Tag: AI-generated code
AI-Assisted Development Under Deadline: What It Takes to Ship Production Code on an Unfamiliar Stack
Ihor Riazantsev | | AI code risk, AI coding tools, AI-assisted development, AI-generated code, Claude Code, code consistency, code review, devops teams, engineering discipline, feature isolation, manual testing, memory leak debugging, Next.js development, production readiness, production validation, production-ready code, rapid feature delivery, React development, software architecture, software engineering judgment, technical debt, TypeScript development, unfamiliar codebaseHow Claude Code accelerated development on an unfamiliar stack while proving that production-ready AI-generated code still requires human judgment ...
AI Moved the Bottleneck From Writing Code to Understanding and Trusting It
Ted Julian | | AI code generation, AI code security, AI coding tools, AI investment ROI, AI-generated code, application security, automated code review, code quality, code review, codebase visibility, compliance, dependency risk, developer productivity, devsecops, Engineering leadership, engineering metrics, engineering ROI, production incidents, pull request review, SCA, software composition analysis, software delivery, software development productivity, software governance, technical debtAI coding boosts velocity, but without stronger review, governance and codebase visibility, teams risk higher costs, security gaps and production failures. TL;DR ...
Your AI Testing Framework Might Be Passing Tests It Should Be Failing
Engineering teams are not evaluating whether AI belongs in their testing workflows anymore, but are discovering, sometimes painfully, that the architectural decisions they made when integrating it are the ones that will ...
Lightrun: IT is in the Dark Over Coding Assistant Runtime Visibility
Adrian Bridgwater | | AI coding assistants, AI-generated code, AI-Powered Engineering, AI-SRE Tools, Application Performance Management (APM), automated remediation, Autonomous Engineering, developer productivity, Lightrun, Manual Debugging, Memory Usage, Observability Stacks, platform engineering, Production Failures, Real-Time Data, Redeploy Cycles, root cause analysis, Runtime Visibility, Site Reliability Engineering (SRE), software reliability, State of AI-Powered Engineering Report 2026, Static Code Generation, System Behavior, tribal knowledge, Variable States, Verification BottleneckSoftware runs, but sometimes it doesn’t… and that’s often down to a lack of runtime visibility in relation to platform engineering teams being able to trust coding assistants and AI-powered site reliability ...
The Risk Profile of AI-Driven Development
Ilkka Turunen | | AI-generated code, AIBOM, autonomous development, autonomous security, challenge bottleneck, CI CD gating, cloud-native security, Dependency Management, GRC engineering, hallucinations, IDE controls, license compliance, machine speed enforcement, OpenSSF Gemara, Outdated Dependencies, policy based dependency selection, prompt level governance, review bottleneck, runtime transparency, SBoM, secure by default, shift left security, Software Supply Chain, threat modeling, transitive dependencies, trusted registries, typosquattingAnalysis arguing that AI-driven code generation accelerates dependency decisions and expands supply-chain risk, requiring shift-left governance, prompt-level controls, automated SBOM/AIBOM visibility, threat-modeling as engineering, and autonomous security to match autonomous development ...
Google’s Conductor Now Reviews the Code it Helps You Write
Google's Conductor for Gemini CLI now reviews AI-generated code against your project standards. Agentic development just got a verification layer ...
Former GitHub CEO Bets $60M That Developer Tools Need a Factory Reset for the AI Age
Tom Smith | | AI coding agents, AI-generated code, automation, Checkpoints, code governance, code review, collaboration, command-line tool, developer tools, devops, funding, software development lifecycleThomas Dohmke's new company, Entire, aims to redefine the software development lifecycle for AI-driven coding by focusing on post-code generation processes. With a $60 million seed round, Entire's first product, Checkpoints, enhances ...
Qodo Adds Multiple AI Agents to Code Review Platform
Mike Vizard | | AI code review, ai copilot, AI-assisted testing, AI-generated code, automated code review, Bitbucket, code review, code review benchmark, defect detection, devops, F1 score, github, gitlab, LLM, memory-enabled agents, multi-agent AI, pull requests, Qodo, software qualityQodo 2.0 adds memory-enabled, task-specific AI agents to its LLM-based code-review platform, improving defect recall and F1 performance to help DevOps scale code quality as AI-generated code rises ...
Surprise! Everybody Uses AI Tools for Software Development, Few Do So Securely
Teri Robinson | | AI security, AI-generated code, appsec, code security automation, Dependency Management, devsecops, SBoM, secure coding, software supply chain riskAI is generating code faster than teams can secure it, widening software supply chain risk and exposing major gaps in AppSec and governance ...
Code Quality and Security Risks of AI-Generated Code
Joydip Kanjilal | | AI attack surface, AI code security, AI coding assistants, AI development tools, AI model limitations, AI security challenges, AI training data vulnerabilities, AI-generated code, AI-powered development risks, automated code review, ChatGPT code generation, CI/CD security scanning, code quality risks, code security best practices, dependency risks, developer awareness, GitHub Copilot risks, hallucinated code, human-led code review, inefficient algorithms, insecure AI code, insecure code patterns, poor code maintainability, secure coding practices, software development security, spaghetti code, technical debt from AI toolsAI-powered tools and coding assistants have become popular and widely used among software developers for several reasons. They promise to deliver not only speed and comfort but also a tool for filling ...
JFrog Adds Ability to Track Usage of AI Coding Tools
Mike Vizard | | AI API management, AI code auditing, AI coding tools, AI compliance tracking, AI Governance, AI in DevOps, AI model inventory, AI risk mitigation, AI-generated code, application security, code risk detection, devsecops, Large Language Models, LLM security, MLOps, SCA, secure coding practices, Shadow AI detection, software composition analysis, software provenance, Software Supply Chain Security, software vulnerabilities, source code scanningJFrog introduces AI-Generated Code Detection and Shadow AI Detection tools to identify AI-created code, track model usage, and enhance DevSecOps governance across software supply chains ...
Sonar Previews Service to Improve Quality of AI Generated Code
Mike Vizard | | AI code quality, AI code review, AI code security, AI model training, AI-generated code, bug reduction, code vulnerabilities, cybersecurity in AI, devsecops, Large Language Models, reinforcement learning, secure coding, software development, Sonar platform, SonarSweepSonar’s SonarSweep improves AI-generated code by reducing bugs and vulnerabilities, helping organizations train more reliable AI models ...
