Skill UI

This guide details the required process for authors responding to ASE (Automated Software Engineering) reviews. It distinguishes between the initial rebuttal and the formal, criteria-bound summary-of-changes required during a revision round. Key principles include maintaining double-anonymity, systematically addressing every reviewer criterion, and focusing on decision-critical points.

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 detailed guide for authors writing the Related Work section of an Automated Software Engineering (ASE) paper. It teaches the crucial 'delta argument' methodology, emphasizing that positioning must be against prior automated tools, covering multiple SE literature lanes, and conducting fair, head-to-head empirical comparisons to clearly delineate the novelty of the proposed work.

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.

Provides comprehensive guidance for researchers deciding the optimal conference venue for Software Engineering (SE) research. It distinguishes between contributions that focus on *automation* (techniques or tools that automate SE tasks) suitable for ASE, versus those that are broad empirical findings, theoretical results, or pure ML advances, directing them to venues like ICSE, FSE, or ISSTA.

A comprehensive, step-by-step guide for researchers planning and executing a submission to the ASE (Automated Software Engineering) conference. Covers the entire academic lifecycle, from initial topic selection and evidence gathering (reproducibility) to abstract registration, double-anonymous submission, navigating the early-rejection gate, rebuttal, criteria-bound revision, and final camera-ready artifact submission.

Provides comprehensive guidelines for structuring and writing academic papers for Automated Software Engineering (ASE) conferences. It teaches authors how to present an automated task, emphasize the automation design over the deep learning model, provide evidence from real subjects, and adhere to strict formatting and argument presentation rules to maximize acceptance chances.

A comprehensive guide for authors targeting top-tier journals in systems and control theory (e.g., Automatica). This skill helps authors re-frame their work from narrow application studies to general, methodologically rigorous advances. It details the necessary components, including theoretical guarantees (stability, convergence, optimality), structured proofs, and necessary breadth to meet the highest academic standards.

This skill automatically converts structured data from Excel (.xlsx) workbooks into a clean, readable Markdown format. It is essential for enabling accurate analysis, summarization, and data extraction of spreadsheet contents, especially when the user asks to 'read', 'analyze', or 'compare' data without specifying a conversion method. It supports both single files and batch processing of entire folders.

This skill automates the entire GitHub Pull Request (PR) review cycle, specifically targeting Copilot Code Review comments. It runs a robust loop: automatically triggering reviews, triaging open threads, dispatching parallel fixes that adhere to repo conventions, committing changes, and resolving comments. It continues iterating until the PR is deemed clean and ready for merge, minimizing manual effort and ensuring high code quality.

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.