Software Development Lifecycle In Engineering

How to compare your eng team's velocity to industry benchmarks (and increase it): Step 1: Send your eng team this 4-question survey to get a baseline on key metrics: https://lnkd.in/gQGfApx4 You can use any surveying tool to do this—Google Forms, Microsoft Forms, Typeform, etc.—just make sure you can view the responses in a spreadsheet in order to calculate averages. Important: responses must be anonymous to preserve trust, and this survey is designed for people who write code as part of their job. Step 2: Calculate your how you're doing. - For Speed, Quality, and Impact, find the average value for each question’s responses. - For Effectiveness, calculate the percent of favorable responses (also called a Top 2 Box score) across all Effectiveness responses. See the example in the template above. Step 3: Track velocity improvements over time. Once you’ve got a baseline, you can start to regularly re-run this survey to track your progress. Use a quarterly cadence to begin with. Benchmarking data, both internal and external, will help contextualize your results. Remember, speed is only relative to your competition. Below are external benchmarks for the key metrics. You can also download full benchmarking data, including segments on company size, sector, and even benchmarks for mobile engineers here: https://lnkd.in/gBJzCdTg Look at 75th percentile values for comparison initially. Being a top-quartile performer is a solid goal for any development team. Step 4: Decide which area to improve first. Look at your data and using benchmarking data as a reference point, pick which metric you believe will make the biggest impact on velocity. To make this decision about what to work on to improve product velocity, drill down to the data on a team level, and also look at qualitative data from the engineers themselves. Step 5: Link efficiency improvements to core business impact metrics Instead of presenting these CI and release improvement projects as “tech debt repayment” or “workflow improvements” without clear goals and outcomes, you can directly link efficiency projects back to core business impact metrics. Ongoing research (https://lnkd.in/grHQNtSA) continues to show a correlation between developer experience and efficiency, looking at data from 40,000 developers across 800 organizations. Improving the Effectiveness score (DXI) by one point translates to saving 13 minutes per week per developer, equivalent to 10 hours annually. With this org’s 150 engineers, improving the score by one point results in about 33 hours saved per week. For so much more, don't miss the full post: https://lnkd.in/grrpfwrK

AI won’t kill the Scrum Master role. But it will expose the ones who were just glorified Jira babysitters. You know the type: • Runs the Daily. • Shares the Burndown. • Asks, “Any blockers?” like a broken record. AI can summarize standups, track metrics, and even write user stories The bar has moved. Permanently. What high-performing SMs are doing in 2025 to stay relevant? --------------------------------------------------------------------- 🔹 1. Sprint Planning is a Strategy Room — not a calendar block → Use AI to surface delivery risks based on historical velocity → Guide trade-offs: “Here’s the scope we can commit to with 85% confidence” → Train teams on capacity forecasting using actual throughput 🔹 2. Backlog Refinement = Opportunity to Level Up Your PO → Use ChatGPT to draft acceptance criteria, or flag logical gaps → Run backlog refinement like a product-thinking workshop → Push for clarity, not just ticket grooming 🔹 3. Retrospectives Should Feel Like a Coaching Session, Not a Routine → Go beyond “what went well” → Use AI to analyze sprint data or retro notes for patterns → Start with: “What’s draining our energy right now?” 🎯 Agile isn’t about ceremonies. It’s about conversations that lead to outcomes. Your edge as a Scrum Master isn’t your ability to remove blockers — It’s your ability to elevate the thinking of the team. In a world where tools are getting smarter… Make sure your impact isn’t just seen — but felt. 👇 What’s one AI-powered move you’re using today as an SM?

I have put together this DevOps Metrics infographic - it's like a cheat sheet for keeping your finger on the pulse of your entire development pipeline. Let's break it down- We start with the "Plan" phase - because hey, failing to plan is planning to fail, right? 😉 We're talking Sprint Burndown, Team Velocity, and even Epic Burndown. These metrics help you understand if your team is biting off more than they can chew or if they're ready to take on more challenges. Moving on to "Code" - this is where the rubber meets the road. Code Reviews, Code Churn, Technical Debt - these aren't just buzzwords, folks. They're vital signs of your codebase's health. And don't get me started on the importance of Maintainability Index! The "Build" and "Test" phases are where things get real. Build Success Rate, Test Coverage, Defect Metrics - these are your early warning systems. They'll tell you if you're building on solid ground or if you're in for a world of hurt down the line. Now, "Release" and "Deploy" - this is where many teams start sweating. But with metrics like Release Duration, Deployment Frequency, and Change Failure Rate, you can turn this nail-biting phase into a smooth, predictable process. Finally, "Operate" and "Monitor" - because your job isn't done when the code hits production. Customer Feedback, System Uptime, Mean Time to Detect and Repair - these metrics ensure you're not just shipping code, but delivering value. The best part? I've included some of the go-to tools for each phase. Jira, GitHub, Gradle, Jenkins, Docker, Kubernetes - these aren't just fancy names, they're the workhorses that'll help you track these metrics without losing your mind. Remember, folks - you can't improve what you don't measure.

Have you ever noticed how software that functions seamlessly often goes unnoticed, while problematic ones capture all the attention? Through my experience across various industries, I have seen firsthand that when projects derail, they demand significant interventions from executive oversight to teams working late into the night. But why do we tend to focus more on failures than successes? This observation underscores the importance of foundational work in engineering, often referred to as #DeliveryAssurance. Quality assurance is about much more than just detecting defects. It's about designing robust software that stands the test of time. It is about getting the basics right to ensure our software doesn't just meet current standards but is also resilient enough to withstand future challenges. This not only involves the quality of functionalities, features, and user interfaces but also the crucial non-functional requirements such as security, performance, stability and reliability. Embracing a 'shift-left' approach, or building quality in from the start, is essential. By ensuring the creator of the software is also its first tester, we should embed quality right at the development stage. This proactive stance is crucial for developing systems that function efficiently upon delivery and continue to provide reliable service in the long run. Embracing AI in quality assurance is also essential as it will not only make the process efficient but will also improve the overall quality of software. #QualityAssurance #software #engineering #FTR #Reliability

Too many AI strategies are being built around the technology instead of the business challenges they should solve. The real value of AI comes when it is directly tied to your goals. I have arrived at seven lessons on how to align your AI strategy directly with your business goals: 1. Start with the "why," not the "what." Before discussing models or tools, ask what business problem you need to solve. It could be speeding up product development, or cutting operational costs. Let that answer be your guide. 2. Think in terms of business outcomes. Measure AI success by its impact on metrics like revenue growth or employee productivity not by technical accuracy. 3. Build a cross-functional team. AI can't live solely in the IT department. Include leaders from all relevant departments from day one to ensure the strategy serves the entire business. 4. Prioritize quick wins to build momentum. Identify a few small, high-impact projects that can deliver results quickly. This builds organizational confidence and makes people ready to take on larger initiatives. 5. Invest in data foundations. The best AI strategy will fail without clean and well-governed data. A disciplined approach to data quality is non-negotiable. 6. Focus on change management. Technology is the easy part. Prepare your people for new workflows and equip them with the skills to work alongside AI effectively. 7. Create a feedback loop. An AI strategy is not a one-time plan. Continuously gather feedback from users and analyze performance data to adapt and refine your approach. The goal is to make AI a part of how you achieve your objectives, not a separate project. #AIStrategy #BusinessGoals #DigitalTransformation #Leadership #ArtificialIntelligence

🌍 The Real Reason Your Team Isn’t Connecting Might Surprise You 🛑 You’ve built a diverse team. Communication seems clear. Everyone speaks the same language. So why do projects stall? Why does feedback get misread? Why do brilliant employees feel misunderstood? Because what you’re facing isn’t a language barrier—it’s a cultural one. 🤔 Here’s what that looks like in real life: ✳ A team member from a collectivist culture avoids challenging a group decision, even when they disagree. ✳ A manager from a direct feedback culture gets labeled “harsh.” ✳ An employee doesn’t speak up in meetings—not because they don’t have ideas, but because interrupting feels disrespectful in their culture. These aren't missteps—they’re misalignments. And they can quietly erode trust, engagement, and performance. 💡 So how do we fix it? Here are 5 ways to reduce misalignments and build stronger, more inclusive teams: 🧭 1. Train for Cultural Competence—Not Just Diversity Don’t stop at DEI 101. Offer immersive training that helps employees navigate different communication styles, values, and worldviews. 🗣 2. Clarify Team Norms Make the invisible visible. Talk about what “respectful communication” means across cultures. Set expectations before conflicts arise. 🛎 3. Slow Down Decision-Making Fast-paced environments often leave diverse perspectives unheard. Build in time to reflect, revisit, and invite global input. 🌍 4. Encourage Curiosity Over Judgment When something feels off, ask: Could this be cultural? This small shift creates room for empathy and deeper connection. 📊 5. Audit Systems for Cultural Bias Review how you evaluate performance, give feedback, and promote leadership. Are your systems inclusive, or unintentionally favoring one style? 🎯 Cultural differences shouldn’t divide your team—they should drive your innovation. If you’re ready to create a workplace where every team member can thrive, I’d love to help. 📅 Book a complimentary call and let’s talk about what cultural competence could look like in your organization. The link is on my profile. Because when we understand each other, we work better together. 💬 #CulturalCompetence #GlobalTeams #InclusiveLeadership #CrossCulturalCommunication #DEIStrategy

Your CFO wants to know the return on your software development budget? Here are 5 metrics that actually matter in the boardroom - and they're not story points. As a CTO, I've found these key metrics create a meaningful fitness function for your development organization: 1. Business Value per Feature: Don't just ship features - measure their impact. That new checkout process? Track how it changes conversion rates and order values. 2. Lead Time from Idea to Impact: Understand your value stream. Sometimes a 30-minute deployment is stuck behind weeks of stakeholder meetings. 3. Throughput and its composition: Monitor the balance between new features, maintenance, and bug fixes. When maintenance exceeds 25%, it's time to invest. 4. Quality Signals: Track customer experience, operational efficiency, and technical health. These are your early warning system. 5. Team Health: Happy teams deliver better results. Regular pulse checks predict delivery performance weeks before metrics show issues. But never compare teams through these metrics. Each team operates in a unique context with different challenges. Instead, help each team understand and improve their own trends. Metrics should drive improvement, not punishment. Use them as a compass, not a hammer. What metrics do you use to measure development success?

I’ve been working as a QA Engineer for nearly a decade now. And if there’s one lesson that’s stayed true across every release, it’s this: You don’t earn respect by finding bugs. You earn it by how you handle them. The best testers I’ve worked with? They weren’t loud. They didn’t shame. They didn’t brag. → They raised issues with clarity — not ego. → They questioned with intent — not arrogance. → They challenged the build — without breaking the team. Because great QA isn’t about pointing fingers. It’s about holding the product — and the people behind it — to a higher standard. I’ve seen features saved at the last minute because a QA spoke up with empathy. I’ve seen teams rally because a tester didn’t just report a bug — they offered a solution. And I’ve seen developers thank QAs not because they had to… But because they knew someone had their back. So if you’re just starting in testing, here’s something to remember: Your role isn’t just to test code. It’s to strengthen trust. One conversation, one check-in, one clean report at a time. Because in the end, the best QA engineers don’t just ship better products. They build better teams. #QualityAssurance #SoftwareTesting #TeamCulture #QAEngineer #LeadershipInTech #TestingMindset #EmpathyInTech #AutomationTesting #TechGrowth #QACommunity #bharatpost

Why product roadmaps should be outcome based not feature-driven We do sprints to ship features, and they don’t always work out. Why? Because features alone don’t move the needle -outcomes do. A practice that I usually follow is to ask myself: What problem are we solving, and how will we measure success?” And that’s how we pivot from feature factories to outcome-driven roadmaps with actionable steps to make it stick. ���𝗵𝘆 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 > 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Outcome-based roadmaps focus on measurable results (e.g., “Increase free-to-paid conversion by 15%” vs. “Build a pricing calculator”). This shift: - Aligns teams around business goals, not just deliverables. - Empowers creativity (solve the problem, don’t just check a box). - Reduces waste by killing initiatives that don’t drive impact. But how do you actually make this work? Here’s My Practical Playbook 👇🏻 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 “𝗪𝗵𝘆” - Define outcomes tied to business goals: Partner with leadership to align on 1-2 KPIs per quarter (e.g., “Reduce churn by 10%”). - Ask this question: “If we deliver X feature, what outcome does it enable?”. If there’s no clear answer, rethink it. 2️⃣ 𝗕𝗿𝗲𝗮𝗸 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗶𝗻𝘁𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 Outcomes are broad—break them into testable hypotheses. - Example: To “Increase user engagement by 20%,” run:   - A/B test push notification timing.   - Pilot a gamified onboarding flow.   - Measure DAU/WAU ratios weekly. 3️⃣ 𝗔𝗱𝗼𝗽𝘁 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 - OKRs: Link Objectives (outcomes) to Key Results (metrics). - Impact Mapping: Visualize how features connect to goals. - RICE Scoring: Prioritize initiatives by Reach, Impact, Confidence, Effort. 4️⃣ 𝗚𝗲𝘁 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗕𝘂𝘆-𝗜𝗻 - Frame outcomes as ROI: Show how “Reduce support tickets by 25%” cuts costs. - Prototype outcomes first: Share a mock roadmap with leadership, highlighting gaps in current feature-centric plans. 5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Track leading indicators (e.g., user behavior changes) alongside lagging metrics (e.g., revenue). - Celebrate “failures”: Killing a feature that didn’t drive outcomes is a win. 𝟯 𝗧𝗵𝗶𝗻𝗴𝘀 𝘁𝗼 𝗔𝘃𝗼𝗶𝗱 - - Vague outcomes: “Improve UX” → ❌ | “Reduce checkout abandonment by 20%” → ✅. - Overloading the roadmap: Focus on 1-2 outcomes per quarter. - Ignoring feedback loops: Revisit outcomes bi-weekly—adapt as data comes in. This week, try this: Audit your roadmap. For every feature, ask: “What outcome does this serve?” If it’s unclear, reframe it, or cut it. I believe outcome-based roadmaps is a survival tactic. Let’s build products that matter. 👉 How are you bridging the gap between features and impact? Would love to know your process.

How do you build a culture of lasting improvement? 3M’s story is a standout example. This company has been on a journey to tackle pollution in their products and processes—backed by their employees every step of the way. What started with a few small projects to test Lean Six Sigma eventually grew into a massive initiative involving 55,000 trained employees. Over five years, they completed 8,000+ projects that had a real impact: significant cuts in waste and pollution, surpassing each of their initial goals. The key? They didn’t just introduce a methodology—they made it part of their culture. 3M leaders empowered employees to bring their voices and ideas to the table, using “voice of customer” interviews to connect every change to real needs. This approach made each project not only more efficient but also more meaningful to those involved, giving everyone a stake in the outcome. What can we learn from this? Sustainable change often requires going beyond tools and strategies; it means building a culture that values continuous improvement and listens to every voice. 3M’s results, recognized in studies by the EPA, show the potential of Lean Six Sigma when it’s deeply woven into the company’s DNA. It’s a reminder that real change doesn’t come from buzzwords or quick fixes. It’s about thoughtful action, accountability, and a shared commitment to doing better. What could this kind of commitment look like for your team?