Building a Smart Building HVAC Digital Twin with AI Copilot Using Foundry Local
Introduction Building operations teams face a constant challenge: optimizing HVAC systems for energy efficiency while maintaining occupant comfort and air quality. Traditional building management systems display raw sensor data, temperatures, pressures, CO₂ levels—but translating this into actionable insights requires deep HVAC expertise. What if operators could simply ask "Why is the third floor so warm?" and get an intelligent answer grounded in real building state? This article demonstrates building a sample smart building digital twin with an AI-powered operations copilot, implemented using DigitalTwin, React, Three.js, and Microsoft Foundry Local. You'll learn how to architect physics-based simulators that model thermal dynamics, implement 3D visualizations of building systems, integrate natural language AI control, and design fault injection systems for testing and training. Whether you're building IoT platforms for commercial real estate, designing energy management systems, or implementing predictive maintenance for building automation, this sample provides proven patterns for intelligent facility operations. Why Digital Twins Matter for Building Operations Physical buildings generate enormous operational data but lack intelligent interpretation layers. A 50,000 square foot office building might have 500+ sensors streaming metrics every minute, zone temperatures, humidity levels, equipment runtimes, energy consumption. Traditional BMS (Building Management Systems) visualize this data as charts and gauges, but operators must manually correlate patterns, diagnose issues, and predict failures. Digital twins solve this through physics-based simulation coupled with AI interpretation. Instead of just displaying current temperature readings, a digital twin models thermal dynamics, heat transfer rates, HVAC response characteristics, occupancy impacts. When conditions deviate from expectations, the twin compares observed versus predicted states, identifying root causes. Layer AI on top, and operators get natural language explanations: "The conference room is 3 degrees too warm because the VAV damper is stuck at 40% open, reducing airflow by 60%." This application focuses on HVAC, the largest building energy consumer, typically 40-50% of total usage. Optimizing HVAC by just 10% through better controls can save thousands of dollars monthly while improving occupant satisfaction. The digital twin enables "what-if" scenarios before making changes: "What happens to energy consumption and comfort if we raise the cooling setpoint by 2 degrees during peak demand response events?" Architecture: Three-Tier Digital Twin System The application implements a clean three-tier architecture separating visualization, simulation, and state management: The frontend uses React with Three.js for 3D visualization. Users see an interactive 3D model of the three-floor building with color-coded zones indicating temperature and CO₂ levels. Click any equipment, AHUs, VAVs, chillers, to see detailed telemetry. The control panel enables adjusting setpoints, running simulation steps, and activating demand response scenarios. Real-time charts display KPIs: energy consumption, comfort compliance, air quality levels. The backend Node.js/Express server orchestrates simulation and state management. It maintains the digital twin state as JSON, the single source of truth for all equipment, zones, and telemetry. REST API endpoints handle control requests, simulation steps, and AI copilot queries. WebSocket connections push real-time updates to the frontend for live monitoring. The HVAC simulator implements physics-based models: 1R1C thermal models for zones, affinity laws for fan power, chiller COP calculations, CO₂ mass balance equations. Foundry Local provides AI copilot capabilities. The backend uses foundry-local-sdk to query locally running models. Natural language queries ("How's the lobby temperature?") get answered with building state context. The copilot can explain anomalies, suggest optimizations, and even execute commands when explicitly requested. Implementing Physics-Based HVAC Simulation Accurate simulation requires modeling actual HVAC physics. The simulator implements several established building energy models: // backend/src/simulator/thermal-model.js class ZoneThermalModel { // 1R1C (one resistance, one capacitance) thermal model static calculateTemperatureChange(zone, delta_t_seconds) { const C_thermal = zone.volume * 1.2 * 1000; // Heat capacity (J/K) const R_thermal = zone.r_value * zone.envelope_area; // Thermal resistance // Internal heat gains (occupancy, equipment, lighting) const Q_internal = zone.occupancy * 100 + // 100W per person zone.equipment_load + zone.lighting_load; // Cooling/heating from HVAC const airflow_kg_s = zone.vav.airflow_cfm * 0.0004719; // CFM to kg/s const c_p_air = 1006; // Specific heat of air (J/kg·K) const Q_hvac = airflow_kg_s * c_p_air * (zone.vav.supply_temp - zone.temperature); // Envelope losses const Q_envelope = (zone.outdoor_temp - zone.temperature) / R_thermal; // Net energy balance const Q_net = Q_internal + Q_hvac + Q_envelope; // Temperature change: Q = C * dT/dt const dT = (Q_net / C_thermal) * delta_t_seconds; return zone.temperature + dT; } } This model captures essential thermal dynamics while remaining computationally fast enough for real-time simulation. It accounts for internal heat generation from occupants and equipment, HVAC cooling/heating contributions, and heat loss through the building envelope. The CO₂ model uses mass balance equations: class AirQualityModel { static calculateCO2Change(zone, delta_t_seconds) { // CO₂ generation from occupants const G_co2 = zone.occupancy * 0.0052; // L/s per person at rest // Outdoor air ventilation rate const V_oa = zone.vav.outdoor_air_cfm * 0.000471947; // CFM to m³/s // CO₂ concentration difference (indoor - outdoor) const delta_CO2 = zone.co2_ppm - 400; // Outdoor ~400ppm // Mass balance: dC/dt = (G - V*ΔC) / Volume const dCO2_dt = (G_co2 - V_oa * delta_CO2) / zone.volume; return zone.co2_ppm + (dCO2_dt * delta_t_seconds); } } These models execute every simulation step, updating the entire building state: async function simulateStep(twin, timestep_minutes) { const delta_t = timestep_minutes * 60; // Convert to seconds // Update each zone for (const zone of twin.zones) { zone.temperature = ZoneThermalModel.calculateTemperatureChange(zone, delta_t); zone.co2_ppm = AirQualityModel.calculateCO2Change(zone, delta_t); } // Update equipment based on zone demands for (const vav of twin.vavs) { updateVAVOperation(vav, twin.zones); } for (const ahu of twin.ahus) { updateAHUOperation(ahu, twin.vavs); } updateChillerOperation(twin.chiller, twin.ahus); updateBoilerOperation(twin.boiler, twin.ahus); // Calculate system KPIs twin.kpis = calculateSystemKPIs(twin); // Detect alerts twin.alerts = detectAnomalies(twin); // Persist updated state await saveTwinState(twin); return twin; } 3D Visualization with React and Three.js The frontend renders an interactive 3D building view that updates in real-time as conditions change. Using React Three Fiber simplifies Three.js integration with React's component model: // frontend/src/components/BuildingView3D.jsx import { Canvas } from '@react-three/fiber'; import { OrbitControls } from '@react-three/drei'; export function BuildingView3D({ twinState }) { return ( {/* Render building floors */} {twinState.zones.map(zone => ( selectZone(zone.id)} /> ))} {/* Render equipment */} {twinState.ahus.map(ahu => ( ))} ); } function ZoneMesh({ zone, onClick }) { const color = getTemperatureColor(zone.temperature, zone.setpoint); return ( ); } function getTemperatureColor(current, setpoint) { const deviation = current - setpoint; if (Math.abs(deviation) < 1) return '#00ff00'; // Green: comfortable if (Math.abs(deviation) < 3) return '#ffff00'; // Yellow: acceptable return '#ff0000'; // Red: uncomfortable } This visualization immediately shows building state at a glance, operators see "hot spots" in red, comfortable zones in green, and can click any area for detailed metrics. Integrating AI Copilot for Natural Language Control The AI copilot transforms building data into conversational insights. Instead of navigating multiple screens, operators simply ask questions: // backend/src/routes/copilot.js import { FoundryLocalClient } from 'foundry-local-sdk'; const foundry = new FoundryLocalClient({ endpoint: process.env.FOUNDRY_LOCAL_ENDPOINT }); router.post('/api/copilot/chat', async (req, res) => { const { message } = req.body; // Load current building state const twin = await loadTwinState(); // Build context for AI const context = buildBuildingContext(twin); const completion = await foundry.chat.completions.create({ model: 'phi-4', messages: [ { role: 'system', content: `You are an HVAC operations assistant for a 3-floor office building. Current Building State: ${context} Answer questions about equipment status, comfort conditions, and energy usage. Provide specific, actionable information based on the current data. Do not speculate beyond provided information.` }, { role: 'user', content: message } ], temperature: 0.3, max_tokens: 300 }); res.json({ response: completion.choices[0].message.content, model: 'phi-4', timestamp: new Date().toISOString() }); }); function buildBuildingContext(twin) { const alerts = twin.alerts.filter(a => a.active); const zoneStatus = twin.zones.map(z => `${z.name}: ${z.temperature.toFixed(1)}°F (setpoint: ${z.setpoint}°F), CO₂: ${z.co2_ppm}ppm, ${z.occupancy} people` ); return ` Zones: ${zoneStatus.join('\n')} Active Alerts: ${alerts.length} ${alerts.map(a => `- ${a.severity}: ${a.message}`).join('\n')} Energy: ${twin.kpis.total_power_kw.toFixed(1)} kW Chiller: ${twin.chiller.percent_load}% load, ${twin.chiller.cop.toFixed(2)} COP `.trim(); } Example interactions: User: "Why is the third floor conference room warm?" AI: "The Executive Suite on Floor 3 is currently 75.2°F, 3 degrees above the 72°F setpoint. This is due to high occupancy (8 people) and VAV-F3-EXEC operating at reduced airflow (285 CFM vs 450 CFM capacity). AHU-2 is supplying adequate cool air at 55°F. Recommend checking VAV damper position—it may be partially stuck." User: "What's our current energy usage?" AI: "Total building power consumption is 47.3 kW. Breakdown: Chiller (24.1 kW, 51%), AHU fans (12.8 kW, 27%), Pumps (7.2 kW, 15%), Other (3.2 kW, 7%). Chiller is operating at 62% load with a COP of 4.8, which is efficient for current conditions." User: "Activate demand response level 2" AI: "I cannot directly control building systems for safety reasons. To activate DR Level 2, use the Control Panel and click 'Demand Response' → 'Level 2'. This will raise cooling setpoints by 3°F and reduce auxiliary loads, targeting 15% energy reduction." The AI provides grounded, specific answers citing actual equipment IDs and metrics. It refuses to directly execute control commands, instead guiding operators to explicit control interfaces, a critical safety pattern for building systems. Fault Injection for Testing and Training Real building operations experience equipment failures, stuck dampers, sensor drift, communication losses. The digital twin includes comprehensive fault injection capabilities to train operators and test control logic: // backend/src/simulator/fault-injector.js const FAULT_CATALOG = { chillerFailure: { description: 'Chiller compressor failure', apply: (twin) => { twin.chiller.status = 'FAULT'; twin.chiller.cooling_output = 0; twin.alerts.push({ id: 'chiller-fault', severity: 'CRITICAL', message: 'Chiller compressor failure - no cooling available', equipment: 'CHILLER-01' }); } }, stuckVAVDamper: { description: 'VAV damper stuck at current position', apply: (twin, vavId) => { const vav = twin.vavs.find(v => v.id === vavId); vav.damper_stuck = true; vav.damper_position_fixed = vav.damper_position; twin.alerts.push({ id: `vav-stuck-${vavId}`, severity: 'HIGH', message: `VAV ${vavId} damper stuck at ${vav.damper_position}%`, equipment: vavId }); } }, sensorDrift: { description: 'Temperature sensor reading 5°F high', apply: (twin, zoneId) => { const zone = twin.zones.find(z => z.id === zoneId); zone.sensor_drift = 5.0; zone.temperature_measured = zone.temperature_actual + 5.0; } }, communicationLoss: { description: 'Equipment communication timeout', apply: (twin, equipmentId) => { const equipment = findEquipmentById(twin, equipmentId); equipment.comm_status = 'OFFLINE'; equipment.stale_data = true; twin.alerts.push({ id: `comm-loss-${equipmentId}`, severity: 'MEDIUM', message: `Lost communication with ${equipmentId}`, equipment: equipmentId }); } } }; router.post('/api/twin/fault', async (req, res) => { const { faultType, targetEquipment } = req.body; const twin = await loadTwinState(); const fault = FAULT_CATALOG[faultType]; if (!fault) { return res.status(400).json({ error: 'Unknown fault type' }); } fault.apply(twin, targetEquipment); await saveTwinState(twin); res.json({ message: `Applied fault: ${fault.description}`, affectedEquipment: targetEquipment, timestamp: new Date().toISOString() }); }); Operators can inject faults to practice diagnosis and response. Training scenarios might include: "The chiller just failed during a heat wave, how do you maintain comfort?" or "Multiple VAV dampers are stuck, which zones need immediate attention?" Key Takeaways and Production Deployment Building a physics-based digital twin with AI capabilities requires balancing simulation accuracy with computational performance, providing intuitive visualization while maintaining technical depth, and enabling AI assistance without compromising safety. Key architectural lessons: Physics models enable prediction: Comparing predicted vs observed behavior identifies anomalies that simple thresholds miss 3D visualization improves spatial understanding: Operators immediately see which floors or zones need attention AI copilots accelerate diagnosis: Natural language queries get answers in seconds vs. minutes of manual data examination Fault injection validates readiness: Testing failure scenarios prepares operators for real incidents JSON state enables integration: Simple file-based state makes connecting to real BMS systems straightforward For production deployment, connect the twin to actual building systems via BACnet, Modbus, or MQTT integrations. Replace simulated telemetry with real sensor streams. Calibrate model parameters against historical building performance. Implement continuous learning where the twin's predictions improve as it observes actual building behavior. The complete implementation with simulation engine, 3D visualization, AI copilot, and fault injection system is available at github.com/leestott/DigitalTwin. Clone the repository and run the startup scripts to explore the digital twin, no building hardware required. Resources and Further Reading Smart Building HVAC Digital Twin Repository - Complete source code and simulation engine Setup and Quick Start Guide - Installation instructions and usage examples Microsoft Foundry Local Documentation - AI integration reference HVAC Simulation Documentation - Physics model details and calibration Three.js Documentation - 3D visualization framework ASHRAE Standards - Building energy modeling standardsBeyond the Desktop: The Future of Development with Microsoft Dev Box and GitHub Codespaces
The modern developer platform has already moved past the desktop. We’re no longer defined by what’s installed on our laptops, instead we look at what tooling we can use to move from idea to production. An organisations developer platform strategy is no longer a nice to have, it sets the ceiling for what’s possible, an organisation can’t iterate it's way to developer nirvana if the foundation itself is brittle. A great developer platform shrinks TTFC (time to first commit), accelerates release velocity, and maybe most importantly, helps alleviate everyday frictions that lead to developer burnout. Very few platforms deliver everything an organization needs from a developer platform in one product. Modern development spans multiple dimensions, local tooling, cloud infrastructure, compliance, security, cross-platform builds, collaboration, and rapid onboarding. The options organizations face are then to either compromise on one or more of these areas or force developers into rigid environments that slow productivity and innovation. This is where Microsoft Dev Box and GitHub Codespaces come into play. On their own, each addresses critical parts of the modern developer platform: Microsoft Dev Box provides a full, managed cloud workstation. Dev Box gives developers a consistent, high-performance environment while letting central IT apply strict governance and control. Internally at Microsoft, we estimate that usage of Dev Box by our development teams delivers savings of 156 hours per year per developer purely on local environment setup and upkeep. We have also seen significant gains in other key SPACE metrics reducing context-switching friction and improving build/test cycles. Although the benefits of Dev Box are clear in the results demonstrated by our customers it is not without its challenges. The biggest challenge often faced by Dev Box customers is its lack of native Linux support. At the time of writing and for the foreseeable future Dev Box does not support native Linux developer workstations. While WSL2 provides partial parity, I know from my own engineering projects it still does not deliver the full experience. This is where GitHub Codespaces comes into this story. GitHub Codespaces delivers instant, Linux-native environments spun up directly from your repository. It’s lightweight, reproducible, and ephemeral ideal for rapid iteration, PR testing, and cross-platform development where you need Linux parity or containerized workflows. Unlike Dev Box, Codespaces can run fully in Linux, giving developers access to native tools, scripts, and runtimes without workarounds. It also removes much of the friction around onboarding: a new developer can open a repository and be coding in minutes, with the exact environment defined by the project’s devcontainer.json. That said, Codespaces isn’t a complete replacement for a full workstation. While it’s perfect for isolated project work or ephemeral testing, it doesn’t provide the persistent, policy-controlled environment that enterprise teams often require for heavier workloads or complex toolchains. Used together, they fill the gaps that neither can cover alone: Dev Box gives the enterprise-grade foundation, while Codespaces provides the agile, cross-platform sandbox. For organizations, this pairing sets a higher ceiling for developer productivity, delivering a truly hybrid, agile and well governed developer platform. Better Together: Dev Box and GitHub Codespaces in action Together, Microsoft Dev Box and GitHub Codespaces deliver a hybrid developer platform that combines consistency, speed, and flexibility. Teams can spin up full, policy-compliant Dev Box workstations preloaded with enterprise tooling, IDEs, and local testing infrastructure, while Codespaces provides ephemeral, Linux-native environments tailored to each project. One of my favourite use cases is having local testing setups like a Docker Swarm cluster, ready to go in either Dev Box or Codespaces. New developers can jump in and start running services or testing microservices immediately, without spending hours on environment setup. Anecdotally, my time to first commit and time to delivering “impact” has been significantly faster on projects where one or both technologies provide local development services out of the box. Switching between Dev Boxes and Codespaces is seamless every environment keeps its own libraries, extensions, and settings intact, so developers can jump between projects without reconfiguring or breaking dependencies. The result is a turnkey, ready-to-code experience that maximizes productivity, reduces friction, and lets teams focus entirely on building, testing, and shipping software. To showcase this value, I thought I would walk through an example scenario. In this scenario I want to simulate a typical modern developer workflow. Let's look at a day in the life of a developer on this hybrid platform building an IOT project using Python and React. Spin up a ready-to-go workstation (Dev Box) for Windows development and heavy builds. Launch a Linux-native Codespace for cross-platform services, ephemeral testing, and PR work. Run "local" testing like a Docker Swarm cluster, database, and message queue ready to go out-of-the-box. Switch seamlessly between environments without losing project-specific configurations, libraries, or extensions. 9:00 AM – Morning Kickoff on Dev Box I start my day on my Microsoft Dev Box, which gives me a fully-configured Windows environment with VS Code, design tools, and Azure integrations. I select my teams project, and the environment is pre-configured for me through the Dev Box catalogue. Fortunately for me, its already provisioned. I could always self service another one using the "New Dev Box" button if I wanted too. I'll connect through the browser but I could use the desktop app too if I wanted to. My Tasks are: Prototype a new dashboard widget for monitoring IoT device temperature. Use GUI-based tools to tweak the UI and preview changes live. Review my Visio Architecture. Join my morning stand up. Write documentation notes and plan API interactions for the backend. In a flash, I have access to my modern work tooling like Teams, I have this projects files already preloaded and all my peripherals are working without additional setup. Only down side was that I did seem to be the only person on my stand up this morning? Why Dev Box first: GUI-heavy tasks are fast and responsive. Dev Box’s environment allows me to use a full desktop. Great for early-stage design, planning, and visual work. Enterprise Apps are ready for me to use out of the box (P.S. It also supports my multi-monitor setup). I use my Dev Box to make a very complicated change to my IoT dashboard. Changing the title from "IoT Dashboard" to "Owain's IoT Dashboard". I preview this change in a browser live. (Time for a coffee after this hardwork). The rest of the dashboard isnt loading as my backend isnt running... yet. 10:30 AM – Switching to Linux Codespaces Once the UI is ready, I push the code to GitHub and spin up a Linux-native GitHub Codespace for backend development. Tasks: Implement FastAPI endpoints to support the new IoT feature. Run the service on my Codespace and debug any errors. Why Codespaces now: Linux-native tools ensure compatibility with the production server. Docker and containerized testing run natively, avoiding WSL translation overhead. The environment is fully reproducible across any device I log in from. 12:30 PM – Midday Testing & Sync I toggle between Dev Box and Codespaces to test and validate the integration. I do this in my Dev Box Edge browser viewing my codespace (I use my Codespace in a browser through this demo to highlight the difference in environments. In reality I would leverage the VSCode "Remote Explorer" extension and its GitHub Codespace integration to use my Codespace from within my own desktop VSCode but that is personal preference) and I use the same browser to view my frontend preview. I update the environment variable for my frontend that is running locally in my Dev Box and point it at the port running my API locally on my Codespace. In this case it was a web socket connection and HTTPS calls to port 8000. I can make this public by changing the port visibility in my Codespace. https://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/api/devices wss://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/ws This allows me to: Preview the frontend widget on Dev Box, connecting to the backend running in Codespaces. Make small frontend adjustments in Dev Box while monitoring backend logs in Codespaces. Commit changes to GitHub, keeping both environments in sync and leveraging my CI/CD for deployment to the next environment. We can see the Dev Box running local frontend and the Codespace running the API connected to each other, making requests and displaying the data in the frontend! Hybrid advantage: Dev Box handles GUI previews comfortably and allows me to live test frontend changes. Codespaces handles production-aligned backend testing and Linux-native tools. Dev Box allows me to view all of my files in one screen with potentially multiple Codespaces running in browser of VS Code Desktop. Due to all of those platform efficiencies I have completed my days goals within an hour or two and now I can spend the rest of my day learning about how to enable my developers to inner source using GitHub CoPilot and MCP (Shameless plug). The bottom line There are some additional considerations when architecting a developer platform for an enterprise such as private networking and security not covered in this post but these are implementation details to deliver the described developer experience. Architecting such a platform is a valuable investment to deliver the developer platform foundations we discussed at the top of the article. While in this demo I have quickly built I was working in a mono repository in real engineering teams it is likely (I hope) that an application is built of many different repositories. The great thing about Dev Box and Codespaces is that this wouldn’t slow down the rapid development I can achieve when using both. My Dev Box would be specific for the project or development team, pre loaded with all the tools I need and potentially some repos too! When I need too I can quickly switch over to Codespaces and work in a clean isolated environment and push my changes. In both cases any changes I want to deliver locally are pushed into GitHub (Or ADO), merged and my CI/CD ensures that my next step, potentially a staging environment or who knows perhaps *Whispering* straight into production is taken care of. Once I’m finished I delete my Codespace and potentially my Dev Box if I am done with the project, knowing I can self service either one of these anytime and be up and running again! Now is there overlap in terms of what can be developed in a Codespace vs what can be developed in Azure Dev Box? Of course, but as organisations prioritise developer experience to ensure release velocity while maintaining organisational standards and governance then providing developers a windows native and Linux native service both of which are primarily charged on the consumption of the compute* is a no brainer. There are also gaps that neither fill at the moment for example Microsoft Dev Box only provides windows compute while GitHub Codespaces only supports VS Code as your chosen IDE. It's not a question of which service do I choose for my developers, these two services are better together! *Changes have been announced to Dev Box pricing. A W365 license is already required today and dev boxes will continue to be managed through Azure. For more information please see: Microsoft Dev Box capabilities are coming to Windows 365 - Microsoft Dev Box | Microsoft Learn
Modernizing Spring Framework Applications with GitHub Copilot App Modernization
Upgrading Spring Framework applications from version 5 to the latest 6.x line (including 6.2+) enables improved performance, enhanced security, alignment with modern Java releases, and full Jakarta namespace compatibility. The transition often introduces breaking API changes, updated module requirements, and dependency shifts. GitHub Copilot app modernization streamlines this upgrade by analyzing your project, generating targeted changes, and guiding you through the migration. Supported Upgrade Path GitHub Copilot app modernization supports: Upgrading Spring Framework to 6.x, including 6.2+ Migrating from javax to jakarta Aligning transitive dependencies and version constraints Updating build plugins and configurations Identifying deprecated or removed APIs Validating dependency updates and surfacing CVE issues These capabilities align with the Microsoft Learn quickstart for upgrading Java projects with GitHub Copilot app modernization. Project Setup Open your Spring Framework project in Visual Studio Code or IntelliJ IDEA with GitHub Copilot app modernization enabled. The tool works with Maven or Gradle projects and evaluates your existing Spring Framework, Java version, imports, and build configurations. Project Analysis When you trigger the upgrade, GitHub Copilot app modernization: Detects the current Spring Framework version Flags javax imports requiring Jakarta migration Identifies incompatible modules, libraries, and plugins Validates JDK compatibility requirements for Spring Framework 6.x Reviews transitive dependencies impacted by the update This analysis provides the foundation for the upgrade plan generated next. Upgrade Plan Generation GitHub Copilot app modernization produces a structured plan including: Updated Spring Framework version (6.x / 6.2+) Replacements for deprecated or removed APIs jakarta namespace updates Updated build plugins and version constraints JDK configuration adjustments You can review the plan, modify version targets, and confirm actions before the tool applies them. Automated Transformations After approval, GitHub Copilot app modernization applies automated changes such as: Updating Spring Framework module coordinates Rewriting imports from javax.* to jakarta.* Updating libraries required for Spring Framework 6.x Adjusting plugin versions and build logic Recommending fixes for API changes These transformations rely on OpenRewrite‑based rules to modernize your codebase efficiently. Build Fix Iteration Once changes are applied, the tool compiles your project and automatically responds to failures: Captures compilation errors Suggests targeted fixes Rebuilds iteratively This loop continues until the project compiles with Spring Framework 6.x in place. Security & Behavior Checks GitHub Copilot app modernization performs validation steps after the upgrade: Checks for CVEs in updated dependencies Identifies potential behavior changes introduced during the transition Offers optional fixes to address issues This adds confidence before final verification. Expected Output After a Spring Framework 5 → 6.x upgrade, you can expect: Updated module coordinates for Spring Framework 6.x / 6.2 jakarta‑aligned imports across the codebase Updated dependency versions aligned with the new Spring ecosystem Updated plugins and build tool configurations Modernized test stack (JUnit 5) A summary file detailing versions updated, code edits applied, dependencies changed, and items requiring manual review Developer Responsibilities GitHub Copilot app modernization accelerates framework upgrade mechanics, but developers remain responsible for: Running full test suites Reviewing custom components, filters, and validation logic Revisiting security configurations and reactive vs. servlet designs Checking integration points and application semantics post‑migration The tool handles the mechanical modernization work so you can focus on correctness, runtime behavior, and quality assurance. Learn More For prerequisites, setup steps, and the complete Java upgrade workflow, refer to the Microsoft Learn guide: Upgrade a Java Project with Github Copilot App Modernization Install GitHub Copilot app modernization for VS Code and IntelliJ IDEAModernizing Spring Boot Applications with GitHub Copilot App Modernization
Upgrading Spring Boot applications from 2.x to the latest 3.x releases introduces significant changes across the framework, dependencies, and Jakarta namespace. These updates improve long-term support, performance, and compatibility with modern Java platforms, but the migration can surface breaking API changes and dependency mismatches. GitHub Copilot app modernization helps streamline this transition by analyzing your project, generating an upgrade plan, and applying targeted updates. Supported Upgrade Path GitHub Copilot app modernization supports upgrading Spring Boot applications to Spring Boot 3.5, including: Updating Spring Framework libraries to 6.x Migrating from javax to jakarta Aligning dependency versions with Boot 3.x Updating plugins and starter configurations Adjusting build files for the required JDK level Validating dependency updates and surfacing CVE issues These capabilities complement the Microsoft Learn quickstart for upgrading Java projects using GitHub Copilot app modernization. How GitHub Copilot app modernization helps When you open a Spring Boot 2.x project in Visual Studio Code or IntelliJ IDEA and initiate an upgrade, GitHub Copilot app modernization performs: Project Analysis Detects your current Spring Boot version Identifies incompatible starters, libraries, and plugins Flags javax.* imports requiring Jakarta migration Evaluates your build configuration and JDK requirements Upgrade Plan Generation The tool produces an actionable plan that outlines: New Spring Boot parent version Updated Spring Framework and related modules Required namespace changes from javax.* to jakarta.* Build plugin updates JDK configuration alignment for Boot 3 You can review and adjust the plan before applying changes. Automated Transformations GitHub Copilot app modernization applies targeted changes such as: Updating spring-boot-starter-parent to 3.5.x Migrating imports to jakarta.* Updating dependencies and BOM versions Rewriting removed or deprecated APIs Aligning test dependencies (e.g., JUnit 5) Build / Fix Iteration The agent automatically: Builds the project Captures failures Suggests fixes Applies updates Rebuilds until the project compiles successfully This loop continues until all actionable issues are addressed. Security & Behavior Checks As part of the upgrade, the tool can: Validate CVEs introduced by dependency version changes Surface potential behavior changes Recommend optional fixes Expected Output After running the upgrade for a Spring Boot 2.x project, you should expect: An updated Spring Boot parent in Maven or Gradle Spring Framework 6.x and Jakarta-aligned modules Updated starter dependencies and plugin versions Rewritten imports from javax.* to jakarta.* Updated testing stack A summary file detailing: Versions updated Code edits applied Dependencies changed CVE results Remaining manual review items Developer Responsibilities GitHub Copilot app modernization accelerates technical migration tasks, but final validation still requires developer review, including: Running the full test suite Reviewing custom filters, security configuration, and web components Re-validating integration points Confirming application behavior across runtime environments The tool handles mechanical upgrade work so you can focus on correctness, quality, and functional validation. Learn more For setup, prerequisites, and the broader Java upgrade workflow, refer to the official Microsoft Learn guide: Quickstart: Upgrade a Java Project with GitHub Copilot App Modernization Install GitHub Copilot app modernization for VS Code and IntelliJ IDEAUpgrade your Java JDK (8, 11, 17, 21, or 25) with GitHub Copilot App Modernization
Developers modernizing Java applications often need to upgrade the Java Development Kit (JDK), update frameworks, align dependencies, or migrate older stacks such as Java EE. GitHub Copilot app modernization dramatically speeds up this process by analyzing your project, identifying upgrade blockers, and generating targeted changes. This post highlights supported upgrade paths and what you can expect when using GitHub Copilot app modernization—optimized for search discoverability rather than deep tutorial content. For complete, authoritative guidance, refer to the official Microsoft Learn quickstart. Supported Upgrade Scenarios GitHub Copilot app modernization supports upgrading: Java Development Kit (JDK) to versions 8, 11, 17, 21, or 25 Spring Boot up to 3.5 Spring Framework up to 6.2+ Java EE → Jakarta EE (up to Jakarta EE 10) JUnit Third‑party dependencies to specified versions Ant → Maven build migrations For the full capabilities list, see the Microsoft Learn quickstart. Prerequisites (VS Code or IntelliJ) To use GitHub Copilot app modernization, you’ll need: GitHub account + GitHub Copilot Free Tier, Pro, Pro+, Business, or Enterprise Visual Studio Code Version 1.101+ GitHub Copilot extension GitHub Copilot app modernization extension Restart after installation IntelliJ IDEA Version 2023.3+ GitHub Copilot plugin 1.5.59+ Restart after installation Recommended: Auto‑approve MCP Tool Annotations under Tools > GitHub Project Requirements Java project using Maven or Gradle Git‑managed Maven access to public Maven Central (if Maven) Gradle wrapper version 5+ Kotlin DSL supported VS Code setting: “Tools enabled” set to true if controlled by your org Selecting a Java Project to Upgrade Open any Java project in: Visual Studio Code IntelliJ IDEA Optional sample projects: Maven: uportal‑messaging Gradle: docraptor‑java Once open, launch GitHub Copilot app modernization using Agent Mode. Running an Upgrade (Example: Java 8 → Java 21) Open GitHub Copilot Chat → Switch to Agent Mode → Run a prompt such as: Upgrade this project to Java 21 You’ll receive: Upgrade Plan JDK version updates Build file changes (Maven/Gradle) Dependency version adjustments Framework upgrade paths, if relevant Automated Transformations GitHub Copilot app modernization applies changes using OpenRewrite‑based transformations. Dynamic Build / Fix Loop The agent iterates: Build Detect failure Fix Retry Until the project builds successfully. Security & Behavior Checks Detects CVEs in upgraded dependencies Flags potential behavior changes Offers optional fixes Final Upgrade Summary Generated as a markdown file containing: Updated JDK level Dependencies changed Code edits made Any remaining CVEs or warnings What You Can Expect in a JDK Upgrade Typical outcomes from upgrading Java 8 → Java 21: Updated build configuration (maven.compiler.release → 21) Removal or replacement of deprecated JDK APIs Updated library versions for Java 21 compatibility Surface warnings for manual review Successfully building project with modern JDK settings GitHub Copilot app modernization accelerates these updates while still leaving space for developer review of runtime or architectural changes. Learn More For the complete, authoritative upgrade workflow—covering setup, capabilities, and the full end‑to‑end process—visit: ➡ Quickstart: Upgrade a Java project with GitHub Copilot app modernization (Microsoft Learn) Install GitHub Copilot app modernization for VS Code and IntelliJ IDEAMigrating Application Credentials to Azure Key Vault with GitHub Copilot App Modernization
Storing secrets directly in applications or configuration files increases operational risk. Migrating to Azure Key Vault centralizes secret management, supports rotation, and removes embedded credentials from application code. GitHub Copilot app modernization accelerates this process by identifying credential usage areas and generating changes for Key Vault integration. What This Migration Covers GitHub Copilot app modernization helps with: Detecting secrets hard‑coded in source files, config files, or environment variables. Recommending retrieval patterns using Azure Key Vault SDKs. Updating application code to load secrets from Key Vault. Preparing configuration updates to remove stored credentials. Surfacing dependency, version, and API adjustments required for Key Vault usage. Project Analysis Once the project is opened in Visual Studio Code or IntelliJ IDEA, GitHub Copilot app modernization analyzes: Hard‑coded credentials: passwords, tokens, client secrets, API keys. Legacy configuration patterns using .properties, .yaml, or environment variables. Azure SDK usage and required upgrades for Key Vault integration. Areas requiring secure retrieval or replacement with a managed identity. Migration Plan Generation The tool creates a step‑by‑step migration plan including: Introducing Key Vault client libraries. Mapping existing credential variables to Key Vault secrets. Updating configuration loading logic to retrieve secrets at runtime. Integrating Managed Identity authentication if applicable. Removing unused credential fields from code and configuration. Automated Transformations GitHub Copilot app modernization applies targeted changes: Rewrites code retrieving credentials from files or constants. Generates Key Vault retrieval patterns using SecretClient. Updates build dependencies to current Azure SDK versions. Removes unused configuration entries and environment variables. Build & Fix Iteration The project is rebuilt and validated: Fixes constructor changes related to updated clients. Resolves missing dependency versions. Corrects updated method signatures for Key Vault API calls. Rebuilds until no actionable errors remain. Security & Behavior Checks The tool surfaces: CVEs introduced by new or updated libraries. Behavior changes tied to lazy loading of secrets at runtime. Optional fixes or alternative patterns if Key Vault integration affects existing workflows. Expected Output After modernization: Credentials removed from source and config files. Application retrieves secrets from Azure Key Vault. Updated Azure SDK versions aligned with Key Vault. A summary file detailing code changes, dependency updates, and review items. Developer Responsibilities Developers should: Provision Key Vault resources and assign required access policies. Validate permissions through Managed Identity or service principals. Test application startup, error handling, and rotation scenarios. Review semantic impacts on components relying on early secret loading. Refer to the Microsoft Learn guide on upgrading Java projects with GitHub Copilot app modernization for foundational workflow details. Learn more Predefined tasks for GitHub Copilot app modernization Apply a predefined task Install GitHub Copilot app modernization for VS Code and IntelliJ IDEAModernizing Applications by Migrating Code to Use Managed Identity with Copilot App Modernization
Migrating application code to use Managed Identity removes hard‑coded secrets, reduces operational risk, and aligns with modern cloud security practices. Applications authenticate directly with Azure services without storing credentials. GitHub Copilot app modernization streamlines this transition by identifying credential usage patterns, updating code, and aligning dependencies for Managed Identity flows. Supported Migration Steps GitHub Copilot app modernization helps accelerate: Replacing credential‑based authentication with Managed Identity authentication. Updating SDK usage to token‑based flows. Refactoring helper classes that build credential objects. Surfacing libraries or APIs that require alternative authentication approaches. Preparing build configuration changes needed for managed identity integration. Migration Analysis Open the project in Visual Studio Code or IntelliJ IDEA. GitHub Copilot app modernization analyzes: Locations where secrets, usernames, passwords, or connection strings are referenced. Service clients using credential constructors or static credential factories. Environment‑variable‑based authentication workarounds. Dependencies and SDK versions required for Managed Identity authentication. The analysis outlines upgrade blockers and the required changes for cloud‑native authentication. Migration Plan Generation GitHub Copilot app modernization produces a migration plan containing: Replacement of hard‑coded credentials with Managed Identity authentication patterns. Version updates for Azure libraries aligning with Managed Identity support. Adjustments to application configuration to remove unnecessary secrets. Developers can review and adjust before applying. Automated Transformations GitHub Copilot app modernization applies changes: Rewrites code that initializes clients using username/password or connection strings. Introduces Managed Identity‑friendly constructors and token credential patterns. Updates imports, method signatures, and helper utilities. Cleans up configuration files referencing outdated credential flows. Build & Fix Iteration The tool rebuilds the project, identifies issues, and applies targeted fixes: Compilation errors from removed credential classes. Incorrect parameter types or constructors. Dependencies requiring updates for Managed Identity compatibility. Security & Behavior Checks GitHub Copilot app modernization validates: CVEs introduced through dependency updates. Behavior changes caused by new authentication flows. Optional fixes for dependency vulnerabilities. Expected Output A migrated codebase using Managed Identity: Updated authentication logic. Removed credential references. Updated SDKs and dependencies. A summary file listing code edits, dependency changes, and items requiring manual review. Developer Responsibilities Developers should: Validate identity access on Azure resources. Reconfigure role assignments for system‑assigned or user‑assigned managed identities. Test functional behavior across environments. Review integration points dependent on identity scopes and permissions. Learn full upgrade workflows in the Microsoft Learn guide for upgrading Java projects with GitHub Copilot app modernization. Learn more Predefined tasks for GitHub Copilot app modernization Apply a predefined task Install GitHub Copilot app modernization for VS Code and IntelliJ IDEAModernizing Java EE Applications to Jakarta EE with GitHub Copilot App Modernization
Migrating a Java EE application to Jakarta EE is now a required step as the ecosystem has fully transitioned to the new jakarta.* namespace. This migration affects servlet APIs, persistence, security, messaging, and frameworks built on top of the Jakarta specifications. The changes are mechanical but widespread, and manual migration is slow, error‑prone, and difficult to validate at scale. GitHub Copilot app modernization accelerates this process by analyzing the project, identifying required namespace and dependency updates, and guiding developers through targeted upgrade steps. Supported Upgrade Path GitHub Copilot app modernization supports: Migrating Java EE applications to Jakarta EE (up to Jakarta EE 10) Updating javax.* imports to jakarta.* Aligning dependencies and application servers with Jakarta EE 10 requirements Updating build plugins, BOMs, and libraries impacted by namespace migration Fixing compilation issues and surfacing API incompatibilities Detecting dependency CVEs after migration These capabilities complement the Microsoft Learn guide for upgrading Java projects with GitHub Copilot app modernization. Getting Started Open your Java EE project in Visual Studio Code or IntelliJ IDEA with GitHub Copilot app modernization enabled. Copilot evaluates the project structure, build files, frameworks in use, and introduces a migration workflow tailored to Jakarta EE. Project Analysis The migration begins with a full project scan: Identifies Java EE libraries (javax.*) Detects frameworks depending on older EE APIs (Servlet, JPA, JMS, Security) Flags incompatible versions of application servers and dependencies Determines JDK constraints for Jakarta EE 10 compatibility Analyzes build configuration (Maven/Gradle) and transitive dependencies This analysis forms the basis for the generated migration plan. Migration Plan Generation GitHub Copilot app modernization generates a clear, actionable plan outlining: Required namespace transitions from javax.* to jakarta.* Updated dependency coordinates aligned with Jakarta EE 10 Plugin version updates Adjustments to JDK settings if needed Additional changes for frameworks relying on legacy EE APIs You can review and adjust versions or library targets before applying changes. Automated Transformations After approving the plan, Copilot performs transformation steps: Rewrites imports from javax. to jakarta. Updates dependencies to Jakarta EE 10–compatible coordinates Applies required framework-level changes (JPA, Servlet, Bean Validation, JAX‑RS, CDI) Updates plugin versions aligned with Jakarta EE–based libraries Converts removed or relocated APIs with recommended replacements These transformations rely on OpenRewrite‑based rules surfaced through Copilot app modernization. Build Fix Iteration Copilot iterates through a build‑and‑fix cycle: Runs the build Captures compilation errors introduced by the migration Suggests targeted fixes Applies changes Rebuilds until the project compiles successfully This loop eliminates hours or days of manual mechanical migration work. Security & Behavior Checks After a successful build, Copilot performs additional validation: Flags CVEs introduced by updated or newly resolved dependencies Surfaces potential behavior changes from updated APIs Offers optional fixes for dependency vulnerabilities These checks ensure the migrated application is secure and stable before runtime verification. Expected Output A Jakarta EE migration with GitHub Copilot app modernization results in: Updated imports from javax.* to jakarta.* Dependencies aligned with Jakarta EE 10 Updated Maven or Gradle plugin and library versions Rewritten API usage where needed Updated tests and validation logic if required A summary file containing: Versions updated Code edits applied Dependency changes CVE results Items requiring manual developer review Developer Responsibilities While GitHub Copilot app modernization accelerates the mechanical upgrade, developers remain responsible for: Running the full application test suite Reviewing security, validation, and persistence behavior changes Updating application server configuration (if applicable) Re‑verifying integrations with messaging, REST endpoints, and persistence providers Confirming semantic correctness post‑migration Copilot focuses on the mechanical modernization tasks so developers can concentrate on validating runtime behavior and business logic. Learn More For setup prerequisites and full upgrade workflow details, refer to the Microsoft Learn guide for upgrading Java projects with GitHub Copilot app modernization. Quickstart: Upgrade a Java Project with GitHub Copilot App Modernization | Microsoft Learn
Unlocking Application Modernisation with GitHub Copilot
AI-driven modernisation is unlocking new opportunities you may not have even considered yet. It's also allowing organisations to re-evaluate previously discarded modernisation attempts that were considered too hard, complex or simply didn't have the skills or time to do. During Microsoft Build 2025, we were introduced to the concept of Agentic AI modernisation and this post from Ikenna Okeke does a great job of summarising the topic - Reimagining App Modernisation for the Era of AI | Microsoft Community Hub. This blog post however, explores the modernisation opportunities that you may not even have thought of yet, the business benefits, how to start preparing your organisation, empowering your teams, and identifying where GitHub Copilot can help. I’ve spent the last 8 months working with customers exploring usage of GitHub Copilot, and want to share what my team members and I have discovered in terms of new opportunities to modernise, transform your applications, bringing some fun back into those migrations! Let’s delve into how GitHub Copilot is helping teams update old systems, move processes to the cloud, and achieve results faster than ever before. Background: The Modernisation Challenge (Then vs Now) Modernising legacy software has always been hard. In the past, teams faced steep challenges: brittle codebases full of technical debt, outdated languages (think decades-old COBOL or VB6), sparse documentation, and original developers long gone. Integrating old systems with modern cloud services often requiring specialised skills that were in short supply – for example, check out this fantastic post from Arvi LiVigni (@arilivigni ) which talks about migrating from COBOL “the number of developers who can read and write COBOL isn’t what it used to be,” making those systems much harder to update". Common pain points included compatibility issues, data migrations, high costs, security vulnerabilities, and the constant risk that any change could break critical business functions. It’s no wonder many modernisation projects stalled or were “put off” due to their complexity and risk. So, what’s different now (circa 2025) compared to two years ago? In a word: Intelligent AI assistance. Tools like GitHub Copilot have emerged as AI pair programmers that dramatically lower the barriers to modernisation. Arvi’s post talks about how only a couple of years ago, developers had to comb through documentation and Stack Overflow for clues when deciphering old code or upgrading frameworks. Today, GitHub Copilot can act like an expert co-developer inside your IDE, ready to explain mysterious code, suggest updates, and even rewrite legacy code in modern languages. This means less time fighting old code and more time implementing improvements. As Arvi says “nine times out of 10 it gives me the right answer… That speed – and not having to break out of my flow – is really what’s so impactful.” In short, AI coding assistants have evolved from novel experiments to indispensable tools, reimagining how we approach software updates and cloud adoption. I’d also add from my own experience – the models we were using 12 months ago have already been superseded by far superior models with ability to ingest larger context and tackle even further complexity. It's easier to experiment, and fail, bringing more robust outcomes – with such speed to create those proof of concepts, experimentation and failing faster, this has also unlocked the ability to test out multiple hypothesis’ and get you to the most confident outcome in a much shorter space of time. Modernisation is easier now because AI reduces the heavy lifting. Instead of reading the 10,000-line legacy program alone, a developer can ask Copilot to explain what the code does or even propose a refactored version. Rather than manually researching how to replace an outdated library, they can get instant recommendations for modern equivalents. These advancements mean that tasks which once took weeks or months can now be done in days or hours – with more confidence and less drudgery - more fun! The following sections will dive into specific opportunities unlocked by GitHub Copilot across the modernisation journey which you may not even have thought of. Modernisation Opportunities Unlocked by Copilot Modernising an application isn’t just about updating code – it involves bringing everyone and everything up to speed with cloud-era practices. Below are several scenarios and how GitHub Copilot adds value, with the specific benefits highlighted: 1. AI-Assisted Legacy Code Refactoring and Upgrades Instant Code Comprehension: GitHub Copilot can explain complex legacy code in plain English, helping developers quickly understand decades-old logic without scouring scarce documentation. For example, you can highlight a cryptic COBOL or C++ function and ask Copilot to describe what it does – an invaluable first step before making any changes. This saves hours and reduces errors when starting a modernisation effort. Automated Refactoring Suggestions: The AI suggests modern replacements for outdated patterns and APIs, and can even translate code between languages. For instance, Copilot can help convert a COBOL program into JavaScript or C# by recognising equivalent constructs. It also uses transformation tools (like OpenRewrite for Java/.NET) to systematically apply code updates – e.g. replacing all legacy HTTP calls with a modern library in one sweep. Developers remain in control, but GitHub Copilot handles the tedious bulk edits. Bulk Code Upgrades with AI: GitHub Copilot’s App Modernisation capabilities can analyse an entire codebase and generate a detailed upgrade plan, then execute many of the code changes automatically. It can upgrade framework versions (say from .NET Framework 4.x to .NET 6, or Java 8 to Java 17) by applying known fix patterns and even fixing compilation errors after the upgrade. Teams can finally tackle those hundreds of thousand-line enterprise applications – a task that could take multiple years with GitHub Copilot handling the repetitive changes. Technical Debt Reduction: By cleaning up old code and enforcing modern best practices, GitHub Copilot helps chip away at years of technical debt. The modernised codebase is more maintainable and stable, which lowers the long-term risk hanging over critical business systems. Notably, the tool can even scan for known security vulnerabilities during refactoring as it updates your code. In short, each legacy component refreshed with GitHub Copilot comes out safer and easier to work on, instead of remaining a brittle black box. 2. Accelerating Cloud Migration and Azure Modernisation Guided Azure Migration Planning: GitHub Copilot can assess a legacy application’s cloud readiness and recommend target Azure services for each component. For instance, it might suggest migrating an on-premises database to Azure SQL, moving file storage to Azure Blob Storage, and converting background jobs to Azure Functions. This provides a clear blueprint to confidently move an app from servers to Azure PaaS. One-Click Cloud Transformations: GitHub Copilot comes with predefined migration tasksthat automate the code changes required for cloud adoption. With one click, you can have the AI apply dozens of modifications across your codebase. For example: File storage: Replace local file read/writes with Azure Blob Storage SDK calls. Email/Comms: Swap out SMTP email code for Azure Communication Services or SendGrid. Identity: Migrate authentication from Windows AD to Azure AD (Entra ID) libraries. Configuration: Remove hard-coded configurations and use Azure App Configuration or Key Vault for secrets. GitHub Copilot performs these transformations consistently, following best practices (like using connection strings from Azure settings). After applying the changes, it even fixes any compile errors automatically, so you’re not left with broken builds. What used to require reading countless Azure migration guides is now handled in minutes. Automated Validation & Deployment: Modernisation doesn’t stop at code changes. GitHub Copilot can also generate unit tests to validate that the application still behaves correctly after the migration. It helps ensure that your modernised, cloud-ready app passes all its checks before going live. When you’re ready to deploy, GitHub Copilot can produce the necessary Infrastructure-as-Code templates (e.g. Azure Resource Manager Bicep files or Terraform configs) and even set up CI/CD pipeline scripts for you. In other words, the AI can configure the Azure environment and deployment process end-to-end. This dramatically reduces manual effort and error, getting your app to the cloud faster and with greater confidence. Integrations: GitHub Copilot also helps tackle larger migration scenarios that were previously considered too complex. For example, many enterprises want to retire expensive proprietary integration platforms like MuleSoft or Apigee and use Azure-native services instead, but rewriting hundreds of integration workflows was daunting. Now, GitHub Copilot can assist in translating those workflows: for instance, converting an Apigee API proxy into an Azure API Management policy, or a MuleSoft integration into an Azure Logic App. Multi-Cloud Migrations: if you plan to consolidate from other clouds into Azure, GitHub Copilot can suggest equivalent Azure services and SDK calls to replace AWS or GCP-specific code. These AI-assisted conversions significantly cut down the time needed to reimplement functionality on Azure. The business impact can be substantial. By lowering the effort of such migrations, GitHub Copilot makes it feasible to pursue opportunities that deliver big cost savings and simplification. 3. Boosting Developer Productivity and Quality Instant Unit Tests (TDD Made Easy): Writing tests for old code can be tedious, but GitHub Copilot can generate unit test cases on the fly. Developers can highlight an existing function and ask Copilot to create tests; it will produce meaningful test methods covering typical and edge scenarios. This makes it practical to apply test-driven development practices even to legacy systems – you can quickly build a safety net of tests before refactoring. By catching bugs early through these AI-generated tests, teams gain confidence to modernise code without breaking things. It essentially injects quality into the process from the start, which is crucial for successful modernisation. DevOps Automation: GitHub Copilot helps modernise your build and deployment process as well. It can draft CI/CD pipeline configurations, Dockerfiles, Kubernetes manifests, and other DevOps scripts by leveraging its knowledge of common patterns. For example, when setting up a GitHub Actions workflow to deploy your app, GitHub Copilot will autocomplete significant parts (like build steps, test runs, deployment jobs) based on the project structure. This not only saves time but also ensures best practices (proper caching, dependency installation, etc.) are followed by default. Microsoft even provides an extension where you can describe your Azure infrastructure needs in plain language and have GitHub Copilot generate the corresponding templates and pipeline YAML. By automating these pieces, teams can move to cloud-based, automated deployments much faster. Behaviour-Driven Development Support: Teams practicing BDD write human-readable scenarios (e.g. using Gherkin syntax) describing application behaviour. GitHub Copilot’s AI is adept at interpreting such descriptions and suggesting step definition code or test implementations to match. For instance, given a scenario “When a user with no items checks out, then an error message is shown,” GitHub Copilot can draft the code for that condition or the test steps required. This helps bridge the gap between non-technical specifications and actual code. It makes BDD more efficient and accessible, because even if team members aren’t strong coders, the AI can translate their intent into working code that developers can refine. Quality and Consistency: By using AI to handle boilerplate and repetitive tasks, developers can focus more on high-value improvements. GitHub Copilot’s suggestions are based on a vast corpus of code, which often means it surfaces well-structured, idiomatic patterns. Starting from these suggestions, developers are less likely to introduce errors or reinvent the wheel, which leads to more consistent code quality across the project. The AI also often reminds you of edge cases (for example, suggesting input validation or error handling code that might be missed), contributing to a more robust application. In practice, many teams find that adopting GitHub Copilot results in fewer bugs and quicker code reviews, as the code is cleaner on the first pass. It’s like having an extra set of eyes on every pull request, ensuring standards are met. Business Benefits of AI-Powered Modernisation Bringing together the technical advantages above, what’s the payoff for the business and stakeholders? Modernising with GitHub Copilot can yield multiple tangible and intangible benefits: Accelerated Time-to-Market: Modernisation projects that might have taken a year can potentially be completed in a few months, or an upgrade that took weeks can be done in days. This speed means you can deliver new features to customers sooner and respond faster to market changes. It also reduces downtime or disruption since migrations happen more swiftly. Cost Savings: By automating repetitive work and reducing the effort required from highly paid senior engineers, GitHub Copilot can trim development costs. Faster project completion also means lower overall project cost. Additionally, running modernised apps on cloud infrastructure (with updated code) often lowers operational costs due to more efficient resource usage and easier maintenance. There’s also an opportunity cost benefit: developers freed up by Copilot can work on other value-adding projects in parallel. Improved Quality & Reliability: GitHub Copilot’s contributions to testing, bug-fixing, and even security (like patching known vulnerabilities during upgrades) result in more robust applications. Modernised systems have fewer outages and security incidents than shaky legacy ones. Stakeholders will appreciate that with GitHub Copilot, modernisation doesn’t mean “trading one set of bugs for another” – instead, you can increase quality as you modernise (GitHub’s research noted higher code quality when using Copilot, as developers are less likely to introduce errors or skip tests). Business Agility: A modernised application (especially one refactored for cloud) is typically more scalable and adaptable. New integrations or features can be added much faster once the platform is up-to-date. GitHub Copilot helps clear the modernisation hurdle, after which the business can innovate on a solid, flexible foundation (for example, once a monolith is broken into microservices or moved to Azure PaaS, you can iterate on it much faster in the future). AI-assisted modernisation thus unlocks future opportunities (like easier expansion, integrations, AI features, etc.) that were impractical on the legacy stack. Employee Satisfaction and Innovation: Developer happiness is a subtle but important benefit. When tedious work is handled by AI, developers can spend more time on creative tasks – designing new features, improving user experience, exploring new technologies. This can foster a culture of innovation. Moreover, being seen as a company that leverages modern tools (like AI Copilot) helps attract and retain top tech talent. Teams that successfully modernise critical systems with Copilot will gain confidence to tackle other ambitious projects, creating a positive feedback loop of improvement. To sum up, GitHub Copilot acts as a force-multiplier for application modernisation. It enables organisations to do more with less: convert legacy “boat anchors” into modern, cloud-enabled assets rapidly, while improving quality and developer morale. This aligns IT goals with business goals – faster delivery, greater efficiency, and readiness for the future. Call to Action: Embrace the Future of Modernisation GitHub Copilot has proven to be a catalyst for transforming how we approach legacy systems and cloud adoption. If you’re excited about the possibilities, here are next steps and what to watch for: Start Experimenting: If you haven’t already, try GitHub Copilot on a sample of your code. Use Copilot or Copilot Chat to explain a piece of old code or generate a unit test. Seeing it in action on your own project can build confidence and spark ideas for where to apply it. Identify a Pilot Project: Look at your application portfolio for a candidate that’s ripe for modernisation – maybe a small legacy service that could be moved to Azure, or a module that needs a refactor. Use GitHub Copilot to assess and estimate the effort. Often, you’ll find tasks once deemed “too hard” might now be feasible. Early successes will help win support for larger initiatives. Stay Tuned for Our Upcoming Blog Series: This post is just the beginning. In forthcoming posts, we’ll dive deeper into: Setting Up Your Organisation for Copilot Adoption: Practical tips on preparing your enterprise environment – from licensing and security considerations to training programs. We’ll discuss best practices (like running internal awareness campaigns, defining success metrics, and creating Copilot champions in your teams) to ensure a smooth rollout. Empowering Your Colleagues: How to foster a culture that embraces AI assistance. This includes enabling continuous learning, sharing prompt techniques and knowledge bases, and addressing any scepticism. We’ll cover strategies to support developers in using Copilot effectively, so that everyone from new hires to veteran engineers can amplify their productivity. Identifying High-Impact Modernisation Areas: Guidance on spotting where GitHub Copilot can add the most value. We’ll look at different domains – code, cloud, tests, data – and how to evaluate opportunities (for example, using telemetry or feedback to find repetitive tasks suited for AI, or legacy components with high ROI if modernised). Engage and Share: As you start leveraging Copilot for modernisation, share your experiences and results. Success stories (even small wins like “GitHub Copilot helped reduce our code review times” or “we migrated a component to Azure in 1 sprint”) can build momentum within your organisation and the broader community. We invite you to discuss and ask questions in the comments or in our tech community forums. Take a look at the new App Modernisation Guidance—a comprehensive, step-by-step playbook designed to help organisations: Understand what to modernise and why Migrate and rebuild apps with AI-first design Continuously optimise with built-in governance and observability Modernisation is a journey, and AI is the new compass and Copilot to guide the way. By embracing tools like GitHub Copilot, you position your organisation to break through modernisation barriers that once seemed insurmountable. The result is not just updated software, but a more agile, cloud-ready business and a happier, more productive development team. Now is the time to take that step. Empower your team with Copilot, and unlock the full potential of your applications and your developers. Stay tuned for more insights in our next posts, and let’s modernise what’s possible together!
