting security research, particularly for high-stakes academic conferences like IEEE S&P. It covers critical areas including adaptive adversary modeling, measurement validity (sampling, ethics), statistical reporting, establishing strong baselines/ablations, and adhering to ethical constraints (IRB, authorization) to ensure evidence is robust and reproducible.

This skill provides a comprehensive guide for authors determining the fit of their empirical software engineering (SE) manuscript for the International Symposium on Empirical Software Engineering and Measurement (ESEM). It helps authors rigorously check the study design, evidence bar, proper framing, and necessary re-framing required to transform a journal or arXiv-style paper into a high-quality conference submission suitable for ESEM reviewers. It focuses on measurement validity, experimental rigor, and evidence synthesis.

This tool serves as an expert workflow router for manuscripts targeting top-tier International Relations journals. It guides authors through the entire academic writing process—from initial topic selection and theory building to data analysis, experimental design, and final submission. Its primary function is to ensure the research maintains a strong, generalizable theoretical focus on international or cross-border phenomena, rather than focusing only on domestic case studies.

This guide provides comprehensive guidelines for designing, conducting, and auditing empirical studies in software engineering. It emphasizes achieving scientific rigor by matching every claim to specific, measured evidence, ensuring fair baselining (equal budget), and applying appropriate advanced statistics (non-parametric tests, effect sizes). Essential reading for researchers submitting papers based on performance evaluations.

This guide provides comprehensive methodological advice for academic submissions in Information Systems (IS), particularly for journals like JAIS. It covers selecting and rigorously defending diverse research designs—including behavioral experiments, causal inference (Economics of IS), design science, qualitative studies, and literature reviews. Use it when you need to validate the suitability of your methodology against your research claims and underlying phenomena.

A comprehensive guide for designing and presenting rigorous experiments for submissions to the Journal of Software (JOS). It guides users through setting clear Research Questions (RQ), selecting real-world datasets and baselines, applying advanced statistical testing (effect size, significance), conducting ablation studies, and addressing threats to validity (internal, external, conceptual). This ensures the evidence strongly supports the claims, meeting top-tier academic standards.