Skill UI

A comprehensive guide for designing and presenting rigorous experiments and simulations for top-tier journals in Control Theory, Robotics, and Smart Systems. Covers RQ-driven design, establishing fair baselines, providing theoretical proofs of stability/convergence, performing ablation studies, and ensuring statistical significance, helping authors build an undeniable evidence chain for double-blind external review.

A comprehensive guide for researchers and reviewers designing or auditing Automated Software Engineering (ASE) evaluations. It outlines the necessary rigor, including the use of real subject systems, establishing fair and runnable tool baselines, performing targeted ablations (especially for LLMs), defining explicit oracles, and ensuring full data and model provenance to guarantee reproducible and scientifically sound results.

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.

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.

This guide provides best practices for designing and auditing academic evaluations of programming language features and compilers, following standards used in venues like PLDI. It covers selecting defensible benchmark suites, establishing strong baselines, performing targeted ablations, and measuring multiple performance metrics (runtime, compile time, memory) to ensure research claims are reproducible and scientifically sound.

Provides comprehensive guidelines for designing rigorous technical evaluations for interactive systems, specifically tailored for top-tier venues like UIST. It emphasizes matching evaluation methods to specific artifact claims, reporting distributions over means, closing the interaction loop, and performing thorough ablations against real-world baselines. This framework ensures the scientific rigor and credibility of submitted research.

Design, report, and audit exact, decomposition, heuristic, simulation, and learning-based solution routes for Journal of Systems Engineering, covering pseudocode, convergence, complexity, baselines, ablations, parameters, scalability, and reproducible computational evidence.