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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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","cta","creationDate","2026-05-07T16:00:00Z",[2416,12734,12735,415,12736],"Code quality","Code security","Code compliance","keywords",[],[12740,12741,12742,12743,12744],"Category:AI","Category:Code quality","Category:Code security","Category:SonarQube Cloud","Category:Code compliance","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 13 - 4pm CEST | 9am CDT 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. 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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",[12734,12735,12736,12786],"Languages & frameworks",[],[12741,12742,12744,12789],"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 13 - 4pm CEST | 9am CDT 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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