Skill UI

A comprehensive guide for researchers submitting to ASE (Automated Software Engineering). It details mandatory requirements for ensuring research reproducibility, covering the Data Availability Statement, providing anonymized and runnable tools, pinning system provenance (commits, versions, SHAs), and managing cached LLM outputs. Essential for passing rigorous peer review.

A comprehensive guide detailing the complete academic review pipeline for IEEE/ACM Automated Software Engineering (ASE) submissions. It explains unique stages like the early-rejection gate, the criteria-bound revision process, and the difference between ASE's cycle and those of peer conferences (e.g., ICSE, FSE). Essential reading for authors aiming to submit high-quality research in automatic software engineering.

This skill provides deep intelligence by systematically scraping and analyzing a competitor's paid advertising campaigns across platforms like Meta and Google. It goes beyond simple ad collection, performing structured analysis on creative patterns, common hooks (e.g., Fear/Loss, Outcome), CTAs, and reverse-engineering their landing page funnels to reveal their core positioning and potential vulnerabilities. Ideal for product marketing, growth strategy, and campaign optimization.

This detailed guide helps authors assess the scientific rigor and scope fit for manuscripts targeting Composites Part B: Engineering. It covers advanced domains like composite mechanics, manufacturing processes, multiscale modeling, and multifunctional materials. It emphasizes that a strong contribution must demonstrate a clear, mechanism-driven advance from material science to structural application, supported by rigorous, quantitative experimental evidence and model validation.

This comprehensive guide assists researchers targeting Environmental Science & Technology (ES&T) submissions. It provides detailed heuristics on journal positioning, scope fit, required mechanistic rigor, data presentation standards, and structural elements. Use this checklist to re-frame and strengthen your manuscript before submission, ensuring alignment with the journal's high standards in environmental chemistry, green engineering, and environmental science.

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 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.

A comprehensive guide for positioning database research papers for top-tier conferences like ICDE. It teaches authors how to distinguish genuine novelty (new mechanisms) from engineering delta (implementation improvements), conduct deep literature audits, and compare their work against both academic venues and real-world production systems. Essential for improving manuscript eligibility and impact.

This guide outlines comprehensive standards for scientific reproducibility in data engineering. It details best practices for pinning hardware, software, datasets, and variance protocols, ensuring that all performance claims are verifiable and re-runnable by peers, making reproducibility a scored component of academic submission.

A comprehensive guide to the rigorous, multi-stage peer review process for top-tier data engineering conferences like ICDE. It details the two-round structure, area-chair coordination, single-blind dynamics, and the high standards set by 'builder-heavy' reviewers. Use this to strategize paper submission, understand key evaluation dimensions (novelty, soundness, evaluation), and anticipate common review objections.