AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,517 papers
Deep learning (DL) techniques are increasingly integrated into traditional software systems, giving rise to hybrid AI-enabled systems that combine neural models with program logic. While these systems exhibit remarkable capabilities, their complex and heterogeneous architectures pose significant challenges for reliabil...
Test driven development (TDD) is a controversial and interesting approach to software development; while many think of better tests as a primary purpose of TDD, in practice the goal is as much to use tests to encourage continued progress in coding. That goal however rests on the notion that TDD ensures tests are good e...
A human-LLM workflow that pairs each curated semantic mutant with an instructor-approved seed phrase for an on-demand LLM expansion, which shows that the curated semantic-mutant set contains a fraction of the mutants a traditional mutation engine produces.
SAT solving is a core computational challenge across hardware verification, electronic design, and planning; an FPGA can evaluate SAT candidates at hardware clock rates without software overhead, enabling sub-microsecond solving on small instances. Yet, the throughput-area tradeoffs of parallel FPGA SAT datapaths remai...
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This proposed dissertation investigates mutation testing from three complementary perspectives: how mutations interact with program executions and test oracles to produce mutant kills and reveal opportunities for test improvement, and how mutation testing is adopted, configured, sustained, and acted upon in popular ope...
This talk explores how agent-first development platforms like Google Antigravity shift the paradigm from manual test authoring to autonomous software verification, and describes recent advances in autonomous visual testing.
PyMut4SE is a novel mutation tool for Python that focuses on a comprehensive set of mutations for any Python project, providing a rich and extensible set of mutation operators, access to mutated source code and its characteristics, detailed execution and behavioral observations, and support for both selective mutation...
Memory-safety bugs are one of the oldest and most common sources of security vulnerabilities, and their modern-day prevalence is a consequence of the widespread use of non-memory-safe languages such as C and C++. Transitioning away from C and C++ to memory-safe languages, namely Rust, to eliminate memory-safety bugs is...
Fuzzing finds bugs by testing software behavior with high volumes of input. To create semantically valid inputs for different targets, traditional fuzzers require significant engineering effort. LLM-based fuzzing approaches can be adapted to create inputs for a wide range of software but introduce new issues: every tes...
The paper asks whether high-level conceptual specifications improve LLM-generated PBT quality and whether they help developers extend and maintain AI-generated systems (RQ2), and reports preliminary results applying Spinach to two open-source applications.
LLM-based testing can expose subtle reliability issues in numerical software, but monolithic prompts make it hard to inspect which guidance drives effectiveness. We study whether Agent Skills can make such workflows more explainable by factoring procedural knowledge into composable testing components. Using numerical i...
Specifying contracts as program behavior using Hoare triples is an established practice in software engineering. Compared to traditional testing approaches, formal verification of these contracts provides stronger guarantees of functional correctness. However, its practical adoption has remained limited since significa...
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.
GPT-Lab
· gpt-lab.eu
Sep 23, 2026
Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…
GPT-Lab
· gpt-lab.eu
Sep 17, 2026
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
We’re launching agentic video understanding across our latest Gemini models for improved accuracy and lower costs and token usage.
In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value — and why innovation resists simple formulas.
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