おなじみのプロセスやワークフローを特徴とする継続的インテグレーション（CI）の時代は、急速に終わりを迎えつつあります。従来のCIは、開発者が小規模で頻繁な反復的なコミットを行うことを前提としていました。今日、「継続的」という側面そのものが変化しつつあります。

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\"WebPage\",\n \"@id\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/\",\n \"url\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/\",\n \"name\": \"The future is AC/DC: the Agent Centric Development Cycle | Sonar\",\n \"description\": \"The era of Continuous Integration, with its familiar processes and workflows, is rapidly coming to an end. Traditional CI relies on developers making small, frequent, iterative commits. Today, the “continuous” part is changing.\",\n \"inLanguage\": \"en\",\n \"publisher\": {\n \"@id\": \"https://www.sonarsource.com/#organization\"\n },\n \"sameAs\": [\n \"https://en.wikipedia.org/wiki/SonarQube\",\n \"https://en.wikipedia.org/wiki/Sonar_(company)\",\n \"https://en.wikipedia.org/wiki/Artificial_intelligence\",\n \"https://en.wikipedia.org/wiki/Systems_development_life_cycle\",\n \"https://en.wikipedia.org/wiki/Governance\",\n \"https://en.wikipedia.org/wiki/Software_development\",\n \"https://en.wikipedia.org/wiki/Agent-oriented_software_engineering\",\n \"https://en.wikipedia.org/wiki/AI_agent\"\n ]\n },\n {\n \"@type\": \"BlogPosting\",\n \"@id\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/#article\",\n \"mainEntityOfPage\": {\n \"@id\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/\"\n },\n \"headline\": \"The future is AC/DC: the Agent Centric Development Cycle\",\n \"description\": \"Continuous Integration is changing as agents work asynchronously and deliver larger code payloads. Sonar calls the emerging paradigm the Agent Centric Development Cycle (AC/DC).\",\n \"abstract\": \"The Agent Centric Development Cycle (AC/DC) describes Sonar's vision for the next evolution of the software development lifecycle, in which AI agents autonomously write, scan, and fix code within a continuous loop governed by SonarQube verification. In this model, AI coding agents such as Claude Code or Cursor use the SonarQube MCP Server to query quality gate status and issue data, self-correcting their output before a human developer reviews a pull request. AC/DC reframes the developer's role from primary code author to agent supervisor and quality governor, setting the standards that automated agents must meet rather than writing every line of code manually. Sonar's MCP Server, Agentic Analysis beta, and AI Code Assurance capability provide the tooling infrastructure needed to implement AC/DC workflows at scale within enterprise development environments.\",\n \"image\": [\n \"https://assets-eu-01.kc-usercontent.com/ef593040-b591-0198-9506-ed88b30bc023/7ec302f1-ae36-4b20-9bf1-1bbacb17cdd8/ac_dc_blog_hero_social_landscape_2x.webp?auto=format&fit=crop&h=1256&w=2400\"\n ],\n \"datePublished\": \"2026-03-02\",\n \"dateModified\": \"2026-03-02\",\n \"timeRequired\": \"PT14M\",\n \"articleSection\": \"AI\",\n \"inLanguage\": \"en\",\n \"author\": {\n \"@type\": \"Person\",\n \"name\": \"Tariq Shaukat\",\n \"jobTitle\": \"CEO\",\n \"image\": \"https://assets-eu-01.kc-usercontent.com/ef593040-b591-0198-9506-ed88b30bc023/82e25ff2-5d3d-479a-8459-f9547274b2b1/698%20-%20Tariq%20Shaukat%402x.jpg?auto=format&fit=crop&h=800&w=800\",\n \"worksFor\": {\n \"@id\": \"https://www.sonarsource.com/#organization\"\n }\n },\n \"publisher\": {\n \"@id\": \"https://www.sonarsource.com/#organization\"\n }\n }\n ]\n}",{"_15":329,"_39":54,"_41":7423},[],{"_15":333,"_39":40,"_41":46},{"_15":336,"_39":40,"_41":46},{"_15":339,"_39":85,"_41":7427},[],{"_15":343,"_39":40,"_41":46},{"_15":346,"_39":40,"_41":46},{"_15":349,"_39":85,"_41":7431},[],{"_15":353,"_39":67,"_41":7433,"_73":7434},[],[],{"_187":7271,"_244":245,"_246":7436,"_248":249,"_250":7437,"_15":7438,"_253":7439,"_39":7440,"_256":257,"_258":245},"17f3ca8a-a85d-4fb8-97fc-2782ffe2e42a","2026-04-08T17:50:26.8601498Z","BlogPost | The future is AC/DC",[],"article_page",{"_15":374,"_39":67,"_41":7442,"_73":7443},[],[],{"_187":7265,"_244":245,"_246":7445,"_248":249,"_250":7446,"_15":7447,"_253":7448,"_39":382,"_256":257,"_258":245},"6ea89a7c-8769-4502-8de7-e2ebb46a40a8","2026-03-02T22:34:54.2797306Z","The future is AC/DC | Link action",[],{"_15":385,"_39":67,"_41":7450,"_73":7451},[],[],{"_187":7250,"_244":245,"_246":7453,"_248":367,"_250":7454,"_15":7455,"_253":7456,"_39":393,"_256":257,"_258":245},"3f86df8c-ba6a-4590-b52b-6409b4bc633a","2026-04-30T13:17:30.2399051Z","The future is AC/DC | Link",[],{"_15":148,"_39":54,"_41":7458},[7459],{"_15":7460,"_187":7461},"Text Left","left","padding__top",{"_15":7464,"_39":54,"_41":7465},"Padding - Top",[],"padding__bottom",{"_15":7468,"_39":54,"_41":7469},"Padding - Bottom",[],{"_15":7182,"_39":67,"_41":7471,"_73":7472},[],[],{"_15":4033,"_39":67,"_41":7474,"_73":7475},[],[],{"_187":7219,"_244":245,"_246":7477,"_248":367,"_250":7478,"_15":7479,"_253":7480,"_39":7481,"_256":257,"_258":245},"fa5786e2-58b2-4dba-9df3-27d44b9a183c","2026-04-30T13:17:15.3508509Z","Featured Post - Card block",[],"n5050_card_block",{"_15":7182,"_39":67,"_41":7483,"_73":7484},[],[],{"_15":4033,"_39":67,"_41":7486,"_73":7487},[],[],{"_187":70,"_244":245,"_246":7489,"_248":367,"_250":7490,"_15":7491,"_253":7492,"_39":7493,"_256":257,"_258":245},"b8ebeb63-fdad-4fd6-b337-a452480c087a","2026-04-30T13:16:43.3410253Z","Blog | Hero Block Index",[],"hero_block_index",{"_34":7495,"_242":7645},{"_7311":7496,"_7357":7505,"_7509":7510,"_120":7514,"_7516":7517,"_7624":7625,"_7631":7632,"_7638":7639},{"_15":7313,"_39":67,"_41":7497,"_73":7498},[7315],[7499],{"_34":7500,"_242":7503},{"_15":7501,"_47":7502},{"_15":7289,"_39":40,"_41":42},{"_15":49,"_39":40,"_41":50},{"_187":7315,"_244":245,"_246":7322,"_248":249,"_250":7323,"_15":42,"_253":7504,"_39":7325,"_256":257,"_258":245},[],{"_15":7506,"_39":67,"_41":7507,"_73":7508},"Article Tag",[],[],"exclude_facets_from_filters",{"_15":7511,"_39":67,"_41":7512,"_73":7513},"Exclude facets from filters",[],[],{"_15":122,"_39":54,"_41":7515},[],"cta_card",{"_15":7518,"_39":67,"_41":7519,"_73":7521},"CTA Card",[7520],"newsletter_signup___cta_card",[7522],{"_34":7523,"_242":7618},{"_7524":7525},"content",{"_15":7526,"_39":67,"_41":7527,"_73":7529},"Content",[7528],"mid_page_form_container___blog_signup___card___con",[7530],{"_34":7531,"_242":7612},{"_7211":7532,"_7534":7535,"_7229":7538,"_7545":7546,"_150":7553,"_7555":7556,"_120":7564,"_7566":7567,"_7180":7606,"_7185":7609},{"_15":7227,"_39":40,"_41":7533},"Get new blog posts delivered directly to your inbox!","success_headline",{"_15":7536,"_39":40,"_41":7537},"Success Headline","You’re all set!",{"_7231":7539,"_7233":7540,"_73":7541,"_398":7542,"_15":7543,"_39":7238,"_41":7544},[],[],[],[],"Body","

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March 30, 2026 — Early Access terms updated to reflect that Remediation Agent is now in beta.

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cleaner codebase results in less token usage","Research shows well-structured software helps AI coding agents use fewer tokens and spend less time re-reading files.","Prasenjit Sarkar","cta","creationDate","2026-05-14T16:00:00Z",[2415,12734],"Code quality","keywords",[],[12738,12739],"Category:AI","Category:Code quality","order","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. Authors\n\nOlivier Schmitt | Prasenjit Sarkar | Priyansh Trivedi\n\n\n\n\nThe prevailing take on AI-assisted software development goes something like this: AI agents don't get cognitively overloaded, they read fast, they don't care about naming conventions or nested logic; human limitations don’t apply. Put another way, cleaner code is critical for human readers only and doesn’t really matter for AI tools. \n\nSo maybe clean, structured code is a solved problem. Perhaps it’s an artifact of the old era. It's a reasonable-sounding argument. But we couldn't find evidence anyone had actually tested it. So we did.\n\nWhat we tested\n\nThe question sounds simple: working on the same task, does an AI agent behave differently on cleaner code versus messier code?\n\nAnswering it rigorously is harder than it looks. Real-world repos that differ on code quality also tend to differ on one hundred other things, as well—programming language, framework, test coverage, dependencies, age, team size. If an agent performed better on one repo than the other, we couldn't tell whether that was because the code was easier to work with, or simply because the agent knew the framework better.\n\nSo we created the comparison ourselves. We built six pairs of repositories where both sides ship the same application, pass the same test suite, use the same dependencies, and broadly share the same architecture. They only differed under the hood: how the code was factored, named, nested, and whether it carried the kinds of issues SonarQube flags. Same app, very different insides.\n\nWe intentionally built these pairs in two directions—some started from a clean codebase and got deliberately messed up by an agent pipeline (we called this one “Slopify”). Others started from an organically-grown messy codebase and got cleaned up by a SonarQube-guided agent (called “Vibeclean”). Running the comparison in both directions ensured that any downstream effect is due to the cleaner state of the code, and not due to our process of building the pairs.\n\nAcross these six pairs we wrote 27 coding tasks, routed through the parts of each codebase where the difference between clean and messy actually showed up. We described each task the way a product manager would describe a ticket: inputs, outputs, the behaviour a user should see. No file names, no function names, no internal hints—just enough for the agent to figure out where to go on its own.\n\nThen we ran each task ten times on both sides of every pair, using Claude Code with Sonnet 4.6, about 540 runs in total.\n\nWhat we found\n\nAcross the 540 runs, the cleaner side of every pair was measurably less expensive to run than the unclean side.\n\n7.2% fewer input tokens consumed\n\n8.5% fewer output tokens generated\n\n11.1% reduction in agent reasoning effort (note: this is an estimate as Anthropic doesn't expose reasoning-token counts directly, so we count characters off the event stream)\n\nAbout a third fewer file revisits after the agent had already edited a file\n\n3.6% fewer turns before the first code change, on average\n\nNo meaningful change in whether the task got done (−0.9 percentage points)\n\nThe pass-rate number sits at noise. Whatever cleaner code does for an agent, it doesn't decide whether the work finishes. What it changes is how much the agent has to do to finish it.\n\nOne caveat worth flagging up front: these are dataset-wide averages over a wide per-task spread. Some tasks saved 40% on input tokens; a handful actually cost slightly more on the high-quality side. Across the 27 tasks, the helping effect dominates on average, but not on every task. We'll come back to where it does and doesn't.\n\nThe bottom line\n\nEarly observations point to two critically important findings: \n\n(1) Agents' reasoning budget is impacted by messy code; and \n\n(2) Cleaner code is now an AI infrastructure cost lever, not just an engineering best practice. \n\nWe expect that variance across tasks is real, and not every task benefits equally from a cleaner codebase. However, if the pattern holds across more repos and more models, the headline numbers understate the structural-quality effect. \n\nThe same code that burdens a human reader burdens agents, too. In other words, a codebase that includes deeply nested logic, high cognitive complexity, cryptic naming, etc. will drive more labor and higher cost. When an agent encounters a 400-line function with branchy control flow, it has to work harder: more reading, more re-reading, more reasoning before it touches anything.\n\nWell-maintained code gives agents shorter paths to the same answer: smaller functions, cleaner control flow, and comments that hand context directly to the agent instead of forcing it to infer from structure. The agent doesn't have to build a full mental model before it acts.\n\nTwo of the more telling signals in the data aren’t about how much the agent reads or writes, rather, they’re about how it moves through the work. On the clean side the agent re-reads files it has already edited about 34% less often than on the unclean side, and reaches its first code edit slightly sooner. Both effects show up on every pair we measured, and unlike the input/output/reasoning numbers (which swing widely from task to task), these stay consistent across the dataset.\n\nOne plausible interpretation of this clean statistical signal could be that the agent commits and moves on when it encounters the cleaner code. On messier code, it goes back to re-read what it already touched, spends longer building a mental model before its first edit, and second-guesses itself more often. The token reductions look like a consequence of that behavioural difference, not the cause of it.\n\nHidden tokenomics beyond the \"prompt\"\n\nThe financial burden of AI is shining a light on \"agentic inference\" costs. As software developers move from single-turn prompts to multi-step agentic workflows, token consumption has soared, with platforms like OpenRouter processing over 100 trillion tokens annually.\n\nA single coding agent task on a frontier model now averages three to four million tokens, accumulated across tool calls, file reads, edits, retries, and reasoning steps in one conversation (Bai et al., 2026, our work). Most of those tokens are not spent generating code. They go into reading it, reviewing it, and re-reading it (Bai et al., 2026; Salim et al., 2026). What drives the cost isn’t how much the agent writes, it’s how much code the agent has to look at in order to write it.Anything that reduces this work—smaller files, predictably named code, clearer control flows, comments documenting upstream/downstream consequences of a method—can lead to lower overall costs.\n\nEfficiency gains with cleaner code\n\nOur first results therefore point to a simple takeaway: cleaner code low","source","sonarsource.com","objectID","blogpost___automatically_fix_code_backlog_with_son-0","_snippetResult",{"_40":12748},{"_41":12749,"_12750":12751},"Products SonarQube products Cloud-based static analysis tool for your …","matchLevel","none","_highlightResult",{"_12723":12754,"_40":12768},{"_36":12755,"_89":12758,"_7280":12760,"_7311":12762,"_132":12764,"_7211":12766},{"_41":12727,"_12750":12751,"_12756":12757},"matchedWords",[],{"_41":12728,"_12750":12751,"_12756":12759},[],{"_41":12729,"_12750":12751,"_12756":12761},[],{"_41":42,"_12750":12751,"_12756":12763},[],{"_41":7380,"_12750":12751,"_12756":12765},[],{"_41":12727,"_12750":12751,"_12756":12767},[],{"_41":12741,"_12750":12751,"_12756":12769},[],{"_187":12771,"_250":12772,"_12723":12773,"_12682":12781,"_237":12711,"_12740":12711,"_40":12783,"_12742":12743,"_12744":12784,"_12746":12785,"_12752":12787},"blogpost___import_your_gitlab_group_into_sonarqube","2026-05-13T18:24:03.4075272Z",{"_39":12725,"_394":12774,"_36":12775,"_89":12776,"_7280":12729,"_7311":42,"_132":7380,"_7211":12777,"_12730":7391,"_12731":12778,"_7401":7398,"_7336":7313,"_7326":12779,"_12735":12780},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/6a19d741-f901-4d8e-a14b-eec8494df7f4/sonarqube-remediation-agent-github-blog-landscape.webp","Automatically fix code backlog with SonarQube Remediation Agent","Learn how SonarQube Remediation Agent automatically fixes code backlog issues with scheduled scans and GitHub pull requests.","How to automatically fix your code backlog with the SonarQube Remediation Agent","2026-05-13T16:00:00Z",[415,2415],[],[12782,12738],"Category:SonarQube Cloud","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. TL;DR overview\n\nThe SonarQube Remediation Agent automates backlog remediation by scanning the main branch and opening GitHub pull requests with AI-generated fixes for high-priority security vulnerabilities and bugs.\n\nThis feature allows engineering teams to reduce technical debt on a scheduled daily or weekly cadence without manual developer intervention or dedicated sprints.\n\nThe agent opens one GitHub PR per run containing up to five eligible fixes, adhering to configurable open PR limits to prevent reviewer fatigue.\n\nProject-level settings allow teams to customize remediation frequency and PR limits, ensuring critical repositories receive prioritized attention based on team capacity.\n\nEvery engineering team is facing the same challenge: a constantly accumulating backlog of security vulnerabilities, bugs, and code quality issues on the main branch that teams rarely prioritize. While new features are shipped, these problems continue to accumulate.\n\nSonarQube Remediation Agent’s automated backlog remediation feature (currently in beta) offers a different approach. Instead of waiting for a software developer to manually pick up each issue, a scheduled agent scans your main branch, selects the highest-priority issues, and opens GitHub pull requests with AI-generated fixes—automatically, on a cadence you control.\n\nThis post walks you through how it works, how to enable it, and how to get the most out of it.\n\nWhat is automated backlog remediation?\n\nAutomated backlog remediation is a feature of the SonarQube Remediation Agent that runs on a schedule rather than on demand. Each time it triggers on an enabled project, it will:\n\nScan the issues on your main branch\n\nSelect up to five eligible issues to fix\n\nOpen one GitHub pull request containing the AI-generated fixes, authored by the SonarQube Remediation Agent\n\nYou configure how often it runs (daily is the default), set a cap on the number of open PRs the agent can have at any time, and let it work in the background while your team focuses on new development.\n\nPrerequisites to enabling automated backlog remediation\n\nBefore enabling the feature, make sure the following are in place:\n\nYour organization is on a Team (annual) or Enterprise plan on SonarQube Cloud.\n\nThe SonarQube Remediation Agent GitHub app is installed and bound to your organization.\n\nYour projects are connected to GitHub repositories.\n\nThe projects you want to remediate have issues on the main branch that are eligible for AI fixes.\n\nStep 1: Enable automated backlog remediation\n\nWith the GitHub app installed, scroll down to the Enable agent section on the same page.\n\nYou will see three toggles:\n\nPull request remediation triggers on failing quality gates in PRs\n\nManual backlog remediation lets developers assign individual issues to the agent\n\nAutomated backlog remediation is the scheduled feature covered in this post\n\nToggle automated backlog remediation on. Two configuration options will appear:\n\nSet your frequency\n\nSet how often the agent runs using the Frequency setting. Options include daily and weekly. You can also set the time of day and timezone, for example, daily at 09:00 Europe/London means the agent runs each morning before your team starts their review cycle.\n\nOpen PR limit\n\nThe open PR limit controls the maximum number of agent-created PRs that can be open simultaneously across your repositories. The default is three, while the maximum is 100.\n\nWhen this limit is reached, the agent pauses and does not open new PRs until existing ones are merged or closed. This prevents the agent from flooding your repository with unreviewed PRs.\n\nTip: Start with the default limit of three while your team gets familiar with reviewing agent PRs. You can increase it once you have a feel for the review cadence.\n\nThe limit set here is an org-level default. Individual projects can override it — covered in the next step.\n\nClick Save to activate the schedule.\n\nStep 2: Configure at the project level (optional)\n\nFor teams with projects that have different review capacity or priority, you can override the org-level settings at the project level.\n\nNavigate to your project in SonarQube Cloud, go to Project settings → AI capabilities, and adjust the frequency or PR limit for that specific project. Project-level settings take precedence over the org default.\n\nThis is useful when, for example, a critical security project should run daily while a lower-priority project only needs a weekly run.\n\nWhat the pull request looks like\n\nWhen the agent creates a PR, it:\n\nNames itself as the author (SonarQube Remediation Agent)\n\nIncludes a PR description with the list of Fixed issues, each with the rule name, description, and severity—the same information you would see in a SonarQube rule description\n\nDelivers each fix as a separate commit, so reviewers can evaluate changes individually\n\nThe PR is a standard GitHub pull request. Your existing branch protection rules, required reviewers, and CI pipelines apply to it just as they would to any human-authored PR.\n\nMonitoring with the agent activity page\n\nEvery agent run is logged on the Agent activity page, accessible from your project in SonarQube Cloud.\n\nFor each run, you can see:\n\nStatus and duration: Whether the run succeeded and how long it took\n\nStarted: The timestamp of when the agent was triggered\n\nSource: Confirms this was a backlog fix run against the main branch\n\nOutcome: A direct link to the GitHub PR the agent opened\n\nThis gives you a clear audit trail of what the agent has done, when it ran, and which issues it addressed, without needing to check GitHub separately.\n\nGet started with automated backlog remediation\n\nAutomated backlog remediation is a low-friction way to make steady progress on technical debt without scheduling dedicated sprints or pulling developers away from feature work. The setup takes a few minutes, the agent runs on a schedule you control, and every fix lands as a reviewable GitHub PR.\n\nTo get started, head to Administration → AI capabilities → Remediation agent in your SonarQube Cloud organization.\n\nFor the full documentation, visit the SonarQube Remediation Agent docs. Build trust into every line of code\n\nIntegrate SonarQube into your workflow and start finding vulnerabilities today.\n\n 4.6 / 5 Solutions Products Pricing Company Media Resources Knowledge © 2026 SonarSource Sàrl. All rights reserved. Follow SonarSource on Twitter https://twitter.com/sonarsource Follow SonarSource on Linkedin https://www.linkedin.com/company/sonarsource/","blogpost___import_your_gitlab_group_into_sonarqube-0",{"_40":12786},{"_41":12749,"_12750":12751},{"_12723":12788,"_40":12801},{"_36":12789,"_89":12791,"_7280":12793,"_7311":12795,"_132":12797,"_7211":12799},{"_41":12775,"_12750":12751,"_12756":12790},[],{"_41":12776,"_12750":12751,"_12756":12792},[],{"_41":12729,"_12750":12751,"_12756":12794},[],{"_41":42,"_12750":12751,"_12756":12796},[],{"_41":7380,"_12750":12751,"_12756":12798},[],{"_41":12777,"_12750":12751,"_12756":12800},[],{"_41":12783,"_12750":12751,"_12756":12802},[],{"_187":12804,"_250":12805,"_12723":12806,"_12682":12815,"_237":12711,"_12740":12711,"_40":12817,"_12742":12743,"_12744":12818,"_12746":12819,"_12752":12821},"blogpost___sonarqube_architecture_management_keeps_3d7ab9f","2026-05-08T13:17:38.5519666Z",{"_39":12725,"_394":12807,"_36":12808,"_89":12809,"_7280":12810,"_7311":42,"_132":7380,"_7211":12808,"_12730":7391,"_12731":12811,"_7401":7398,"_7336":7313,"_7326":12812,"_12735":12814},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/dcdb2486-5219-4582-91c1-c7a4c3bcefa9/bulk-import-of-gitlab-blog-landscape.webp","Import your entire GitLab group into SonarQube Cloud with one click","Bulk import GitLab projects into SonarQube Cloud and scale code quality and security coverage across your organization from day one.","Andrew Osborne","2026-05-08T16:00:00Z",[12734,12813,415],"Code security",[],[12739,12816,12782],"Category:Code security","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. TL;DR overview\n\nSonarQube Cloud now supports bulk import for GitLab projects, so teams using GitLab can connect a GitLab group and bring multiple projects into SonarQube Cloud in one go instead of setting them up one by one.\n\nFor teams managing large GitLab groups, this makes onboarding much faster and helps extend code quality, security, and reliability coverage across more of the codebase from day one.\n\nAdmins can connect a GitLab group, let SonarQube Cloud discover projects automatically, trigger the import, and review a consolidated summary showing which projects are ready and which need attention before finalizing.\n\nSonarQube Cloud now supports bulk import of GitLab projects. If your team works in GitLab, you can now connect your GitLab group to SonarQube Cloud and import multiple projects at once — selecting exactly which projects you want to analyze, without having to set each one up individually. For software engineering teams managing large GitLab groups, this means complete code quality and security coverage across your entire codebase from day one.\n\nGitLab onboarding made simple\n\nWith 1‑click bulk import for GitLab projects, onboarding becomes fast and easy. You can import all projects in a GitLab group and start enforcing your code quality, security, and reliability standards across your organization in a single move — without manual setup for each project.\n\nWhat bulk import for GitLab does for you\n\nFor GitLab admins, team leads, and developers, bulk import turns project onboarding from a one by one task into a batch operation. Here’s what you get out of the box:\n\nImport your entire catalog in one step – Bulk-import all existing projects within a GitLab group (including subgroups) in a single sweep, instead of configuring them one by one.\n\nTotal visibility from day one – Once triggered, SonarQube Cloud provisions the bound projects so your existing codebases are just as visible, governed, and compliant as your new ones.\n\nReview and import insights – A consolidated review page shows you which projects are ready to go and highlights anything that needs attention, so you can selectively trigger imports with confidence when you’re ready.\n\nThe result: GitLab administrators can enable coverage at scale, while teams keep working in their usual GitLab workflows.\n\nHow it works at a high level\n\nBulk import is designed to fit naturally into how you already manage GitLab:\n\nConnect your GitLab group (owner permission required) to SonarQube Cloud using the GitLab DevOps integration for your organization.\n\nChoose the GitLab group (and subgroups) you want to import. SonarQube Cloud automatically discovers the projects in that group. \n\nTrigger the import to provision all eligible projects in SonarQube Cloud and start analysis using your defined quality and security rules.\n\nReview the bulk import summary, including which projects are ready and which require attention (for example, missing permissions or empty repositories).\n\nFrom there, as part of your CI pipeline, SonarQube Cloud will deliver results after each build, so teams can fix problems early, and keep code reliable, maintainable, and secure.\n\nFor detailed, step-by-step setup instructions, follow the bulk import guide.\n\nTry bulk import for your GitLab group\n\nGet started today and turn GitLab project onboarding from a manual, one-at-a-time process into a batch operation — so your teams can spend time writing code, not configuring analysis projects. Build trust into every line of code\n\nIntegrate SonarQube into your workflow and start finding vulnerabilities today.\n\n 4.6 / 5 Solutions Products Pricing Company Media Resources Knowledge © 2026 SonarSource Sàrl. All rights reserved. Follow SonarSource on Twitter https://twitter.com/sonarsource Follow SonarSource on Linkedin https://www.linkedin.com/company/sonarsource/","blogpost___sonarqube_architecture_management_keeps_3d7ab9f-0",{"_40":12820},{"_41":12749,"_12750":12751},{"_12723":12822,"_40":12835},{"_36":12823,"_89":12825,"_7280":12827,"_7311":12829,"_132":12831,"_7211":12833},{"_41":12808,"_12750":12751,"_12756":12824},[],{"_41":12809,"_12750":12751,"_12756":12826},[],{"_41":12810,"_12750":12751,"_12756":12828},[],{"_41":42,"_12750":12751,"_12756":12830},[],{"_41":7380,"_12750":12751,"_12756":12832},[],{"_41":12808,"_12750":12751,"_12756":12834},[],{"_41":12817,"_12750":12751,"_12756":12836},[],{"_187":12838,"_250":12839,"_12723":12840,"_12682":12849,"_237":12711,"_12740":12711,"_40":12851,"_12742":12743,"_12744":12852,"_12746":12853,"_12752":12855},"blogpost___why_technical_debt_is_still_your_team_s","2026-05-08T02:14:27.1124704Z",{"_39":12725,"_394":12841,"_36":12842,"_89":12843,"_7280":12844,"_7311":42,"_132":7380,"_7211":12842,"_12730":7391,"_12731":12845,"_7401":7398,"_7336":7313,"_7326":12846,"_12735":12848},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/44be4e1e-ff86-44ae-a31c-4abcd67c255f/sqarch-mgmt-blog-index-2x.webp","SonarQube architecture management keeps agent-generated code architecturally sound","Learn how SonarQube architecture management helps AI generated code stay aligned with system design and prevents structural debt in modern development workflows.","Taylor Luttrell-Williams","2026-05-07T16:00:00Z",[2415,12734,12813,415,12847],"Code compliance",[],[12738,12739,12816,12782,12850],"Category:Code compliance","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. TL;DR overview\n\nArchitecture management in SonarQube Cloud identifies relationships and dependencies, illustrates project structure, and enables users to design interfaces.\n\nContext Augmentation extends the SonarQube MCP Server with additional tools that provide AI agents with the current and intended architectures before code generation.\n\nSonarQube Cloud reverse-engineers the current component hierarchy on every analysis, compares it against the intended architecture, and surfaces deviations as maintainability issues within existing workflows.\n\nProactive architectural awareness reduces architectural debt by giving AI agents visibility into the intended architecture's allow-list of permitted dependencies before code is generated.\n\nConsider the following scenario: an AI coding agent adds a feature to your project, thereby writing a service class, importing what it needs, and compiling the code without issue. Tests proceed to pass and the PR is merged, but one generated import reaches into a module that was never meant to be accessed from that layer. This raises an important concern: neither the compiler, nor the tests, nor your linter, nor code-level static analysis caught this issue, because none of these tools properly account for the project’s code-level architecture.\n\nAI coding agents often operate at the function and file level of a given project, and in doing so generate code that satisfies the immediate request: the functionality works, the unit test passes, the type checks resolve, etc. With this in mind, agents possess a limited view of the project’s broader system and how that code sits therein, and snippets that work in isolation but deviate from architectural boundaries accumulate as architectural debt that's expensive to untangle later.\n\nThe architectural blind spot\n\nCode-level static analysis can identify bugs, vulnerabilities, and code smells, but won’t catch a dependency that deviates from your intended architecture. This evokes an altogether different dimension of code health: form vs. function.\n\nWithout the necessary context, an agent won't consider questions such as: which modules are allowed to depend on which? Or, should one layer of the application reach directly into another, ignoring important abstractions? It may instead duplicate a utility function rather than locate the correct import path across a module boundary, or create a circular dependency if doing so resolves a compilation error. In such examples, the code may be locally correct every time but introduce crippling architectural debt. This differs from code-level issues in that it accumulates through decisions regarding where code lives and upon what it depends, not through how individual functions are implemented. The increasing use of AI coding agents compounds these effects and can accelerate architectural debt.\n\n\n\n\nIn the past, this erosion of a project’s architecture accumulated gradually, and by the time it became readily apparent, the architectural debt was already expensive to remediate. AI agents accelerate that timeline as the increased rate of code generation introduces further opportunities for architectural deviations that pass existing checks.\n\nHow SonarQube detects architectural drift\n\nArchitecture management in SonarQube Cloud addresses this challenge by operating on the following concepts:\n\nCurrent architecture is reverse-engineered from your source code at every analysis. It produces an interactive map which depicts the actual component hierarchy and dependencies: a live view of what exists today, always up to date, with no required configuration.\n\nIntended architecture is an allow-list wherein you specify which components you want to control, where they sit in the overall structure, authorize dependencies between them, and define their interface. The model is unambiguous by nature.\n\nDeviations constitute the gaps between your current and intended architectures. They surface as standard SonarQube maintainability issues that count toward the project’s maintainability rating and can even fail your quality gate, requiring remediation to pass. They flow through your existing workflow: dashboards, issue lists, PR decoration, etc. in the same way that other surfaced issues do. A single architectural deviation (e.g., module A should not depend on module B) can generate numerous code-level issues: one for each import statement, method call, or type reference that creates the forbidden dependency.\n\nFrom detection to prevention\n\nDetecting deviations after the code is generated is important, but Sonar Context Augmentation adds a proactive layer by providing the current architectural model and intended architecture to AI agents before they generate code.\n\nThrough the SonarQube MCP Server, an AI coding agent can, for example, query the following architecture endpoints:\n\nget_current_architecture returns the actual component hierarchy and relationships.\n\nget_intended_architecture returns the allow-list of permitted dependencies.\n\nWith both pieces of context, an agent can check whether a proposed dependency is permitted before writing it. Instead of generating an import that crosses a module boundary (and hoping that someone catches the deviation), the agent can recognize that the relationship isn't a part of the allow-list and therefore choose a different approach. The current architecture as context assists the agent by improving the accuracy of the code it generates. By providing the current and intended architectures to agents, they can produce code that is compliant with your architectural decisions.\n\nThe full list of currently available architecture tools can be found here.\n\nWhat to know\n\nContext Augmentation provides context, not enforcement. The agent receives the current architectural model and intended architecture, but SonarQube analysis remains the verification layer. If the agent ignores the context, deviations are still caught on the next scan.\n\nFull architecture MCP tool support (call flow tracing, type hierarchy, signature search) is currently Java-only. C#, JavaScript, Python, and TypeScript are currently limited to get_current_architecture and get_intended_architecture.\n\nContext Augmentation requires a SonarQube Cloud Team or Enterprise plan and is currently in open beta.\n\nPutting the pieces together\n\nDefine the intended architecture by creating the allow-list of permitted dependencies in the SonarQube Cloud UI and then:\n\nGuide AI agents with architectural context. Context Augmentation provides both the current and intended architectures to the agent through the MCP Server before code is generated.\n\nGenerate code with architectural awareness. The agent writes code that respects the architectural boundaries it received as context.\n\nVerify on eve","blogpost___why_technical_debt_is_still_your_team_s-0",{"_40":12854},{"_41":12749,"_12750":12751},{"_12723":12856,"_40":12869},{"_36":12857,"_89":12859,"_7280":12861,"_7311":12863,"_132":12865,"_7211":12867},{"_41":12842,"_12750":12751,"_12756":12858},[],{"_41":12843,"_12750":12751,"_12756":12860},[],{"_41":12844,"_12750":12751,"_12756":12862},[],{"_41":42,"_12750":12751,"_12756":12864},[],{"_41":7380,"_12750":12751,"_12756":12866},[],{"_41":12842,"_12750":12751,"_12756":12868},[],{"_41":12851,"_12750":12751,"_12756":12870},[],{"_187":12872,"_250":12873,"_12723":12874,"_12682":12884,"_237":12711,"_12740":12711,"_40":12886,"_12742":12743,"_12744":12887,"_12746":12888,"_12752":12890},"blogpost___sonarqube_architecture_management_keeps","2026-05-05T18:49:11.536139Z",{"_39":12725,"_394":12875,"_36":12876,"_89":12877,"_7280":12878,"_7311":42,"_132":7380,"_7211":12879,"_12730":7391,"_12731":12880,"_7401":7398,"_7336":7313,"_7326":12881,"_12735":12883},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/dc793c51-c578-4f36-b0fe-7c6639ac2c55/coverage-base-learn-page-landscape-2x.webp","How SonarQube Coverage Reporting Works?","Struggling with missing or incorrect coverage in SonarQube? Discover the four-stage coverage pipeline and the most common causes of 0% results and mismatched metrics.","Killian Carlsen-Phelan","How SonarQube code coverage reporting works","2026-05-04T16:00:00Z",[12734,12813,12847,12882],"Languages & frameworks",[],[12739,12816,12850,12885],"Category:Languages & frameworks","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. TL;DR overview\n\nSonarQube code coverage measures how much of the codebase is exercised by automated tests; it doesn’t itself generate coverage data. It imports reports produced by JaCoCo, coverage.py, Istanbul, or the equivalent tool for your language. The pipeline then runs through four stages, and most failures happen at the handcuffs between them.\n\n0% coverage almost always traces to one of seven causes: automatic analysis mode, missing report file, wrong format, wrong scanner property name (deprecated names fail silently), wrong path, scanner running before tests, or file paths inside the report not matching the project layout.\n\nWhen numbers don't match between tools, the cause is one of three things: different definitions of \"coverable line\" (Python def and import, JaCoCo closing braces), different file scope (your coverage tool only reports on files tests loaded; SonarQube sees every file), or SonarQube's combined line-plus-branch formula vs. tools that report them separately.\n\nCoverage percentage alone misses tests that exercise code without verifying it. SonarQube rules flag tests with no assertions (java:S2699), assertions trapped inside pytest.raises blocks where they never execute (python:S5915), and empty test classes (java:S2187).\n\nHere’s a common developer scenario: every test passes, but SonarQube reports 0.0% code coverage. Or coverage shows up, but the number is 20 points lower than what pytest or JaCoCo reported on the same code, and the scanner logs don't explain why.\n\nThe problem is almost never SonarQube itself. Coverage reporting is a four-stage pipeline, and most failures happen at the handoff points between your test framework, your coverage tool, the scanner, and the dashboard. Once you see the pipeline clearly, diagnosing a coverage failure takes minutes.\n\nThe coverage pipeline\n\nSonarQube does not generate code coverage data. It imports reports produced by third-party tools. The pipeline works like this:\n\nYour test framework (JUnit, pytest, Jest) runs your tests.\n\nA coverage tool (JaCoCo, coverage.py, Istanbul/c8) instruments your code and records which lines and branches executed during those tests.\n\nThe coverage tool writes a report file to disk in a specific format (JaCoCo XML, Cobertura XML, LCOV).\n\nThe sonar-scanner reads that report file via a configured analysis property and uploads the data to SonarQube.\n\nThe report file is the handoff artifact. It sits between your build tooling and SonarQube's scanner, and it's where most failures happen, such as wrong format, wrong path, or being absent entirely.\n\nIn practice, stages 1 and 2 are often a single command. JaCoCo hooks into Maven's test phase. Jest has Istanbul built in. go test -coverprofile combines both. The conceptual separation matters for troubleshooting because the test can pass while the coverage tool fails to produce a report, but you won't need to run two separate commands.\n\nOne constraint to know upfront is that coverage requires CI-based analysis, where you run sonar-scanner yourself. SonarQube Cloud's automatic analysis mode doesn't support it.\n\nEach programming language has its own coverage tool, report format, and scanner property:\n\nWhen coverage shows 0%\n\nThe pipeline has four transitions and a failure at any one of them produces the same symptom: 0% coverage on the dashboard. Work through these checks in order as most issues are caught by the first four.\n\nIs coverage supported in your analysis mode?\n\nAutomatic analysis doesn't import coverage reports. Check your project's Administration > Analysis Method in SonarQube Cloud. If it says \"Automatic,\" switch to CI-based analysis. No amount of property configuration will fix this.\n\nDoes the report file exist?\n\nBefore sonar-scanner runs, your build must produce a coverage report. After your test step completes, verify the file is where you expect it:\n\nIf the file doesn't exist after your build step, the problem is in your build configuration, not SonarQube.\n\nIs the report in the right format?\n\nEach programming language requires a specific format. Using the wrong one causes the scanner to silently ignore the report. You won’t get an error or a warning in normal output.\n\nJaCoCo must produce XML, not binary .exec files. The old sonar.jacoco.reportPaths property that accepted binary format is deprecated. Python's coverage.py must output Cobertura XML (coverage xml), not the .coverage binary or HTML report. JavaScript coverage must be LCOV, not JSON or Clover format.\n\nOpen the report file. XML starts with

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