From Test Cases to Trust: Elevating Enterprise Quality with GitHub Copilot
The Traditional QA Bottleneck In complex enterprise systems, QA teams often face familiar challenges: Time‑consuming test case creation from evolving requirements Repetitive automation scripting and refactoring Heavy regression cycles under tight release timelines Limited bandwidth for deeper risk analysis and exploratory testing None of these issues are caused by lack of skill—they’re symptoms of scale and complexity. This is where GitHub Copilot entered our workflow—not as a “magic button,” but as a thinking partner. Where GitHub Copilot Actually Helped Used responsibly, Copilot added value in very specific QA scenarios: Faster Test Design from Requirements Transforming business or technical requirements into structured test scenarios is intellectually demanding but time-intensive. Copilot helped accelerate: Initial test scenario drafting Gherkin-style acceptance criteria Coverage identification for edge and negative cases The result wasn’t “auto-generated tests,” but faster starting points, reviewed and refined by humans. Accelerating Automation Without Losing Control Whether working with UI automation or API tests, a significant portion of effort goes into boilerplate code, assertions, and structuring. Copilot assisted with: Suggesting test skeletons Refactoring repetitive code Improving readability and consistency This freed engineers to focus on test intent, not syntax. Supporting Debugging and Maintenance Automation maintenance is often underestimated. Copilot helped: Identify potential fixes during test failures Suggest improvements during refactoring Reduce turnaround time during regression cycles Again, nothing was auto‑merged. Human review remained non‑negotiable. The Most Important Shift: QA Mindset The real impact of Copilot wasn’t just efficiency—it changed how QA engineers spent their time. Instead of: Writing repetitive scripts Manually expanding similar test cases The team could focus more on: Risk‑based testing Failure pattern analysis Cross‑team quality discussions Improving test strategy and coverage depth In short, AI didn’t remove QA effort—it redirected it to higher‑value work. Responsible AI Was Not Optional In an enterprise setup, responsible AI usage matters. Key principles we followed: No blind acceptance of AI suggestions Strict human validation of all test logic Awareness of data sensitivity and compliance boundaries Treating Copilot as an assistant, not an authority This balance ensured quality and trust were never compromised in the pursuit of speed. What This Means for QA Teams From this experience, one thing became clear: AI won’t replace QA engineers. But QA engineers who use AI effectively will redefine quality. GitHub Copilot helped shift QA from execution to enablement—from writing tests faster to thinking about quality better. For enterprise teams, this is a powerful evolution. Final Thoughts Quality engineering is no longer just about finding defects—it’s about enabling confidence in delivery. Tools like GitHub Copilot, when used responsibly, can become catalysts in that transformation. The future of QA isn’t manual vs automation. It’s human judgment amplified by AI assistance. And that’s where real quality lives. References Create and manage manual test cases - Azure Test Plans | Microsoft Learn What is Azure Test Plans? Manual, exploratory, and automated test tools. - Azure Test Plans | Microsoft Learn Create and manage test plans - Azure Test Plans | Microsoft LearnAI apps and agents: choosing your Marketplace offer type
Choosing your Marketplace offer type is one of the earliest—and most consequential—decisions you’ll make when preparing an AI app for Microsoft Marketplace. It’s also one of the hardest to change later. This post is the second in our Marketplace‑ready AI app series. Its goal is not to push you toward a specific option, but to help you understand how Marketplace offer types map to different AI delivery models—so you can make an informed decision before architecture, security, and publishing work begins. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. Why offer type is an important Marketplace decision Offer type is more than a packaging choice. It defines the operating model of your AI app on Marketplace: How customers acquire your solution Where the AI runtime executes Determining the right security and business boundaries for the AI solution and associated contextual data Who operates and updates the system How transactions and billing are handled Once an offer type is selected, it cannot be changed without creating a new offer. Teams that choose too quickly often discover later that the decision creates friction across architecture, security boundaries, or publishing requirements. Microsoft’s Publishing guide by offer type explains the structural differences between offer types and why this decision must be made up front. How Marketplace offer types map to AI delivery models AI apps differ from traditional software in a few critical ways: Contextual data may need to remain in a specific tenant or geography Agents may operate autonomously and continuously Control over infrastructure often determines trust and compliance How the solution is charged and monetized, including whether pricing is usage‑based, metered, or subscription‑driven (for example, billing per inference, per workflow execution, or as a flat monthly fee) The buyer’s technical capability, including the level of engineering expertise required to deploy and operate the solution (for example, SaaS is generally easier to consume, while container‑based and managed application offers often require stronger cloud engineering and DevOps skills) Marketplace offer types don’t describe features. They define responsibility boundaries—who controls the AI runtime, who owns the infrastructure, and where customer data is processed. At a high level, Marketplace supports four primary delivery models for AI solutions: SaaS Azure Managed Application Azure Container Virtual Machine Each represents a different balance between publisher control and customer control. The sections below explain what each model means in practice. Check out the interactive offer selection wizard in App Advisor for decision support. Below, we unpack each of the offer types. SaaS offers for AI apps SaaS is the most common model for AI apps and agents on Marketplace—and often the default starting point. With a SaaS offer, the AI service runs in the publisher’s Azure environment and is accessed by customers through a centralized endpoint. This model works well for: Multi‑tenant AI platforms and agents Continuous model and prompt updates Rapid experimentation and iteration Usage‑based or subscription billing Because the service is centrally hosted, publishers retain full control over deployment, updates, and operational behavior. For multi-tenant AI apps, this also means making early decisions about Microsoft Entra ID configuration—such as how customers are onboarded, whether access is granted through tenant-level consent or external identities, and how user identities, roles, and data are isolated across tenants to prevent cross-tenant access or data leakage. For official guidance, see the SaaS section of the Marketplace publishing guide and the AI agent overview, which describes SaaS‑based agent deployments. Plan a SaaS offer for Microsoft Marketplace. Azure Managed Applications for AI solutions In this model, the solution is deployed into the customer’s Azure subscription, not the publisher’s. There are two variants: Managed applications, where the publisher retains permissions to operate and update the deployed resources Solution templates, where the customer fully manages the deployment after installation This model is a strong fit when AI workloads must run inside customer‑controlled environments, such as: Regulated or sensitive data scenarios Customer‑owned networking and identity boundaries Infrastructure‑heavy AI solutions that can’t be centralized Willingness or need on part of the customer or IT team to tailor the app to the needs of the end customer specific environment Managed Applications sit between SaaS and fully customer‑run deployments. They offer more customer control than SaaS, while still allowing publishers to manage lifecycle aspects when appropriate. Marketplace guidance for Azure Applications is covered in the publishing guide. For more information, see the following links: Plan an Azure managed application for an Azure application offer. Azure Container offers for AI workloads Container offer AI workloads—typically on AKS—using container images supplied by the publisher. This model is best suited for scenarios that require: Strict data residency Air‑gapped or tightly controlled environments Customer‑managed Kubernetes infrastructure The publisher delivers the container artifacts, but deployment, scaling, and runtime operations occur in the customer’s environment. This shifts operational responsibility, risk and compute costs away from the publisher and toward the customer. Container offer requirements are covered in the Marketplace publishing guide. Plan a Microsoft Marketplace Container offer. Virtual Machine offers for AI solutions Virtual Machine offers still play a role, particularly for specialized or legacy AI solutions. VM offers package a pre‑configured AI environment that customers deploy into their Azure subscription. Compared to other models, they offer: Updates and scaling are more tightly scoped Iteration cycles tend to be longer The solution is more closely aligned with specific OS or hardware requirements They are most commonly used for: Legacy AI stacks Fixed‑function AI appliances Solutions with specialized hardware or driver dependencies VM publishing requirements are also documented in the Marketplace publishing guide. Plan a virtual machine offer for Microsoft Marketplace. Comparing offer types across AI‑specific decision dimensions Rather than asking “which offer type is best,” it’s more useful to ask what trade‑offs you’re making in an AI app delivery model comparison. Key lenses to consider include: Who operates the AI runtime day‑to‑day Where customer data and AI prompts inputs and outputs are processed and stored Example: When evaluating Saas vs managed apps for AI, check industry specific compliance requirements to evaluate whether the data has to remain in the customer’s tenant or it can be sent to the publisher’s tenant. How quickly models, prompts, and logic can evolve The balance between publisher control and customer control How Marketplace transactions and billing align with runtime behavior SaaS Container (AKS / ACI) Virtual Machine (VM) Azure Managed Application What it is Fully managed, externally hosted app integrated with Marketplace for billing and entitlement Containerized app deployed into customer-managed Azure container environments VM image deployed directly into the customer’s Azure subscription Azure native solution deployed into the customer’s subscription, managed by the publisher Control plane Publisher‑owned Customer owned Customer owned Customer owned (with publisher access) Operational model Centralized operations, updates, and scaling Customer operates infra; publisher provides containers Customer operates infra; publisher provides VM image Per customer deployment and lifecycle Good fit scenarios • Multi‑tenant AI apps serving many customers • Fast onboarding and trials • Frequent model or feature updates • Publisher has full runtime control • AI apps or agents built as microservices • Legacy or lift-and-shift AI workloads • Enterprise AI solutions requiring customer owned infrastructure Avoid when • Customers require deployment into their own subscription • Strict data residency mandates customer control • Offline or air‑gapped environments are required • Customers standardize on Kubernetes • Custom OS or driver dependencies • Tight integration with customer Azure resources Typical AI usage pattern Centralized inference and orchestration across tenants • Portability across environments is important • Specialized runtime requirements • Strong compliance and governance needs Different AI solutions land in different places across these dimensions. The right choice is the one that matches your operational reality—not just your product vision. Note: If your solution primarily delivers virtual machines or containerized workloads, use a Virtual Machine or Container offer instead of an Azure Managed Application. Supported sales models and pricing options by Marketplace offer type Marketplace offer types don’t just define how an AI app and agent is deployed — they also determine how it can be sold, transacted, and expanded through Microsoft Marketplace. Understanding the supported sales models early helps avoid misalignment between architecture, pricing, and go‑to‑market strategy. Supported sales models Offer type Transactable listing Public listing Private offers Resale enabled Multiparty private offers Azure IP Co‑sell eligible SaaS Yes Yes Yes Yes Yes Yes Container Yes Yes Yes No Yes Yes Virtual Machine Yes Yes Yes Yes Yes Yes Azure Managed Application Yes Yes Yes No Yes Yes What these sales models mean Transactable listing A Marketplace listing that allows customers to purchase the solution directly through Microsoft Marketplace, with billing handled through Microsoft. Public listing A listing that is discoverable by any customer browsing Microsoft Marketplace and available for self‑service acquisition. Private offers Customer‑specific offers created by the publisher with negotiated pricing, terms, or configurations, purchased through Marketplace. Resale enabled Using resale enabled offers, software companies can authorize their channel partners to sell their existing Marketplace offers on their behalf. After authorization, channel partners can independently create and sell private offers without direct involvement from the software company. Multiparty private offers Private offers that involve one or more Microsoft partners (such as resellers or system integrators) as part of the transaction. Azure IP Co‑sell eligible When all requirements are met this allows your offers to contribute toward customers' Microsoft Azure Consumption Commitments (MACC). Pricing section Marketplace offer types determine the AI pricing models available. Make sure you build towards a marketplace offer type that aligns with how you want to deploy and price your solution. SaaS – Subscription or flat‑rate pricing, per‑user pricing, and usage‑based (metered) pricing. Container – Kubernetes‑based offers support multiple Marketplace‑transactable pricing models aligned to how the workload runs in the customer’s environment, including per core, per core in cluster, per node, per node in cluster, per pod, or per cluster pricing, all billed on a usage basis. Container offers can also support custom metered dimensions for application‑specific usage. Alternatively, publishers may offer Bring Your Own License (BYOL) plans, where customers deploy through Marketplace but bring an existing software license. Virtual Machine – Usage-based hourly pricing (flat rate, per vCPU, or per vCPU size), with optional 1-year or 3-year reservation discounts. Publishers may also offer Bring Your Own License (BYOL) plans, where customers bring an existing software license and are billed only for Azure infrastructure. Azure Managed Application – A monthly management or service fee charged by the publisher; Azure infrastructure consumption is billed separately to the customer. Note: Azure Managed Applications are designed to charge for management and operational services, not for SaaS‑style application usage or underlying infrastructure consumption. Buyer‑side assumptions to be aware of For customers to purchase AI apps and agents through these sales models: The customer must be able to purchase through Microsoft Marketplace using their existing Microsoft procurement setup Marketplace purchases align with enterprise buying and governance controls, rather than ad‑hoc vendor contracts For private and multiparty private offers, the customer must be willing to engage in a negotiated Marketplace transaction, rather than pure self‑service checkout Important clarification Supported sales models are consistent across Marketplace offer types. What varies by offer type is how the solution is provisioned, billed, operated, and updated. Sales flexibility alone should not drive offer‑type selection — it must align with the architecture and operating model of the AI app and agent. How this decision impacts everything that follows Offer type selection for AI apps and agents ripples through the rest of the Marketplace journey. They directly shape: Architecture design choices Security and compliance boundaries Fulfillment APIs and billing integration Publishing and certification requirements Cost, scalability, and operational responsibility Follow the series for updates on new posts. What’s next in the journey With the offer type decision in place, the focus shifts to turning that choice into a production‑ready solution. This includes designing an architecture that aligns with your delivery model, establishing clear security and compliance boundaries, and preparing the operational foundations required to run, update, and scale your AI app or agent confidently in customer environments. Getting these elements right early reduces rework and sets the stage for a smoother Marketplace journey. See the next post in the series: Designing Production‑Ready AI App and Agent Architectures for Microsoft Marketplace. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessSuccess with AI apps and agents on Marketplace: the end-to-end
Preparing an AI app or agent for Microsoft Marketplace requires solving a broader set of problems—ones that extend beyond the model and into architecture, security, compliance, operations, and commerce. These requirements often surface late, when teams are already moving toward launch. Teams often reach the same milestone: the AI works, the demo is compelling, and early customers are interested. But when it’s time to publish, transact, and operate that solution through Marketplace, gaps emerge—around security, compliance, reliability, operations, or commerce integration. Whether you are demo ready or starting with a great AI idea, this series is designed to address those challenges across the AI app publishing lifecycle through a connected, end‑to‑end journey. It brings together the decisions and requirements needed to build AI apps and agents that are not only functional, but Marketplace‑ready from day one. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents on Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. Why an end‑to‑end journey matters A working AI app does not automatically mean a Marketplace‑ready AI app. Marketplace readiness spans far more than model selection or prompt engineering. It requires a holistic approach across: Architecture and hosting design Security and AI guardrails Compliance and governance Operational maturity Commerce, billing, and lifecycle integration While guidance exists across each of these areas, it is often fragmented. This series connects those pieces into a single, reusable mental model that software companies can use to design, build, publish, and operate AI apps and agents—supporting end‑to‑end AI readiness with confidence. This first post frames the journey. Each subsequent post goes deep into one stage. The marketplace‑ready AI app and agent lifecycle At a high level, Marketplace‑ready AI apps and agents follow this lifecycle: Define how the AI app and agent will be delivered Identify industry compliance and regulatory requirements Design a production‑ready AI architecture Embed security and AI guardrails into the design Validate compliance and governance Build and test an MVP with potential customers Build for quality, reliability, and scale Integrate with Marketplace commerce Prepare for publishing and go‑live Operate, monitor, and evolve safely Promoting your AI app and agent to close initial sales This lifecycle is intentionally introduced once, at a high level and serves as an AI app launch checklist, where decisions made earlywill shape everything that follows. Throughout the series, this lifecycle serves as a shared reference point. Step 1: Decide how your AI app and agent will be packaged and delivered The first decision is how the AI app and agent will be delivered through Marketplace. Offer types—such as SaaS, Azure Managed Applications, Containers, and Virtual Machines—are not just listing formats. They are delivery models that directly impact: Identity and authentication Billing and metering Deployment responsibilities Operational ownership Customer onboarding experience Supported sales models Choosing an offer type early helps avoid costly redesigns later. Step 2: Design a production‑ready AI architecture Marketplace AI apps and agents are expected to meet enterprise customer expectations for performance, reliability, and security. Architecture decisions must account for: Customer business, compliance, and security needs Offer‑specific hosting best practices For example, SaaS offers typically require: Tenant isolation Environment separation Strong identity boundaries Architecture must also support both AI behavior and Marketplace lifecycle events, such as provisioning, subscription changes, and entitlement checks. Step 3: Secure the AI app and agent and define guardrails Security cannot be treated as a certification checklist at the end of the process. AI introduces new risks beyond traditional applications, including expanded attack surfaces through prompts and inputs. Frameworks such as the OWASP GenAI Top 10 provide a useful lens for identifying these risks. Guardrails must be enforced: At design time through architecture and policy decisions At runtime through monitoring, enforcement, and response AI‑specific incident response must also factor in privacy regulations and customer trust. Step 4: Treat AI agents as compliance‑governed systems AI agents and their data are first‑class compliance subjects. This includes: Prompts and responses Contextual and training data Actions taken by the agent These artifacts must be auditable and governed inline, not retroactively. At the same time, publishers must balance compliance with intellectual property protection by enabling explainability and transparency without exposing proprietary logic. Step 5: Build for quality, reliability, and scale Marketplace buyers expect predictable behavior. AI apps and agents should formalize: Quality and evaluation frameworks Reliability and performance targets Scaling and cost optimization strategies Quality, reliability, and performance directly influence customer trust and satisfaction. Step 6: Integrate with Marketplace commerce and lifecycle APIs Marketplace is not “just a listing,” but a runtime contract that depends on AI commerce integration. For transactable offers that help you sell globally direct to customers or through channel and allow customers to count sales of your app against their cloud commitments, Marketplace becomes an operational contract. Subscription state, entitlements, billing, and metering are runtime responsibilities of the application. For SaaS offers, SaaS Fulfillment APIs define the source of truth for subscription lifecycle events. Integrate Marketplace lead flows with your CRM using the Marketplace lead connector for CRM Step 7: Prepare for publishing, certification, and go‑live Publishing requires more than code completion. Marketplace certification validates: Security posture Customer experience Operational readiness Using templates, checklists, and tooling such as Quick Start Development Toolkit, Marketplace Rewards resources, and App Advisor reduces friction and rework. Step 8: Operate and evolve safely after go‑live Launch is not the end of the journey. AI apps and agents evolve continuously, making: Safe deployment strategies CI/CD discipline Rollback and monitoring practices This is essential for protecting both customers and publishers. Operational maturity also includes maintaining Marketplace offer assets (store images) as the product evolves. Use this framework to help you build a production ready AI app and agent, well architected, secured, reliable, scalable and integrated with Microsoft Marketplace global commerce engine. Step 9: Promote your AI app and agent Becoming Marketplace‑ready does not end at publication. AI app and agent success also depends on how effectively the solution is discovered, evaluated, and trusted by customers within Microsoft Marketplace and the broader Microsoft ecosystem. Promotion in Microsoft Marketplace is tightly integrated with how customers discover and purchase solutions. AI apps and agents are surfaced through Marketplace search, categories, and in‑product experiences, and once your AI app or agent becomes Azure IP co-sell eligible - the purchase of your offer can count towards your customers' Microsoft Azure Consumption Commitments (MACC) motivating customers to buy your offer. This reduces buying friction and accelerates evaluation‑to‑purchase transitions. Top activities to grow your sales: Optimize your listing once you publish your app, by getting an agentic review of your published listing in seconds, based on Marketplace listing best practices and expert Microsoft editorial guidance. Promote your Marketplace offer and track your engagement following best practices. Manage and nurture leads from trials to purchase, and from purchase to higher level SKUs. Private offers, which allow publishers to create customer-specific or negotiated offers directly through Marketplace, including multiparty private offers involving Microsoft channel partners Sell through channel, use resale enabled offers to enable resellers and channel partners to sell your app to their customers, Co-sell motions, where eligible AI apps and agents are sold jointly with Microsoft sellers and count toward customer cloud consumption commitments Effective customer engagement depends on alignment between how the AI app and agent is positioned and how it is delivered. Clear descriptions, accurate architectural boundaries, and transparent operational expectations help customers move confidently from discovery to production adoption. For publishers, programs such as ISV Success provide guidance and tooling to help align technical readiness, Marketplace requirements, and go‑to‑market execution as AI apps and agents scale through Microsoft Marketplace. Sales don't happen by accident, it's essential you engage in promoting your marketing. When promotion is treated as a first‑class step in the lifecycle, it reinforces trust, accelerates evaluation, and increases the likelihood that an AI app and agent transitions from initial interest to sustained use. How to use this series This series is designed to be used in two ways: Read sequentially to understand the full Marketplace‑ready journey Use individual posts alongside Microsoft Learn content, App Advisor, and Quick Start resources for deeper implementation guidance This series provides a structured, end‑to‑end view of what it takes to move from a working AI app and agent to a solution that customers can trust, deploy, and buy through Marketplace. It is designed to complement hands‑on implementation guidance, including Microsoft Learn resources such as Publishing AI agents to the Microsoft marketplace, and to help software companies navigate Marketplace readiness with fewer surprises and less rework. The upcoming post is about choosing your marketplace offer type which defines the operating model of your AI app or agent on Marketplace and influences key architectural decisions for your app or agent. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor, Quick-Start Development Toolkit Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessMoving Beyond Prompts: A Practical Introduction to Spec-Driven Development
In the last year, many of us have started writing code differently. We describe what we want, let AI generate an answer, review it, tweak the prompt, and try again. This loop—prompt, retry, adjust—has quietly become part of our daily workflow. At first, it feels incredibly productive. But as the complexity of the task increases, something changes. The iteration cycle becomes longer, outputs become inconsistent, and the effort shifts from solving the problem to refining the prompt. This is where a subtle but important shift in approach can help: moving from prompt-driven development to spec-driven development. The Problem: Prompt → Retry → Guess Most AI-assisted workflows today look something like this: Write a prompt describing the task Review the generated output Adjust the prompt Repeat until it looks acceptable In practice, this often simplifies to: Prompt → Retry → Guess Figure: Prompt-driven vs spec-driven workflow comparison For simple tasks, this works well. But for anything involving multiple inputs, constraints, or edge cases, the process can become unpredictable. In my experience, the challenge is not the model—it is the lack of structure in how we describe the problem. A Shift in Thinking: From Prompts to Specifications Instead of asking AI to “figure it out,” spec-driven development introduces a simple idea: Define the problem clearly before asking for a solution. A specification (spec) is not a long document—it is a structured way of describing: Inputs Outputs Constraints Edge cases When this structure is provided upfront, the interaction changes significantly. Rather than iterating on vague prompts, you are guiding the system with a clear contract. What This Looks Like in Practice Let’s take a simple example: an order summary API (for example, a backend service hosted on Azure App Service). Without a Spec (Typical Prompt) “Write an API that returns order details for a user.” A model can generate something reasonable, but in practice, the responses often vary: Field names may be inconsistent Pagination may be missing Edge cases (no orders, large datasets) may not be handled Structure may change across iterations Example response (typical output): { "userId": 123, "orders": [ { "id": 1, "amount": 250 } ] } With a Spec (Structured Input) Now consider providing a simple specification: Specification: Input: userId page pageSize Output: userId orders[] orderId totalAmount orderDate pagination page pageSize totalRecords Constraints: Default pageSize = 10 Return empty list if no orders Handle large datasets efficiently Example response (based on the spec): { "userId": 123, "orders": [ { "orderId": 1, "totalAmount": 250, "orderDate": "2024-01-10" } ], "pagination": { "page": 1, "pageSize": 10, "totalRecords": 50 } } Why This Tends to Work The difference here is not just stylistic—it is structural. An unstructured prompt leaves room for interpretation. A spec reduces ambiguity by defining expectations explicitly. In practice, I have observed that providing structured inputs like this often leads to the following: More consistent field naming Better handling of edge cases Reduced need for repeated prompt refinement Rather than relying on trial-and-error, the interaction becomes more predictable and aligned with expectations. Applying This to Existing Code (Refactor Scenario) This approach becomes even more useful when applied to existing code. Instead of asking: “Fix the bug in the Auth controller” You can define expected behavior: Input validation rules Response formats Error handling Authorization behavior The task then becomes aligning the implementation with the defined spec. This shifts the interaction from guesswork to validation—comparing current behavior with intended behavior. Example Comparison (Auth Scenario) Without Spec (Typical Prompt) “Fix the login issue in Auth controller” Possible outcomes include: Partial validation added Inconsistent error responses No clear handling of repeated failed attempts With Spec (Defined Behavior) Spec defines: Validate username and password Return consistent error responses Lock account after 5 failed attempts Do not expose internal errors Resulting behavior: Input validation is consistently applied Error responses follow a defined structure Edge cases like account lockout are handled explicitly This mirrors the same pattern seen in the API example—moving from ambiguity to clearly defined behavior. A Practical Way to Start You do not need new tools or frameworks to try this. A simple workflow that has worked well in practice: Ask – Describe the problem (prompt, discussion, or notes) Write a spec – Define inputs, outputs, constraints Refine – Remove ambiguity Generate – Use the spec as input Validate – Compare output with the spec This adds a small upfront step, but it often reduces back-and-forth iterations later. The Practical Challenge One important point to note: Writing a good spec requires understanding the problem. Spec-driven development does not eliminate complexity—it surfaces it earlier. In many cases, the hardest part is not writing code, but clearly defining: What the system should do What it should not do How it should behave under edge conditions This is also why specs evolve over time. They do not need to be perfect upfront. They improve as your understanding improves. Where This Approach Helps From what I have seen, this approach is most useful in scenarios where the problem involves multiple inputs, defined contracts, or structured outputs such as APIs, schema-driven systems, or refactoring existing code where consistency matters. Where It May Not Be Necessary For simpler tasks such as small scripts, minor UI changes, or quick experiments, a detailed specification may not add much value. In those cases, a straightforward prompt is often sufficient. A Note on Tools Tools like GitHub Copilot, Azure AI Studio, and AI-assisted workflows in Visual Studio Code tend to be more effective when given clear, structured inputs. Spec-driven development is not tied to any specific tool. It is a way of thinking about how we interact with these systems more effectively. References https://github.com/features/copilot https://platform.openai.com/docs/guides/prompt-engineering https://github.com/github/spec-kit Amplifier - Modular AI Agent Framework - Amplifier Final Thoughts Many discussions around AI-assisted development focus on what tools can do. This approach focuses on something slightly different: How developers can structure problems more effectively before implementation. In my experience, moving from prompts to specs does not eliminate iteration, but it makes that iteration more predictable and purposeful.
SonarPilot - Bulk Managing SonarQube Issues
Stop Managing SonarQube Issues One by One, Introducing SonarPilot Introduction If you've worked on a large projects, you know the feeling. You open your SonarQube dashboard, and you're staring at 30,000+ open issues spread across many rules. You need to triage them before the next sprint. You need to assign a category of issues to the right developer. You need to mark 4000 findings as false positives. And the only tool you have is, the SonarQube web UI. Click. Scroll. Click. Filter. Click again. This is the reality for thousands of development teams. SonarQube is a fantastic static analysis platform, but its UI is designed for investigating individual issues, not for making mass decisions across hundreds or thousands of findings at once. We built SonarPilot to solve exactly this problem. It's a Node.js/TypeScript IP accelerator that bridges SonarQube and Microsoft Excel, turning what used to take hours of repetitive portal work into a fast, familiar, spreadsheet-driven workflow. And we've published it as an IP on the Microsoft Chrysalis Portal so the entire community can benefit. In this blog, we'll walk through the problem in detail, show you how SonarPilot works step-by-step, and share the scenarios where it can save your team significant time. The Problem: SonarQube at Scale SonarQube works brilliantly for catching issues during development. But once a project accumulates technical debt or when you onboard a legacy codebase, you're quickly faced with a management problem that the tool itself doesn't solve well. Here are the specific pain points we kept running into: No native bulk-action UI SonarQube does not provide a way to select all issues under a rule and change their status in one action. You can filter by rule, but resolving or marking issues requires individual interactions. No offline review or sharing There's no way to export the backlog, share it with a project manager or architect for review, or triage issues in a meeting without portal access. Missing rule-level aggregation Leadership needs answers like "how many OPEN BLOCKER issues do we have in C# code across all security rules?", not a paginated list of individual findings. Bulk assignment isn't supported Assigning a category of issues (e.g., all SQL Injection warnings) to a developer requires manual effort, as bulk assignment is limited to a maximum of 400 issues per operation. False-positive triaging is tedious Marking 3000 false positives one at a time is slow enough that many teams simply don't do it, leaving dashboards permanently cluttered with noise. Introducing SonarPilot SonarPilot is a command-line tool built with Node.js and TypeScript that operates in two primary modes and an optional automation layer: Mode What It Does Read Connects to SonarQube, fetches all issues grouped by rule, and exports them to a structured multi-tab Excel workbook Update Reads the triage decisions you made in Excel and pushes them back to SonarQube via its bulk-change API Auto Pipeline File watcher + Azure Service Bus integration for a fully automated, event-driven update cycle The core idea is simple: use Excel as the triage interface. Everyone already knows Excel. You don't need to train anyone on the SonarQube portal to make triage decisions. The workbook becomes the artefact, reviewable, shareable, and auditable and SonarPilot handles the API communication in both directions. How the Excel Workbook is Structured When you run Read mode, SonarPilot generates an Excel workbook with the following structure: Information Sheet -> Report metadata: project key, branch, date generated, language filters, total issue count. Rules Sheet -> One row per SonarQube rule with: Rule key and name Severity (BLOCKER, CRITICAL, MAJOR, MINOR, INFO) Language Issue count (formula-linked to the rule's individual sheet) Action column this is where you make your triage decision One Sheet Per Rule -> Each rule gets its own tab containing every individual issue under that rule, with full details: Issue key Description / message File path and line number Current status Current assignee This structure gives you a complete, rule-first view of your entire code quality backlog in a single file. The Action Column: Your Triage Vocabulary The action column on the Rules Sheet is what powers SonarPilot's bulk-update capability. When you're ready to make a decision about an entire rule's worth of issues, you type one of the following values: Action Value Effect in SonarQube Typical Use Case CONFIRM Sets status to Confirmed Acknowledging real issues that need fixing UNCONFIRM Resets status to Reopened Reversing a previous confirmation RESOLVE_FP Resolves as False Positive Suppressing findings that don't apply RESOLVE_WF Resolves as Won't Fix Accepted risk or out-of-scope findings RESOLVE_FIXED Resolves as Fixed Issues already remediated in code ASSIGN:username Assigns to specified user Routing issues to the responsible developer SET_SEVERITY:value Changes severity level Reclassifying issue priority SET_TYPE:value Changes type (BUG, VULNERABILITY, CODE_SMELL) Correcting miscategorised findings You type the action once in the Rules Sheet, run Update mode, and SonarPilot applies that action to every issue under that rule. What would have been 300 clicks becomes a single cell edit. Step-by-Step: The SonarPilot Workflow Step 1 -> Configure and Connect Install and configure SonarPilot with zero code changes required. All settings are driven by environment variables or CLI arguments: npm install # Set environment variables export SONAR_URL=https://your-sonarqube-server.com export SONAR_TOKEN=your-api-token export SONAR_PROJECT_KEY=your-project-key export SONAR_BRANCH=main Or pass them as CLI arguments: npx ts-node src/index.read.ts --sonarUrl https://your-server.com --sonarToken <token> --projectKey my-project --branch main Step 2 -> Export Issues to Excel (Read Mode) Run the Read command to pull the full issue backlog from SonarQube: npx ts-node src/index.read.ts SonarPilot will: Authenticate via your SonarQube API token Fetch all quality profiles and rules for the project Paginate through the full issue list (handling SonarQube's 10,000-issue API limit) Generate a structured Excel workbook with the Information sheet, Rules sheet, and one sheet per rule The output is a timestamped .xlsx file in the configured output directory. Step 3 -> Triage in Excel Open the Excel workbook in Microsoft Excel. Navigate to the Rules Sheet. For each rule you want to act on, type the appropriate action value in the action column: Want to mark all "unused import" findings as Won't Fix? Type RESOLVE_WF Want to confirm all SQL injection warnings and assign them? Type CONFIRM in one row, ASSIGN:john.doe in another Want to dismiss 200 false positives in a formatting rule? Type RESOLVE_FP Save the file. That's all the human input needed. Step 4 -> Push Changes Back (Update Mode) Run the Update command pointing to your modified workbook: npx ts-node src/index.write.ts SonarPilot reads the action column, maps each action to the corresponding SonarQube API call, and executes them as bulk operations. You'll see a summary of how many issues were updated per rule. Step 5 -> Verify and Archive Open your SonarQube dashboard, you'll see all the changes reflected immediately. Keep the Excel workbook as a permanent audit record of what was triaged, when, and by whom. The Automation Pipeline: Zero-Touch Updates For teams that want to go further, SonarPilot supports a fully automated pipeline using Azure Service Bus and a local file watcher: Service Bus Listener -> Listens on an Azure Service Bus queue for trigger messages (e.g., from a CI/CD pipeline completing a SonarQube scan) Auto-Export -> On receiving a message, automatically runs Read mode to generate the Excel workbook File Watcher -> Monitors the output directory using chokidar; when the workbook is saved after triage, it triggers Update mode automatically Service Bus Sender -> After the update completes, sends a completion message back to the queue for downstream consumers This turns the entire Export → Triage → Update cycle into an event-driven pipeline. Configure it once, and the only manual step is opening the Excel file and making your triage decisions. # Start the Service Bus listener npx ts-node src/index.servicebus.ts # Start the file watcher (in a separate terminal) npx ts-node src/index.filewatcher.ts Key Features at a Glance Feature Description Rule-Grouped Excel Export Issues organised by rule with one tab per rule, not a flat dump 8 Bulk Actions Confirm, Unconfirm, Resolve (FP/WF/Fixed), Assign, Set Severity, Set Type Multi-Language Support Works with any language SonarQube analyses, C#, Java, TypeScript, Python, etc. Pagination Handling Automatically pages through SonarQube's 10K issue API limit Event-Driven Pipeline Azure Service Bus + file watcher for hands-free automation Dependency Injection Built with tsyringe for clean, testable architecture Zero Portal Dependency All triage happens in Excel, no SonarQube UI training needed Real-World Use Cases Legacy Codebase Onboarding A project being migrated to a new platform carries 45,000+ historical SonarQube issues. Before the first sprint, the architect exports the workbook, marks 12,000 findings as false positives (legacy patterns from the old framework), and assigns the remaining 33,000 issues to developers by rule category, all done in a 10-minute Excel session. One update run applies everything. The dashboard is clean and actionable on day one. Security Hotspot Triage The security team needs to classify all BLOCKER and CRITICAL findings across a multi-language project (C#, TypeScript, Java). SonarPilot exports them grouped by rule across all three languages into a single workbook. The security reviewer works through the Rules sheet, marking known false positives and assigning real findings to the appropriate team. The entire triage is completed and pushed in under one hour, no portal access required for the reviewer. Sprint Quality Check Before every sprint review, a developer runs Read mode against the main branch and shares the workbook with the delivery lead. The lead sees at a glance which rules have grown in issue count, marks any quick wins for resolution, and the update run clears them before the sprint demo. Clean dashboards, visible quality progress, zero portal navigation. Why Excel? Why Not a Custom UI? The choice to use Excel as the triage interface was deliberate, and it comes down to one thing: zero adoption friction. Every developer, architect, project manager, and delivery lead already knows Excel. Building a custom web UI would have added weeks of development, required deployment infrastructure, and introduced a learning curve. Excel gives you: Sorting and filtering -> instantly see all BLOCKER rules, sort by issue count Formulas -> the issue count on the Rules sheet is formula-linked to each rule's sheet Shareability -> email the workbook to anyone, no portal access needed Auditability -> every decision is recorded in the file, which is then archived Who Should Use SonarPilot? Engineering leads who need to triage thousands of issues across multiple projects before a release gate Security teams reviewing vulnerability findings and marking false positives in bulk after a penetration test or onboarding a legacy codebase Quality architects who want a shareable, offline artefact for sprint planning meetings, not a live portal session DevOps engineers automating quality gates in CI/CD pipelines where SonarQube scan results need programmatic triage Migration teams onboarding existing projects to SonarQube where the initial scan produces thousands of pre-existing findings that need bulk disposition Getting Started SonarPilot is published as an IP on the Microsoft Chrysalis Portal and available on GitHub. Prerequisites: Node.js v18+ (v20 LTS recommended) A SonarQube instance (8.x or 9.x) with an API token Microsoft Excel (for viewing/editing the workbook) Optional: Azure Service Bus namespace (for Auto Pipeline mode) Quick Start: # Clone the repository git clone https://github.com/HiteshDutt/sonar-qube-desktop-client.git cd sonar-qube-desktop-client # Install dependencies npm install # Configure (edit src/config/appsettings.ts or use environment variables) export SONAR_URL=https://your-server.com export SONAR_TOKEN=your-token export SONAR_PROJECT_KEY=your-project # Run Read mode npx ts-node src/index.read.ts # Make triage decisions in the generated Excel file... # Run Update mode npx ts-node src/index.write.ts What's Next? We're actively developing SonarPilot and have several enhancements planned: Branch comparison mode -> Compare issues between two branches to see what's new vs. inherited Dashboard summary sheet -> Auto-generated charts in the Excel workbook for executive reporting SonarCloud support -> Extending compatibility to SonarCloud's API Web UI companion -> A lightweight web interface for teams that prefer browser-based triage Conclusion SonarQube is an indispensable tool for code quality. But managing its output at scale, triaging, assigning, resolving, and auditing thousands of issues, has always been a manual, tedious process. SonarPilot bridges that gap by turning Excel into a powerful triage interface and automating the two-way communication with the SonarQube API. If your team spends hours clicking through the SonarQube portal to manage issues in bulk, give SonarPilot a try. We built it to solve our own pain, and we hope it helps yours too. Got questions or feedback? Drop a comment below or reach out to us directly. We'd love to hear how you're using SonarPilot and what features would help your workflow.Access fixes released in Version 2603 (Build 19822.20114)
Here's a summary of bug fixes in the latest version of Access: Bug Name Issue Fixed Edge Browser Control didn't navigate from code when inside a tab control When the Edge Browser control was hosted in a tab control, calls to the Navigate method from VBA succeeded, but the control didn’t refresh to show the new page. Switching tabs forced the refresh. The control now refreshes automatically after navigation, even when it’s hosted in a tab control. Some Unicode characters displayed incorrectly in Quick Import Certain extended Unicode characters were displayed as squares when importing data using Quick Import. These characters are now displayed correctly. Modern Chart titles truncated in Print Preview When viewing a report containing Modern Charts in Print Preview, chart titles might be truncated. Chart titles now render correctly in Print Preview. Some Unicode characters displayed incorrectly in exported object names When exporting an object whose name contained certain extended Unicode characters, the sheet name in the exported file displayed the characters incorrectly. These characters are now preserved correctly during export. Some Unicode characters displayed incorrectly in error messages for long object names When renaming a database object to a name that was too long, the error message displayed certain extended Unicode characters incorrectly. These characters now display correctly in error messages. Standard colors in Access didn't match other Office apps The standard color palette in Access used different color values than other Office applications like Word and Excel. For example, the standard red in Access was #ED1C24 instead of the updated Office standard red #EE0000. The color palette has been updated to match the rest of Office. Field.Properties("Precision") and Properties("Scale") on a query column caused the query to execute Accessing the Precision or Scale properties of a field in a query's Fields collection caused the query to execute. This was a regression that broke add-ins and code that enumerate field properties, since query execution can be expensive and have side effects. These properties are now returned without executing the query. Toggle filter button in status bar didn't work when no records were displayed When a form filter resulted in no matching records, clicking the Toggle Filter button in the status bar to remove the filter had no effect. The button now properly removes the filter even when the filtered result set is empty. PDF files with capitalized extension didn't render in Edge Browser Control When navigating to a PDF file using the Edge Browser control, if the file extension was capitalized (e.g., ".PDF" instead of ".pdf"), the file contents were rendered as raw text instead of as a formatted PDF document. The extension comparison is now case-insensitive. Monaco SQL editor inserted blank lines in multi-line comments When switching between Design View and SQL View, the Monaco SQL editor inserted blank lines between each line inside /* multi-line comment */ blocks. This affected both local and passthrough queries. The editor now preserves the original comment formatting. Please continue to let us know if this is helpful and share any feedback you have.Partner perspective: How Breakthru uses App Advisor and AI-listing optimization to drive growth
Optimizing a Marketplace listing isn’t just a marketing exercise—it directly impacts discoverability, demand, and revenue. But knowing what to change (and when) can be challenging for software development companies. In this partner‑written blog post, Marketplace software development company Breakthru shares firsthand experience using AI‑powered listing recommendations in App Advisor to move from guesswork to confident, data‑driven optimization—without risking listing performance. Dan Langille also reflects on how App Advisor became a core part of their business, what’s working in practice, and how AI is changing how teams iterate on their Marketplace presence. 👉 Read the partner story here: Improve Marketplace outcomes with AI‑powered listing recommendations in App Advisor Discussion prompts for the community: Would AI‑driven recommendations change how often you iterate on your listing? Have you used App Advisor for selling and growing app and AI agent sales? Curious to hear how other Marketplace partners are approaching listing optimization today!From Terminal to Autonomous Coding: Mastering GitHub Copilot CLI ACP Server
Introduction The rise of AI-powered development is no longer just about autocomplete—it’s about autonomous agents that can think, act, and collaborate. At the center of this transformation is the Agent Client Protocol (ACP) and its integration with GitHub Copilot CLI. If you’ve ever wanted to: Integrate Copilot into your own tools Build custom AI-driven developer workflows Orchestrate coding agents in CI/CD Then understanding the GitHub Copilot CLI ACP Server is a game-changer. This article will take you from zero to advanced, covering concepts, architecture, setup, and real-world use cases. What Is Agent Client Protocol (ACP)? The Agent Client Protocol (ACP) is an open standard designed to connect clients (like IDEs or tools) with AI agents in a consistent and interoperable way. Why ACP Exists Before ACP: Every IDE needed custom integration for each AI agent Every agent needed custom APIs per editor ACP solves this by introducing a universal communication layer. Key Idea “Any editor can talk to any agent.” Core Capabilities ACP enables: Standardized messaging between client and agent Streaming responses Tool execution with permissions Session lifecycle management Multi-agent coordination This makes ACP a foundation layer for the agentic developer ecosystem. What Is GitHub Copilot CLI ACP Server? GitHub Copilot CLI can run as an ACP-compatible server, exposing its AI capabilities programmatically. 👉 In simple terms: It turns Copilot into a backend AI agent service that any tool can connect to. According to GitHub Docs: Copilot CLI can run in ACP mode using a flag It supports standardized communication via ACP It enables integration with IDEs, pipelines, and custom tools Architecture: How ACP + Copilot CLI Works Components Component Role Client Sends prompts, receives responses ACP Protocol Standard communication layer Copilot CLI AI agent executing tasks System Files, repos, tools Getting Started (Beginner Level) Install GitHub Copilot CLI Ensure: Copilot subscription is active CLI installed and authenticated Start ACP Server Default (stdio mode – recommended) TCP Mode (for remote systems) stdio: Best for IDE integration TCP: Best for distributed systems Connect Using ACP Client (Example) Using TypeScript SDK: You: Start Copilot as a process Create streams Initialize connection Send prompt Receive streaming response ACP uses: NDJSON streams over stdin/stdout Event-driven communication ACP Workflow Explained A typical flow looks like this: Step-by-step lifecycle Initialize connection Create session Send prompt Agent processes task Streaming updates returned Optional tool execution (with permissions) Session ends ACP supports: Text + multimodal inputs Incremental responses Cancellation and control Real-World Use Cases IDE Integration (Custom Editors) Build your own AI-powered editor: Connect via ACP Send code context Receive suggestions CI/CD Automation Imagine: Use ACP to: Auto-fix bugs Generate tests Refactor code Multi-Agent Systems ACP enables: Copilot + other agents working together Task delegation Workflow orchestration Custom Developer Tools Examples: AI code review dashboards Internal dev assistants ChatOps integrations Advanced Concepts Session Management ACP allows: Isolated sessions Custom working directories Context persistence Streaming Responses Instead of waiting: Receive responses in chunks Build real-time UIs Permission Handling ACP includes: Tool execution approvals Security boundaries Controlled automation Extensibility ACP supports: Multiple SDKs (TypeScript, Python, Rust, Kotlin) Custom clients Future protocol evolution ACP vs Traditional Integration Feature Traditional APIs ACP Integration Custom per tool Standardized Streaming Limited Native Multi-agent Hard Built-in Extensibility Low High Interoperability Poor Excellent Why ACP + Copilot CLI Is a Big Deal This combination unlocks: ✅ Platform-level AI integration No more vendor lock-in per editor ✅ True agentic workflows Agents don’t just suggest—they act ✅ Ecosystem growth Any tool can plug into Copilot Challenges & Considerations ACP is still in public preview Requires understanding of: Streams Async communication Debugging agent workflows can be complex Future of Developer Experience ACP represents a shift toward: “AI-native development platforms” Future possibilities: Fully autonomous CI/CD pipelines Cross-agent collaboration Self-healing codebases Final Thoughts The GitHub Copilot CLI ACP Server is not just a feature—it’s a foundation for the next generation of software development. If you are: A developer → build smarter tools A tech lead → design AI-driven workflows A CTO aspirant → understand this deeply Then ACP is something you must master early. Quick Summary ACP = Standard protocol for AI agents Copilot CLI = Can run as ACP server Enables = IDEs, CI/CD, multi-agent systems Key power = interoperability + automationA new Excel Think Tank
After nearly 30 years of using Excel commercially, I am now coming to retirement. But before I finally hang up my Excel boots, I have setup a small Excel think tank. The idea being people can send me their issues and I will work with you to build your permanent solution in Excel. I have created a number of solutions from Email Validator, Automatic dashboard creators, Fraud analysis, Auto resume makes, Music Syns (All in Excel), so if give it try.
Lost Access to Microsoft Authenticator
Hello team, I reimage my phone, unfortunately lost Microsoft Authenticator, I dont have cloud backup enabled in my Authenticator, I try login account in Microsoft Authenticator, but still ask me Authenticator verification, and no other option (like email, SMS, or backup codes) shown, I'm register Microsoft 365 developer account, i'm only admin, could you please let me know the exact steps I need to take or if there is a support escalation route I should follow, thanks for advance.
