AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,195 papers
Results show that FPSieve can reduce false positives, reduce manual effort, and improve OS migration validation throughput, on a large scale OS migration differential testing dataset collected from industrial practice.
This experience paper introduces a property-based testing approach for WHRs, finding 21 previously unknown bugs, including axis limit violations, entanglement issues, and missing interlock checks, and distilled a number of practical lessons learned.
SIBS is the first semantics-aware scheduling framework for incremental builds across configurations, addressing both inaccurate cost estimation and the limitations of greedy scheduling, and demonstrates that semantics-aware cost modeling and global scheduling provide substantial and robust efficiency gains across diver...
Agentic software-development systems increasingly wrap language models in workflows —repair loops, best-of-N samplers, multi-agent pipelines. We argue for a specific lens on why such structure helps and on how to design it automatically. Our position is that the value of an agentic workflow is the quality it converts a...
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This work presents QGenome, a semantic foundation for quantum software genomics that establishes a falsifiable framework and a concrete research agenda for gene mining, lineage recovery, cross-layer regression testing and repair, and evidence-grounded quantum software agents.
This analysis focused on their classification performance, stability, agreement with experts, top-2 candidate recovery, and multi-LLM voting workflows (S1-S4) along with a corresponding analysis on Keras.
The experience suggests that the PromptOps practices recently proposed for general LLM applications transfer directly to security engineering, and that a fixed evaluation corpus with cost-aware metrics is the single most valuable artifact a prompt-pipeline team can maintain.
It is concluded that developers should use LLMs at the source-code level to counter adversaries applying the same techniques on decompiled code and research should advance decompilation support for the growing language diversity in the iOS ecosystem.
Coding-agent trajectories are read as evidence about the software process, and analyses pool runs from many different tasks. The task predicts both the behaviour and the outcome, so a pooled estimate mixes a within-task and a between-task channel, and is read as a statement about runs. We quantify that mixing with the...
FitGen is presented, a validation guided repair framework combining structured prompt design with output, syntax, and semantic validation to automatically diagnose and repair executable fitness functions.
This work examines three test-generation strategies that vary in coverage and emphasis on ambiguous behaviours and discusses the implications for autonomous software engineering pipelines in which a single model generates both code and tests from the same specification.
The number of exploited software vulnerabilities rises every year, and their impact grows as software systems become ever more interconnected. To detect such vulnerabilities in large codebases, many organizations rely on Static Application Security Testing (SAST) tools. However, SAST tools can produce high volumes of f...
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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