Skill UI

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.

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.

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 details best practices for achieving reproducibility in highly theoretical computer science papers, such as those submitted to ICALP. Since artifact evaluation is absent, reproducibility hinges on the ability for any reader to independently verify every step of the proof. Key requirements include writing self-contained proofs, providing verifiable computational certificates, maintaining consistency between submissions, and optionally using formal verification tools like Coq or Lean.

This guide outlines the standards for ensuring the verifiability and rigor of theoretical computer science papers, particularly for venues like ICDT. It mandates complete proofs, precise pinning of models and assumptions, explicit matching of upper and lower bounds, and strict internal consistency across all claims and full versions. Use this checklist before submission to elevate academic quality.

A comprehensive guide for authors submitting to major academic conferences (e.g., ICSE). It details the strict rules for differentiating mandatory core content (the 10-page limit) from supporting evidence, such as extended proofs, scripts, and raw data, which must be placed in an anonymized supplementary package. Learn the 'summarize-and-point' discipline to ensure verifiability and maintain reviewer trust.

This guide outlines best practices for packaging research artifacts (code, data, proofs, models) submitted for major AI conferences like IJCAI. It helps researchers create convincing, anonymous, and reproducible evidence to support their claims, even when formal artifact evaluation tracks are unavailable.

This guide helps researchers determine the optimal conference or journal venue for their software engineering contribution. It specifically addresses whether the core of the work is a testing or analysis technique evaluated on real subjects (ISSTA), or if it fits better within broader Software Engineering (ICSE/FSE), formal verification (PLDI/CAV), or applied domains.

A guide for authors submitting to theoretical computer science venues (ITCS). It strictly defines what constitutes valid evidence for a mathematical claim, emphasizing that complete proofs are paramount. The guidelines detail how to use worked examples, separating objects, and appropriately scoped computations, while strongly advising against using empirical benchmarks or performance metrics.

This guide details the crucial rules for structuring a scientific paper submitted to conferences like ITCS. It dictates how content—including main theorems, proofs, and motivation—must be distributed across three locations: the main body, the submitted appendix, and the external full version (arXiv). The core principle ensures that all decision-critical evidence for accepting the paper resides within the submitted PDF, preventing reviewers from relying on external or unread preprints.

This structured workflow guide is designed for researchers submitting to top-tier management science journals. It acts as a comprehensive router, guiding the manuscript through the rigorous academic process: from problem definition and model construction, through theoretical proofs, algorithm analysis, and numerical validation, ensuring methodological depth over mere empirical findings.

This guide provides a comprehensive second-pass capability check for academic manuscripts, designed to meet the rigorous standards of top-tier econometric journals like the Journal of Econometrics (JoE). It verifies abstract conciseness, structures the introduction contribution-first, improves the legibility of complex proofs, and ensures adherence to Elsevier/elsarticle formatting conventions.