Azure App Service Logging: How to Monitor Your Web Apps in Real-Time
As a developer, having visibility into the behavior of your applications is crucial to maintaining the reliability and performance of your software. Luckily, Azure App Service provides two powerful logging features to help you monitor your web apps in real-time: App Service Logs and Log Stream. In this blog post, we'll explore how to configure these features for both Windows and Linux Web Apps in Azure App Service.Collaborate Kafka and Azure Functions securely within closed network
Azure has offered options for Apache Kafka as PaaS - "Event Hubs for Kafka" or Kafka cluster on HDInsight. Event Hub option is more cloud managed than HDInsight one, so HDInsight option is user managed than Event Hub one. It's required to create "Producer" sending data to Kafka and "Consumer" retriving data from Kafka for both options. In this post, you can acquire knowledge how to develop scalable Kafka Producer by using Azure Functions.Disciplined Guardrail Development in enterprise application with GitHub Copilot
What Is Disciplined Guardrail-Based Development? In AI-assisted software development, approaches like Vibe Coding—which prioritize momentum and intuition—often fail to ensure code quality and maintainability. To address this, Disciplined Guardrail-Based Development introduces structured rules ("guardrails") that guide AI systems during coding and maintenance tasks, ensuring consistent quality and reliability. To get AI (LLMs) to generate appropriate code, developers must provide clear and specific instructions. Two key elements are essential: What to build – Clarifying requirements and breaking down tasks How to build it – Defining the application architecture The way these two elements are handled depends on the development methodology or process being used. Here are examples as follows. How to Set Up Disciplined Guardrails in GitHub Copilot To implement disciplined guardrail-based development with GitHub Copilot, two key configuration features are used: 1. Custom Instructions (.github/copilot-instructions.md): This file allows you to define persistent instructions that GitHub Copilot will always refer to when generating code. Purpose: Establish coding standards, architectural rules, naming conventions, and other quality guidelines. Best Practice: Instead of placing all instructions in a single file, split them into multiple modular files and reference them accordingly. This improves maintainability and clarity. Example Use: You might define rules like using camelCase for variables, enforcing error boundaries in React, or requiring TypeScript for all new code. https://docs.github.com/en/copilot/how-tos/configure-custom-instructions/add-repository-instructions 2. Chat Modes (.github/chatmodes/*.chatmode.md): These files define specialized chat modes tailored to specific tasks or workflows. Purpose: Customize Copilot’s behavior for different development contexts (e.g., debugging, writing tests, refactoring). Structure: Each .chatmode.md file includes metadata and instructions that guide Copilot’s responses in that mode. Example Use: A debug.chatmode.md might instruct Copilot to focus on identifying and resolving runtime errors, while a test.chatmode.md could prioritize generating unit tests with specific frameworks. https://code.visualstudio.com/docs/copilot/customization/custom-chat-modes The files to be created and their relationships are as follows. Next, there are introductions for the specific creation method. #1: Custom Instructions With custom instructions, you can define commands that are always provided to GitHub Copilot. The prepared files are always referenced during chat sessions and passed to the LLM (this can also be confirmed from the chat history). An important note is to split the content into several files and include links to those files within the .github/copilot-instructions.md file. Because it can become too long if everything is written in a single file. There are mainly two types of content that should be described in custom instructions: A: Development Process (≒ outcome + Creation Method) What documents or code will be created: requirements specification, design documents, task breakdown tables, implementation code, etc. In what order and by whom they will be created: for example, proceed in the order of requirements definition → design → task breakdown → coding. B: Application Architecture How will the outcome be defined in A be created? What technology stack and component structure will be used? A concrete example of copilot-instructions.md is shown below. # Development Rules ## Architecture - When performing design and coding tasks, always refer to the following architecture documents and strictly follow them as rules. ### Product Overview - Document the product overview in `.github/architecture/product.md` ### Technology Stack - Document the technologies used in `.github/architecture/techstack.md` ### Coding Standards - Document coding standards in `.github/architecture/codingrule.md` ### Project Structure - Document the project directory structure in `.github/architecture/structure.md` ### Glossary (Japanese-English) - Document the list of terms used in the project in `.github/architecture/dictionary.md` ## Development Flow - Follow a disciplined development flow and execute the following four stages in order (proceed to the next stage only after completing the current one): 1. Requirement Definition 2. Design 3. Task Breakdown 4. Coding ### 1. Requirement Definition - Document requirements in `docs/[subsystem_name]/[business_name]/requirement.md` - Use `requirement.chatmode.md` to define requirements - Focus on clarifying objectives, understanding the current situation, and setting success criteria - Once requirements are defined, obtain user confirmation before proceeding to the next stage ### 2. Design - Document design in `docs/[subsystem_name]/[business_name]/design.md` - Use `design.chatmode.md` to define the design - Define UI, module structure, and interface design - Once the design is complete, obtain user confirmation before proceeding to the next stage ### 3. Task Breakdown - Document tasks in `docs/[subsystem_name]/[business_name]/tasks.md` - Use `tasks.chatmode.md` to define tasks - Break down tasks into executable units and set priorities - Once task breakdown is complete, obtain user confirmation before proceeding to the next stage ### 4. Coding - Implement code under `src/[subsystem_name]/[business_name]/` - Perform coding task by task - Update progress in `docs/[subsystem_name]/[business_name]/tasks.md` - Report to the user upon completion of each task Note: The only file that is always sent to the LLM is `copilot-instructions.md`. Documents linked from there (such as `product.md` or `techstack.md`) are not guaranteed to be read by the LLM. That said, a reasonably capable LLM will usually review these files before proceeding with the work. If the LLM does not properly reference each file, you may explicitly add these architecture documents to the context. Another approach is to instruct the LLM to review these files in the **chat mode settings**, which will be described later. There are various “schools of thought” regarding application architecture, and it is still an ongoing challenge to determine exactly what should be defined and what documents should be created. The choice of architecture depends on factors such as the business context, development scale, and team structure, so it is difficult to prescribe a one-size-fits-all approach. That said, as a general guideline, it is desirable to summarize the following: Product Overview: Overview of the product, service, or business, including its overall characteristics Technology Stack: What technologies will be used to develop the application? Project Structure: How will folders and directories be organized during development? Module Structure: How will the application be divided into modules? Coding Rules: Rules for handling exceptions, naming conventions, and other coding practices Writing all of this from scratch can be challenging. A practical approach is to create template information with the help of Copilot and then refine it. Specifically, you can: Use tools like M365 Copilot Researcher to create content based on general principles Analyze a prototype application and have the architecture information summarized (using Ask mode or Edit mode, feed the solution files to a capable LLM for analysis) However, in most cases, the output cannot be used as-is. The structure may not be analyzed correctly (hallucinations may occur) Project-specific practices and rules may not be captured Use the generated content as a starting point, and then refine it to create architecture documentation tailored to your own project. When creating architecture documents for enterprise-scale application development, a useful approach is to distinguish between the foundational parts and the individual application parts. Discipline-based guardrail development is particularly effective when building multiple applications in a “cookie-cutter” style on top of a common foundation. A cler example of this is Data-Oriented Architecture (DOA). In DOA, individual business applications are built on top of a shared database that serves as the overall common foundation. In this case, the foundational parts (the database layer) should not be modified arbitrarily by individual developers. Instead, focus on how to standardize the development of the individual application parts (the blue-framed sections) while ensuring consistency. Architecture documentation should be organized with this distinction in mind, emphasizing the uniformity of application-level development built upon the stable foundation. #2 Chat Mode By default, GitHub Copilot provides three chat modes: Ask, Edit, and Agent. However, by creating files under .github/chatmodes/*.chatmode.md, you can customize the Agent mode to create chat modes tailored for specific tasks. Specifically, you can configure the following three aspects. Functionally, this allows you to perform a specific task without having to manually change the model or tools, or write detailed instructions each time: model: Specify the default LLM to use (Note: The user can still manually switch to another LLM if desired) tools: Restrict which tools can be used (Note: The user can still manually select other tools if desired) custom instructions: Provide custom instructions specific to this chat mode A concrete example of .github/chatmodes/*.chatmode.md is shown below. description: This mode is used for requirement definition tasks. model: Claude Sonnet 4 tools: ['changes', 'codebase', 'editFiles', 'fetch', 'findTestFiles', 'githubRepo', 'new', 'openSimpleBrowser', 'runCommands', 'search', 'searchResults', 'terminalLastCommand', 'terminalSelection', 'usages', 'vscodeAPI', 'mssql_connect', 'mssql_disconnect', 'mssql_list_servers', 'mssql_show_schema'] --- # Requirement Definition Mode In this mode, requirement definition tasks are performed. Specifically, the project requirements are clarified, and necessary functions and specifications are defined. Based on instructions or interviews with the user, document the requirements according to the format below. If any specifications are ambiguous or unclear, Copilot should ask the user questions to clarify them. ## File Storage Location Save the requirement definition file in the following location: - Save as `requirement.md` under the directory `docs/[subsystem_name]/[business_name]/` ## Requirement Definition Format While interviewing the user, document the following items in the Markdown file: - **Subsystem Name**: The name of the subsystem to which this business belongs - **Business Name**: The name of the business - **Overview**: A summary of the business - **Use Cases**: Clarify who uses this business, when/under what circumstances, and for what purpose, using the following structure: - **Who (Persona)**: User or system roles - **When/Under What Circumstances (Scenario)**: Timing when the business is executed - **Purpose (Goal)**: Objectives or expected outcomes of the business - **Importance**: The importance of the business (e.g., High, Medium, Low) - **Acceptance Criteria**: Conditions that must be satisfied for the requirement to be considered met - **Status**: Current state of the requirement (e.g., In Progress, Completed) ## After Completion - Once requirement definition is complete, obtain user confirmation and proceed to the next stage (Design). Tips for Creating Chat Modes Here are some tips for creating custom chat modes: Align with the development process: Create chat modes based on the workflow and the deliverables. Instruct the LLM to ask the user when unsure: Direct the LLM to request clarification from the user if any information is missing. Clarify what deliverables to create and where to save them: Make it explicit which outputs are expected and their storage locations. The second point is particularly important. Many AI (LLMs) tend to respond to user prompts in a sycophantic manner (known as sycophancy). As a result, they may fill in unspecified requirements or perform tasks that were not requested, often with the intention of being helpful. The key difference between Ask/Edit modes and Agent mode is that Agent mode allows the LLM to proactively ask questions and engage in dialogue with the user. However, unless the user explicitly includes a prompt such as “ask if you don’t know,” the AI rarely initiates questions on its own. By creating a custom chat mode and instructing the LLM to “ask the user when unsure,” you can fully leverage the benefits of Agent mode. About Tools You can easily check tool names from the list of available tools in the command palette. Alternatively, as shown in the diagram below, it can be convenient to open the custom chat mode file and specify the tool configuration. You can specify not only the MCP server functionality but also built-in tools and Copilot Extensions. Example of Actual Operation An example interaction when using this chat mode is as follows: The LLM behaves according to the custom instructions defined in the chat mode. When you answer questions from GHC, the LLM uses that information to reason and proceed with the task. However, the output is not guaranteed to be correct (hallucinations may occur) → A human should review the output and make any necessary corrections before committing. The basic approach to disciplined guardrail-based development has been covered above. In actual business application development, it is also helpful to understand the following two points: Referencing the database schema Integrated management of design documents and implementation code (Important) Reading the Database Schema In business application development, requirements definition and functional design are often based on the schema information of entities. There are two main ways to allow the system to read schema information: Dynamically read the schema from a development/test DB server using MCP or similar tools. Include a file containing schema information within the project and read from it. A development/test database can be prepared, and schema information can be read via the MCP server or Copilot Extensions. For SQL Server or Azure SQL Database, an MCP Server is available, but its setup can be cumbersome. Therefore, using Copilot Extensions is often easier and recommended. This approach is often seen online, but it is not recommended for the following reasons: Setting up MCP Server or Copilot Extensions can be cumbersome (installation, connection string management, etc.) It is time-consuming (the LLM needs schema information → reads the schema → writes code based on it) Connecting to a DB server via MCP or similar tools is useful for scenarios such as “querying a database in natural language” for non-engineers performing data analysis. However, if the goal is simply to obtain the schema information of entities needed for business application development, the method described below is much simpler. Storing Schema Information Within the Project Place a file containing the schema information inside the project. Any of the following formats is recommended. Write custom instructions so that development refers to this file: DDL (full CREATE DATABASE scripts) O/R mapper files (e.g., Entity Framework context files) Text files documenting schema information, etc. DDL files are difficult for humans to read, but AI (LLMs) can easily read and accurately understand them. In .NET + SQL development, it is recommended to include both the DDL and EF O/R mapper files. Additionally, if you include links to these files in your architecture documents and chat mode instructions, the LLM can generate code while understanding the schema with high accuracy. Integrated Management of Design Documents and Implementation Code Disciplined guardrail-based development with LLMs has made it practical to synchronize and manage design documents and implementation code together—something that was traditionally very difficult. In long-standing systems, it is common for old design documents to become largely useless. During maintenance, code changes are often prioritized. As a result, updating and maintaining design documents tends to be neglected, leading to a significant divergence between design documents and the actual code. For these reasons, the following have been considered best practices (though often not followed in reality): Limit requirements and external design documents to the minimum necessary. Do not create internal design documents; instead, document within the code itself. Always update design documents before making changes to the implementation code. When using LLMs, guardrail-based development makes it easier to enforce a “write the documentation first” workflow. Following the flow of defining specifications, updating the documents, and then writing code also helps the LLM generate appropriate code more reliably. Even if code is written first, LLM-assisted code analysis can significantly reduce the effort required to update the documentation afterward. However, the following points should be noted when doing this: Create and manage design documents as text files, not Word, Excel, or PowerPoint. Use text-based technologies like Mermaid for diagrams. Clearly define how design documents correspond to the code. The last point is especially important. It is crucial to align the structure of requirements and design documents with the structure of the implementation code. For example: Place design documents directly alongside the implementation code. Align folder structures, e.g., /doc and /src. Information about grouping methods and folder mapping should be explicitly included in the custom instructions. Conclusion of Disciplined Guardrail-Based Development with GHC Formalizing and Applying Guardrails Define the development flow and architecture documents in .github/copilot-instructions.md using split references. Prepare .github/chatmodes/* for each development phase, enforcing “ask the AI if anything is unclear.” Synchronization of Documents and Implementation Code Update docs first → use the diff as the basis for implementation (Doc-first). Keep docs in text format (Markdown/Mermaid). Fix folder correspondence between /docs and /src. Handling Schemas Store DDL/O-R mapper files (e.g., EF) in the repository and have the LLM reference them. Minimize dynamic DB connections, prioritizing speed, reproducibility, and security. This disciplined guardrail-based development technique is an AI-assisted approach that significantly improves the quality, maintainability, and team efficiency of enterprise business application development. Adapt it appropriately to each project to maximize productivity in application development.Spring AI 1.0 GA is Here - Build Java AI Apps End-to-End on Azure Today
Spring AI 1.0 is now generally available, and it is ready to help Java developers bring the power of AI into their Spring Boot applications. This release is the result of open collaboration and contributions across the Spring and Microsoft Azure engineering teams. Together, they have made it simple for Java developers to integrate LLMs, vector search, memory, and agentic workflows using the patterns they already know. Why This Matters for Java Developers? Spring AI 1.0, built and maintained by the Spring team at Broadcom with active contributions from Microsoft Azure, delivers an intuitive and powerful foundation for building intelligent apps. You can plug AI into existing Spring Boot apps with minimal friction, using starters and conventions familiar to every Spring developer. Whether you are building new intelligent features or exploring AI use cases, Spring AI and Azure have you covered. Azure - The Complete AI Stack for Java Developers Azure offers every essential component needed to build intelligent Java applications. At the core of this offering is Azure AI Foundry, which provides a unified platform for enterprise AI operations, model builders, and application development. With AI Foundry, Java developers do not need to train or fine-tune models. They can deploy foundation models themselves, interact with already deployed foundation models, or connect to models deployed by their AI engineers or data/ML engineers. Developers can connect to these deployed models, test prompt templates, inspect token usage and latency metrics, and embed model interactions directly into their Spring Boot applications. This platform combines production-grade infrastructure with user-friendly tools, helping developers operate AI-powered applications with confidence. Here is how each piece fits into your Spring development workflow: Model-as-a-Service – Use Azure OpenAI for hosted large language models or choose from models available in Azure AI model inference, including offerings from Meta, Mistral AI, and DeepSeek. These models can be accessed directly using Spring AI starters, allowing your app to summarize, answer, generate, or assist through a simple, declarative API. Vector Databases – Embeddings and vector similarity are essential for semantic search and RAG. Azure provides multiple options: Cosmos DB with vector search support, Azure AI Search for advanced indexing and reranking, PostgreSQL with pgvector for SQL-based access, or Redis for in-memory speed. Spring AI integrates with these options to retrieve relevant context dynamically. Relational Databases – Use familiar relational databases like Azure SQL Database, PostgreSQL, and MySQL for structured data and transactional workloads. These remain the backbone for business logic, customer data, and system state that complement AI-generated content. Chat Memory – Track conversations across multiple turns using memory stores. Cosmos DB and Redis can persist chat memory, enabling your Spring-based agents to retain history, manage context, and respond in a more personalized way. RAG Workflows – Retrieval-Augmented Generation allows models to respond using external knowledge. Spring AI provides out-of-the-box support for RAG using vector stores and document loaders, enabling Java developers to build grounded, trustworthy interactions with minimal boilerplate. Orchestration with MCP – Build intelligent agents that call functions, retrieve data, and reason across steps. With Model Context Protocol (MCP) support, Spring AI apps can integrate seamlessly with external toolchains and invoke capabilities across multiple services. The MCP Java SDK is available through Spring Boot starters, simplifying the creation of both MCP Servers and Clients. App, AI Agent or MCP Server Deployment – Run your Spring AI applications, AI agents, and MCP servers on your choice of Azure compute: Azure App Service for managed deployments, AKS for containerized microservices, Azure Container Apps for serverless scale-out, or Virtual Machines for full control. Spring Boot simplifies packaging and deployment across these environments. In addition to deploying applications built with Spring AI, you can deploy any MCP Server - regardless of the language stack - to Azure and interoperate with it using MCP Clients built with Spring AI. This ensures that Java developers can connect and collaborate with agents and services written by other teams using different technology stacks. Whether you are deploying an AI-powered web app, a backend intelligence service, or a full agentic workflow across services, Azure provides the flexibility, scalability, and operational reliability you need. Fundamentals for Enterprise-Grade AI Applications In production, AI features must align with enterprise requirements like security, reliability, and explainability. Here is how Spring AI and Azure together deliver on these needs: Security and Access Control – Ensure AI features respect role-based access policies. Use keyless or passwordless authentication for Spring Boot apps accessing Azure OpenAI, PostgreSQL + pgvector, Cosmos DB, and connections to Azure SQL, PostgreSQL, and MySQL – using managed identities and Entra ID. Integrate identity with Microsoft Entra ID and protect secrets using Azure Key Vault. Spring Security helps you enforce user-scoped interactions. Safety and Compliance – Use content filtering, prompt injection protection, and policy alignment to control model behavior. Azure OpenAI includes built-in safety filters. Combined with Spring AI’s prompt templates, you can enforce structured, policy-compliant interactions. Observability – Monitor model usage, token consumption, latency, and errors. Spring Boot Actuator and Micrometer provide metrics out-of-the-box. You can export these metrics to Azure Monitor and Application Insights for full-stack visibility. Structured Outputs – Use JSON or XML formats when integrating AI responses into downstream systems. Spring AI supports output parsing and schema validation, so generated content can drive actions within your application without post-processing. Reasoning and Explainability – Let applications show sources, highlight references, or explain decision flows. In domains like healthcare or finance, this transparency builds trust. Spring AI supports tool calling and multi-step workflows that help models reason and communicate clearly. Tools, Demos and Learning Paths As always, you can start at start.spring.io, the easiest place to start any Spring Boot project, to quickly generate your app project using Spring AI, Web and Azure starters like OpenAI, AI Search, Cosmos DB, PostgreSQL + pgvector, relational databases, and Redis. End-to-End Demo – watch a hands-on demo of building enterprise AI agents with Java, Spring, and MCP. Get Started Today! Resources: Azure QuickStart using Spring AI and Spring AI resources Azure QuickStart using Spring AI o Chat o Use Your Data o Completions Spring AI o Spring AI Project o Spring AI Documentation o Spring AI Examples Collaboration and Contributions This GA release of Spring AI reflects the combined engineering and open-source commitment of Broadcom and Microsoft. Special thanks to Adib Saikali, Mark Pollack, Christian Tzolov, Dariusz Jędrzejczyk, Josh Long, Dan Vega, DaShaun Carter, Asir Selvasingh, Theo van Kraay, Mark Heckler, Matt Gotteiner, Govind Kamtamneni, Jorge Balderas, Brendan Mitchell, and Karl Erickson for their leadership, code, documentation, guidance, and community focus. Get Started Today! Everything you need is ready. Build your next AI-powered Java app with Spring AI and Azure. Begin your journey here: aka.ms/spring-aiJump Start your Developer Skills with Visual Studio Code Coding Packs
Coding Packs are specialized installs of VS Code that are pre-configured for specific coding environments. They are designed to help students and educators ramp up on VS Code quicker by automating editor and environment configuration. Coding Packs are a single download that performs the following when installed. Learn how to use them to jump start your development! This article was written by Sana Ajani, Program Manager at Microsoft
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 Co-pilots) 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 co-pilot 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!Optimizing Docker Images for Java Applications on Azure Container Apps
Learn how to optimize Docker images for Java/Spring Boot applications using multi-stage builds, Spring Boot Layer Tools, and Class Data Sharing (CDS) on Azure Container Apps. This guide demonstrates how to reduce image size, improve image pull times, and accelerate startup performance, making your deployments more efficient and faster. Real-world metrics and practical Dockerfile examples are provided to illustrate the benefits of each optimization technique.Intelligent app on Azure Container Apps Landing Zone Accelerator
Are you looking for deploying your AI infused or intelligent app to Azure Container apps, look no further!! Check out the end-to-end guidance to fast-track your journey to production with intelligent applications, it's crucial to implement your solutions adhering to the most effective practices in security, monitoring, networking, and operational excellence.
