Agile Methodologies Guide

Reflecting on Agile Development with DevOps 2.0: A Flexible CI/CD Flow Last year, I shared a CI/CD process flow for Agile Development with DevOps 2.0, and it’s been amazing to see how much it resonated with the community! This framework isn’t about specific tools—it’s about creating a seamless, collaborative process that supports quality and agility at every step. ✅ 𝗣𝗹𝗮𝗻: Building a Strong Foundation with Clear Alignment The journey begins with planning—whether it's user stories, tasks, or broader product goals. Tools like JIRA or Asana (or any project management platform) help capture requirements and align the team with the Product Owner’s vision. This early alignment is essential to avoid misunderstandings and establish a shared understanding of success. Key Insight: Planning thoroughly and involving stakeholders from the start leads to a smoother process. When everyone’s on the same page, the entire pipeline benefits. ✅ 𝗖𝗼𝗱𝗲: Collaborative Development and Real-Time Feedback In the coding phase, developers work together, often pushing code to a version control platform like GitHub or Bitbucket and communicating via real-time collaboration tools like Slack or Teams. Open communication and continuous feedback help catch issues early and keep the team in sync. Key Insight: Real-time feedback is crucial for speed and quality. Regardless of the tools, creating a culture of continuous collaboration makes all the difference. ✅ 𝗕𝘂𝗶𝗹𝗱: Automating Quality and Security Checks As code is committed, it’s essential to automate quality and security checks. Tools like Jenkins, CircleCI, or any CI/CD platform can trigger builds and run automated tests, ensuring that quality checks are consistent and fast. This step helps prevent issues from creeping into production. Key Insight: Automated checks for quality and security are invaluable. Integrating these checks into the build process improves confidence in every deployment. ✅ 𝗧𝗲𝘀𝘁: Structured, Multi-Environment Testing Testing is layered across environments—whether it’s regression, unit, or user acceptance testing (UAT). Using frameworks like Selenium for automated testing or dedicated QA/UAT environments enables rigorous validation before production. Key Insight: Testing across environments is a safeguard for quality. Structured testing helps ensure that code is reliable and ready for release. ✅ 𝗥𝗲𝗹𝗲𝗮𝘀𝗲: Scalable, Reliable Deployments with Infrastructure as Code (IAC) Finally, using Infrastructure as Code (IAC) principles with tools like Terraform, Ansible, or other IAC solutions, deployments are made repeatable and scalable. IAC empowers teams to manage infrastructure more efficiently, ensuring consistent and controlled releases. Thank you to everyone who has engaged with this diagram and shared your insights! I’d love to hear how others approach CI/CD. Are there any tools or strategies that have worked well for you?

𝗠𝗼𝘀𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗳𝗿𝗼𝗺 𝗱𝗼𝗶𝗻𝗴 𝘁𝗼𝗼 𝗺𝘂𝗰𝗵, 𝗻𝗼𝘁 𝘁𝗼𝗼 𝗹𝗶𝘁𝘁𝗹𝗲. The difference between good PMs and the great ones lies in their ability to say "no" with conviction.  Prioritization isn’t about task management, it’s about strategic sacrifice. The frameworks you use determine whether you:   - Multiply impact (or spread teams thin)   - Build what moves the needle (or what’s loudest)   - Create category-defining products (or bloated ones)  𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 7 𝗺𝗼𝘀𝘁 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗱𝗲𝗰𝗼𝗱𝗲𝗱:  1️⃣ RICE – When you need to quantify "gut feel" (Score Reach, Impact, Confidence, Effort)   2️⃣ MoSCoW – For ruthless trade-offs (Must-have, Should-have, Could-have, Won’t-have)   3️⃣ Kano Model – To separate "delighters" from "basics" (Before competitors copy them)   4️⃣ Opportunity Scoring – When user pain points > feature ideas   5️⃣ Weighted Scoring – For stakeholder battles (Math beats opinions)   6️⃣ User Story Mapping – To prioritize features based on the user journey   7️⃣ Value vs Effort Matrix – The 2x2 that kills pet projects  Swipe for the breakdown on each framework! Your turn: Which framework has saved you from a disaster? (Or which one needs a funeral?) 👇  

I don’t write test cases until I’ve torn the user story apart. Sounds aggressive? It has to be. Because here’s what I’ve learned after 25+ years in testing: Most testers blindly trust what’s in the user story. No questions. No pushback. Just start writing tests and hope for the best. Then week 2 hits, and everything falls apart. You begin second-guessing the story. You ping the BA/PO mid-sprint. Dev keeps building off half-baked assumptions. Your test cases? Useless. Time to rewrite. Defects pile up. Rework shoots up—work pressure increases. Sprint turns into survival mode. 🔥 I’ve seen it too many times. So here’s what I do instead: 1. Review user stories before the sprint starts 2. Challenge every assumption 3. Clarify what “done” really means 4. Align with the team while there’s still time to pivot It’s not fancy. It’s not complicated. But this is how you stop chaos before it starts. And yet, most teams still skip it. Why? 👉 What’s stopping teams from reviewing stories early? #SoftwareTesting #QualityAssurance #TestMetry

𝗖𝗜/𝗖𝗗 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗶𝗻 𝟳 𝘀𝗶𝗺𝗽𝗹𝗲 𝘀𝘁𝗲𝗽𝘀 — 𝘂𝘀𝗶𝗻𝗴 𝗮 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗻𝗮𝗹𝗼𝗴𝘆 Students and early professionals often ask: “What exactly happens between writing code and it going live?” Here’s a high-level view of a CI/CD pipeline — explained through a real-world process, like running a production line at a bakery. 𝗢𝗻𝗲 𝗹𝗶𝗻𝗲. 𝗢𝗻𝗲 𝗮𝗻𝗮𝗹𝗼𝗴𝘆. 𝗢𝗻𝗲 𝗶𝗺𝗽𝗮𝗰𝘁. 1️⃣ Change in Code → Updating the Recipe  A chef refines the cookie recipe for better taste.  A developer updates the source code with a new feature or bug fix. 2️⃣ Code Repository → Saving the New Recipe  The revised recipe is stored where all chefs can access it.  The code is committed to a version control system (e.g., GitHub). 3️⃣ Build → Mixing the Dough  The kitchen team mixes a new dough batch using the recipe.  The code is built — dependencies are resolved and the app is packaged. 4️⃣ Pre-Deployment Tests → Tasting the Dough Before Baking  A sample of the dough is checked for flavor and texture.  Automated tests (unit/integration) are run to detect early issues. 5️⃣ Staging Environment → Baking in a Test Kitchen  A small batch is baked in a test oven for inspection.  Code is deployed to a staging environment that simulates production. 6️⃣ Staging Tests → Internal Quality Review Staff taste and inspect the test batch for quality assurance.  QA performs final testing and validation before public release. 7️⃣ Production → Shipping to Customers  The cookies pass QA and are distributed to stores.  The code is deployed to production and made live for users. CI/CD is not just automation. It’s a quality-first mindset — enabling faster, safer, and more reliable software delivery. If you’re a student, fresher, or just starting out in tech — this is the foundation of modern software engineering. Image credit: Rocky Bhatia #data #engineering #reeltorealdata #devops #softwareengineering #analytics

In the last few months, I have explored LLM-based code generation, comparing Zero-Shot to multiple types of Agentic approaches. The approach you choose can make all the difference in the quality of the generated code. Zero-Shot vs. Agentic Approaches: What's the Difference? ⭐ Zero-Shot Code Generation is straightforward: you provide a prompt, and the LLM generates code in a single pass. This can be useful for simple tasks but often results in basic code that may miss nuances, optimizations, or specific requirements. ⭐ Agentic Approach takes it further by leveraging LLMs in an iterative loop. Here, different agents are tasked with improving the code based on specific guidelines—like performance optimization, consistency, and error handling—ensuring a higher-quality, more robust output. Let’s look at a quick Zero-Shot example, a basic file management function. Below is a simple function that appends text to a file: def append_to_file(file_path, text_to_append): try: with open(file_path, 'a') as file: file.write(text_to_append + '\n') print("Text successfully appended to the file.") except Exception as e: print(f"An error occurred: {e}") This is an OK start, but it’s basic—it lacks validation, proper error handling, thread safety, and consistency across different use cases. Using an agentic approach, we have a Developer Lead Agent that coordinates a team of agents: The Developer Agent generates code, passes it to a Code Review Agent that checks for potential issues or missing best practices, and coordinates improvements with a Performance Agent to optimize it for speed. At the same time, a Security Agent ensures it’s safe from vulnerabilities. Finally, a Team Standards Agent can refine it to adhere to team standards. This process can be iterated any number of times until the Code Review Agent has no further suggestions. The resulting code will evolve to handle multiple threads, manage file locks across processes, batch writes to reduce I/O, and align with coding standards. Through this agentic process, we move from basic functionality to a more sophisticated, production-ready solution. An agentic approach reflects how we can harness the power of LLMs iteratively, bringing human-like collaboration and review processes to code generation. It’s not just about writing code; it's about continuously improving it to meet evolving requirements, ensuring consistency, quality, and performance. How are you using LLMs in your development workflows? Let's discuss!

🧠 Product Ownership Isn't Just a Role—It's a Discipline. A takeaway that really landed with me during Sumeet’ class. If you're not actively managing your Product Backlog, you're not leading your product. 📌 Product Backlog Management is not about maintaining a feature list—it's about making strategic product decisions constantly. It's one of the most underrated yet powerful skills a Product Owner must master. 🎯 A well-managed backlog helps the Scrum Team: ✅ Deliver the correct value at the right time ✅ Reduce ambiguity and rework ✅ Align around a shared Product Goal ✅ Increase transparency for stakeholders ✅ Focus effort on outcomes, not outputs But when backlog management is neglected… ❌ Teams get buried under bloated wish lists ❌ Stakeholders lose trust ❌ Developers waste time refining items no one wants ❌ The product loses direction 🔍 Here's what excellent Product Backlog Management looks like: 🧭 It starts with the Product Goal → Clear, outcome-driven, measurable goals that guide the team toward the vision. 🚫 It includes knowing what not to build → A lean backlog requires ruthless prioritization and the courage to say no—with empathy. 📈 It's ordered by value → Not all bugs deserve fixing. Not all features deserve building. Prioritize by impact. 🧩 It's continuously refined → Break down large items. Add clarity as you learn. Refine collaboratively with the team. 📐 It enables sizing → Empower Developers to estimate using what works best—story points, t-shirt sizing, or right-sizing for one Sprint. 🧠 It's a team sport → Collaborate with stakeholders and Developers. Transparency and feedback shape the best backlog. 📌 Product Owner doesn't just collect requests. They shape strategy through the backlog—one decision at a time. The backlog isn't a to-do list. It's a map of how you'll deliver value—iteratively, transparently, and intentionally. #Scrum #ReTHINKscrum #ProductOwnership #BacklogManagement Agilemania Agilemania Malaysia

Don’t Focus Too Much On Writing More Tests Too Soon 📌 Prioritize Quality over Quantity - Make sure the tests you have (and this can even be just a single test) are useful, well-written and trustworthy. Make them part of your build pipeline. Make sure you know who needs to act when the test(s) should fail. Make sure you know who should write the next test. 📌 Test Coverage Analysis: Regularly assess the coverage of your tests to ensure they adequately exercise all parts of the codebase. Tools like code coverage analysis can help identify areas where additional testing is needed. 📌 Code Reviews for Tests: Just like code changes, tests should undergo thorough code reviews to ensure their quality and effectiveness. This helps catch any issues or oversights in the testing logic before they are integrated into the codebase. 📌 Parameterized and Data-Driven Tests: Incorporate parameterized and data-driven testing techniques to increase the versatility and comprehensiveness of your tests. This allows you to test a wider range of scenarios with minimal additional effort. 📌 Test Stability Monitoring: Monitor the stability of your tests over time to detect any flakiness or reliability issues. Continuous monitoring can help identify and address any recurring problems, ensuring the ongoing trustworthiness of your test suite. 📌 Test Environment Isolation: Ensure that tests are run in isolated environments to minimize interference from external factors. This helps maintain consistency and reliability in test results, regardless of changes in the development or deployment environment. 📌 Test Result Reporting: Implement robust reporting mechanisms for test results, including detailed logs and notifications. This enables quick identification and resolution of any failures, improving the responsiveness and reliability of the testing process. 📌 Regression Testing: Integrate regression testing into your workflow to detect unintended side effects of code changes. Automated regression tests help ensure that existing functionality remains intact as the codebase evolves, enhancing overall trust in the system. 📌 Periodic Review and Refinement: Regularly review and refine your testing strategy based on feedback and lessons learned from previous testing cycles. This iterative approach helps continually improve the effectiveness and trustworthiness of your testing process.

A robust CI/CD pipeline is fundamental to streamlining your software delivery. We recently embarked on establishing a CI/CD pipeline for our team at LXA, and instead of the usual suspects (GitHub Actions, GitLab CI, Jenkins), we opted for GCP’s Cloud Build and Cloud Deploy. Here’s what we learned: Pros: • Serverless: No more managing VMs or clusters! • Enhanced Security: All build steps run within our GCP environment with support for granular service accounts. • Container-First: Native support for GKE/Kubernetes and Cloud Run. • Rapid Testing: Convenient build and deployment triggering without unnecessary commits. • Modern CD Workflow: Built-in support for releases, canaries, promotions, approvals, and rollbacks. • Cost-Effective: True pay-as-you-go pricing. Cons: • Fragmented Experience: Navigating between Cloud Build and Deploy can feel disjointed. • Git Integration: Better traceability with Git metadata (revisions, comments, PRs) would be really ideal. • Steep Learning Curve: Need to understand container and Kubernetes tooling, e.g. Docker, Skaffold, Kustomize. • Notifications: Surprisingly, setting up notifications/alerts is not user-friendly. Managing a CI/CD system can be challenging, especially at scale. Based on our experience so far, Cloud Build and Cloud Deploy seem to provide a good and comprehensive solution to run our CI/CD pipeline. --- Have you tried GCP’s CI/CD tools? Any learning you can share?

I was having team with my neighbors who is Director at a reputed consulting firm. He has seen me facilitate teams for bring clarity through Sketchnotes 📝 He promptly asked me to suggest some way to resolve conflicts in his team. He said “they are always on fire, waiting to put each other down”. My eyes lit up and rolled up 🧠remembering what I did in my team few years ago. In high-performing teams, conflict is inevitable. When collaboration 👥is frequent and stakes are high, differing working styles, communication gaps, and behavioural patterns can often spark friction. But rather than letting these conflicts fester, what if we turned them into opportunities for clarity and growth? One powerful ritual I’ve found useful is something called a Behavioural Retrospective 🙌— a structured conversation that helps teams reflect on behaviours causing friction and co-create better ways of working together. Let’s break it down 🧩 What is a Behavioural Retrospective? Unlike project retrospectives that focus on processes and outcomes, a Behavioural Retrospective dives into the interpersonal actions and behaviours that impact team dynamics. It guides teams to safely surface frustrations, understand the root causes, and collectively agree on more constructive behaviours. Here’s a simple four-step framework to run one: ⸻ 1. Get Frustrations on Paper Start by asking team members to quietly write down actions or behaviours of peers that are frustrating them. Encourage specificity — focusing on actions, not people. ⸻ 2. Take Turns Sharing Create a safe, non-defensive space where team members can take turns sharing what they’ve written. A crucial mindset here: listen to understand, not to defend. Everyone deserves to be heard. ⸻ 3. Ask Revealing Questions Encourage the team to ask revealing, open-ended questions to uncover what’s beneath the surface. This helps build empathy, as people often act from unseen pressures or intentions. ⸻ 4. Make Suggestions for Alternate Behaviours End the session by inviting the team to suggest constructive, alternative behaviours. Focus on actions that can replace the problematic behaviours moving forward. Capture these as actionable, specific agreements. ⸻ Why This Works Behavioural Retrospectives promote empathy, mutual respect, and a culture of continuous improvement within the team. ⸻ If your team has been experiencing behavioural conflicts, this might be a good ritual to introduce in your next cycle. It’s a simple but transformative way to realign as a team — not just on what you build, but how you work together. Have you tried something similar? Would love to hear how you handle behavioural conflicts in your team. #TeamCulture #Leadership #Retrospective #ConflictResolution

Mukta, can you handle this release all by yourself? My manager asked. I took a deep breath and said, Okay, Yes, I’ll do it. This wasn’t just about getting the release out — it was about owning the entire cycle in an Agile/Scrum setup. Here's what it really looked like to me when I started working on it: Analyzing incomplete or ambiguous user stories — had to go back to the PO and stakeholders several times to clarify acceptance criteria Estimating story points with the team, balancing technical effort with business expectations but focusing more on qa efforts. Daily or when required, syncs with developers to unblock issues, prioritize bugs, and adjust scope as needed Managing cross-functional discussions — sometimes it was the PO, other times it was UX or even the solution architect when flows weren’t aligned,things weren't proper. Coordinating bug fixes and regression testing- under tight deadlines, especially when defects came late in the sprint Chasing last-minute changes— scope creep happens even when it's not supposed to, and I had to push back while staying collaborative Juggling between QA ownership and Scrum responsibilities— attending all ceremonies, tracking progress on the board, and ensuring nothing slipped through It was hectic. Not everything went smoothly. But I learned more from this one release than from several previous sprints combined. Key takeaways: Don’t assume stories are ready just because they’re in the backlog — deep dive early strong communication with devs, PO, and designers is everything stay flexible — priorities shift, but quality shouldn’t. agree? Own the outcome — not just your tasks This experience pushed me outside my comfort zone — but that's exactly where the real growth happens, I believe. If you've led a release end-to-end in Agile/Scrum, what’s the biggest challenge you faced? Would love to learn from your experiences too. #releasetime #sprintownership #Scrumenvironment #AgileTesting