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2,429 papers
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.
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.
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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.
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.
Under this model, generative components ground test intent at authoring time into reviewable artifacts, rather than re-evaluating locators during every test run, and semantic agreement is achievable but not automatic: nine of twenty guarantee-surface pairs resolve.
Eight lessons from six months of developing CHERI-PIT, an LLM-agent pipeline that ports C/C++ projects to the memory-safe pure-capability (purecap) ABI of CHERI hardware on Arm Morello, trace a path toward a porting tool that emits reproducible and auditable port recipes.
This vision paper argues that trustworthy AI-assisted software engineering requires behavioural accountability: making implicit assumptions explicit, surfacing behavioural violations, and producing auditable evidence for developers and CI pipelines.
Viewing trustworthy automation through this governance perspective reveals two complementary governance responsibilities: Before automated execution, trustworthiness objectives must be systematically operationalised into lifecycle artefacts that guide or constrain engineering behaviour and after execution, operational...
This paper argues that AI and program analysis are not competing technologies but complementary capabilities, and envision enterprise software engineering as a collaboration in which program analysis provides evidence, AI provides intelligence, and human engineers contribute the judgment and wisdom required to build tr...
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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