Improving Productivity in Modern Software Development

Probably the simplest most-effective way to improve productivity is to reduce your work in progress (things you work on simultaneously) to 1. Think about a situation where you must work with a "platform team." Your team is bopping along until it comes across something it needs to do that the platform can't handle. It then stops work and hands off to the platform team. Rather than being idle while it waits, the first team now starts working on a second thing until it needs a database change, which it hands off to the database team. Not wanting to be idle, it starts working on a third thing. Weinberg points out that every "thing" you work on reduces productivity by about 20%. So, if you have three 5-day tasks. Working on two of them at once adds 20% to each task, so it will take 12 days to do 10 days of work. Add a third task and we're adding 2 days to each task, so it now will take 21 days to do 15 days of work. This isn't even considering what happens if the other team gets it wrong and you need to resubmit the request or the fact that it now takes up to four times longer (21 days rather than 5) to get something useful into your customer's hands. So, to work on only one thing at a time, we need to eliminate the dependencies. Our single product team needs to be able to make platform and database changes (safe ones, at least, to avoid collisions with other teams). They need to align with the other teams when they make those changes so that they don't break anything, but I find that an occasional chapter/guild meeting to deal with consistency issues takes way less time than the time you lose to WIP>1.

As a client project manager, I consistently found that offshore software development teams from major providers like Infosys, Accenture, IBM, and others delivered software that failed 1/3rd of our UAT tests after the provider's independent dedicated QA teams passed it. And when we got a fix back, it failed at the same rate, meaning some features cycled through Dev/QA/UAT ten times before they worked. I got to know some of the onshore technical leaders from these companies well enough for them to tell me confidentially that we were getting such poor quality because the offshore teams were full of junior developers who didn't know what they were doing and didn't use any modern software engineering practices like Test Driven Development. And their dedicated QA teams couldn't prevent these quality issues because they were full of junior testers who didn't know what they were doing, didn't automate tests and were ordered to test and pass everything quickly to avoid falling behind schedule. So, poor quality development and QA practices were built into the system development process, and independent QA teams didn't fix it. Independent dedicated QA teams are an outdated and costly approach to quality. It's like a car factory that consistently produces defect-ridden vehicles only to disassemble and fix them later. Instead of testing and fixing features at the end, we should build quality into the process from the start. Modern engineering teams do this by working in cross-functional teams. Teams that use test-driven development approaches to define testable requirements and continuously review, test, and integrate their work. This allows them to catch and address issues early, resulting in faster, more efficient, and higher-quality development. In modern engineering teams, QA specialists are quality champions. Their expertise strengthens the team’s ability to build robust systems, ensuring quality is integral to how the product is built from the outset. The old model, where testing is done after development, belongs in the past. Today, quality is everyone’s responsibility—not through role dilution but through shared accountability, collaboration, and modern engineering practices.

Has Amazon cracked the code on developer productivity with its cost to serve software (CTS-SW) metric? Amazon applied its well-known "working backwards" methodology to developer productivity. "Working backwards" in this case starting with the outcome: concrete returns for the business. This is measured by looking at the rate of customer-facing changes delivered by developers, i.e. "what the team deems valuable enough to review, merge, deploy, and support for customers", in the words of the blog post by Jim Haughwout https://lnkd.in/eqvW5wbi . This metric is different from other measures of developer productivity which look only at velocity or time saved. Instead, "CTS-SW directly links investments in the developer experience to those outcomes by assessing how frequently we deliver new or better experiences. Some organizations fall into the anti-pattern of calculating minutes saved to measure value, but that approach isn’t customer-centered and doesn’t prove value creation." This aligns with Gartner's own research on developer productivity. In our 2024 Software Engineering survey, we asked what productivity metric organizations are using to measure their developers. We also asked about a basket of ten success metrics, including software usability, retention of top performers, and meeting security standards. This allowed us to find out which productivity metric was associated most with success. What we found in our survey was that *rate of customer-facing changes* is the metric most associated with success. Some other productivity metrics were actually *negative associated* with success. But *rate of customer-facing changes* is what organizations should focus on. Sadly, our survey found that few organizations (just 22%) use this metric. I presented this data at our #GartnerApps summit [and the next summit is coming up in September: https://lnkd.in/ey2kpc2 ] Every metrics gets gamed. So I always recommend "gaming the gaming". A developer might game the CTS-SW metric by focusing more on customer-facing changes. But... this is actually a good thing. You're gaming the gaming. We will be watching closely how this metric gets adopted alongside DORA, SPACE, and other metrics in the industry.

GenAI is delivering productivity gains of up to 20% to the software development lifecycle, and Deloitte’s latest research dives into how GenAI is driving this transformation. Faruk Muratovic, Diana Kearns-Manolatos (she/her), and Ahmed Alibage, CMS®, Ph.D. recently published an insightful report in the IEEE Computer Society’s journal [https://deloi.tt/3TtkCC6]. Their findings highlight not only productivity gains, but also the importance of trust and transparency.     Building trust in GenAI starts with thoughtful human oversight. The report recommends keeping humans-in-the-loop (HITL) to ensure code quality, manage risk, and provide transparency. These key actions stand out:    •Promote design transparency and explainability: By fostering open, iterative design, teams can balance innovation with consistent, high-quality results.  •Strengthen code accuracy with clear metrics: Leveraging repeatable measures like defect density and time-to-delivery helps maintain quality and build confidence in GenAI-driven solutions.  •Create a culture of continuous learning and improvement. As GenAI evolves, teams will stay resilient and innovative.    By taking these actions, tech leaders can help build a future where technology and human expertise go hand in hand—delivering real value, safely and responsibly.   

Step-by-Step Guide to Measuring & Enhancing GCC Productivity - Define it, measure it, improve it, and scale it. Most companies set up Global Capability Centers (GCCs) for efficiency, speed, and innovation—but few have a clear playbook to measure and improve productivity. Here’s a 7-step framework to get you started: 1. Define Productivity for Your GCC Productivity means different things across industries. Is it faster delivery, cost reduction, innovation, or business impact? Pro tip: Avoid vanity metrics. Focus on outcomes aligned with enterprise goals. Example: A retail GCC might define productivity as “software features that boost e-commerce conversion by 10%.” 2. Select the Right Metrics Use frameworks like DORA and SPACE. A mix of speed, quality, and satisfaction metrics works best. Core metrics to consider: • Deployment Frequency • Lead Time for Change • Change Failure Rate • Time to Restore Service • Developer Satisfaction • Business Impact Metrics Tip: Tools like GitHub, Jira, and OpsLevel can automate data collection. 3. Establish a Baseline Track metrics over 2–3 months. Don’t rush to judge performance—account for ramp-up time. Benchmark against industry standards (e.g., DORA elite performers deploy daily with <1% failure). 4. Identify & Fix Roadblocks Use data + developer feedback. Common issues include slow CI/CD, knowledge silos, and low morale. Fixes: • Automate pipelines • Create shared documentation • Protect developer “focus time” 5. Leverage Technology & AI Tools like GitHub Copilot, generative AI for testing, and cloud platforms can cut dev time and boost quality. Example: Using AI in code reviews can reduce cycles by 20%. 6. Foster a Culture of Continuous Improvement This isn’t a one-time initiative. Review metrics monthly. Celebrate wins. Encourage experimentation. Involve devs in decision-making. Align incentives with outcomes. 7. Scale Across All Locations Standardize what works. Share best practices. Adapt for local strengths. Example: Replicate a high-performing CI/CD pipeline across locations for consistent deployment frequency. Bottom line: Productivity is not just about output. It’s about value. Zinnov Dipanwita Ghosh Namita Adavi ieswariya k Karthik Padmanabhan Amita Goyal Amaresh N. Sagar Kulkarni Hani Mukhey Komal Shah Rohit Nair Mohammed Faraz Khan

We know LLMs can substantially improve developer productivity. But the outcomes are not consistent. An extensive research review uncovers specific lessons on how best to use LLMs to amplify developer outcomes. 💡 Leverage LLMs for Improved Productivity. LLMs enable programmers to accomplish tasks faster, with studies reporting up to a 30% reduction in task completion times for routine coding activities. In one study, users completed 20% more tasks using LLM assistance compared to manual coding alone. However, these gains vary based on task complexity and user expertise; for complex tasks, time spent understanding LLM responses can offset productivity improvements. Tailored training can help users maximize these advantages. 🧠 Encourage Prompt Experimentation for Better Outputs. LLMs respond variably to phrasing and context, with studies showing that elaborated prompts led to 50% higher response accuracy compared to single-shot queries. For instance, users who refined prompts by breaking tasks into subtasks achieved superior outputs in 68% of cases. Organizations can build libraries of optimized prompts to standardize and enhance LLM usage across teams. 🔍 Balance LLM Use with Manual Effort. A hybrid approach—blending LLM responses with manual coding—was shown to improve solution quality in 75% of observed cases. For example, users often relied on LLMs to handle repetitive debugging tasks while manually reviewing complex algorithmic code. This strategy not only reduces cognitive load but also helps maintain the accuracy and reliability of final outputs. 📊 Tailor Metrics to Evaluate Human-AI Synergy. Metrics such as task completion rates, error counts, and code review times reveal the tangible impacts of LLMs. Studies found that LLM-assisted teams completed 25% more projects with 40% fewer errors compared to traditional methods. Pre- and post-test evaluations of users' learning showed a 30% improvement in conceptual understanding when LLMs were used effectively, highlighting the need for consistent performance benchmarking. 🚧 Mitigate Risks in LLM Use for Security. LLMs can inadvertently generate insecure code, with 20% of outputs in one study containing vulnerabilities like unchecked user inputs. However, when paired with automated code review tools, error rates dropped by 35%. To reduce risks, developers should combine LLMs with rigorous testing protocols and ensure their prompts explicitly address security considerations. 💡 Rethink Learning with LLMs. While LLMs improved learning outcomes in tasks requiring code comprehension by 32%, they sometimes hindered manual coding skill development, as seen in studies where post-LLM groups performed worse in syntax-based assessments. Educators can mitigate this by integrating LLMs into assignments that focus on problem-solving while requiring manual coding for foundational skills, ensuring balanced learning trajectories. Link to paper in comments.

I started my software career in 1999 at 𝗜𝗕𝗠. In 2004 I joined 𝗕𝗖𝗚 to be even more challenged (I wasn’t disappointed). 𝗙𝗮𝘀𝘁‑𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝘁𝗼 𝟮𝟬𝟮𝟱: with an AI assistant (the free 𝗚𝗲𝗺𝗶𝗻𝗶 extension in 𝗩𝗦 𝗖𝗼𝗱𝗲) + 𝗣𝘆𝘁𝗵𝗼𝗻, I’ve re‑discovered the joy of “𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗳𝗹𝗼𝘄.” Nights and weekends turned into personal builds: a GPU‑intensive underwater/dive color‑correction pipeline, archival indexing, and upscaling old VHS videos. The productivity boost came from the combo of 𝗔𝗜 + 𝗣𝘆𝘁𝗵𝗼𝗻: vast libraries (math, video, image, PDF — there’s a library for everything) and compact script that amplifies the AI context window. Software productivity is 𝐦𝐮𝐥𝐭𝐢𝐟𝐚𝐜𝐭𝐨𝐫𝐢𝐚𝐥, it's not only AI: 2005 → 2025 learnings • 📦 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 ecosystems & Python/JS → assemble, don’t hand‑code (PyPI ~700k projects). • 🔀 𝗚𝗶𝘁 + PRs + CI/CD → lightweight branching & continuous delivery enabled agile at scale. • ☁️ 𝗖𝗹𝗼𝘂𝗱 & managed services → building blocks on demand; by 2025, cloud‑native platforms underpin >95% of new digital initiatives. • 🐳 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 & 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 → consistent, portable runtime + scaling; ~66% used K8s in production in 2023. • 🤖 𝗔𝗜 coding assistants → big gains when used well (one study: 55% faster on tasks); typical uplift is ~10–15% unless you also streamline reviews, integration, and release. In my case, that meant unit testing to prevent the AI from introducing regressions in a complex pipeline. • 🧮 𝗚𝗣𝗨 acceleration & ML → matrix math and high‑perf compute are now cheap and more efficient than traditional parallel programming. • ⚙️ 𝗗𝗲𝘃 environments as code → devcontainers/Codespaces slash setup time (from 2 days to ~1 minute with prebuilds). 𝗪𝗵𝘆 𝗶𝘁’𝘀 𝗲𝗮𝘀𝗶𝗲𝗿 𝘁𝗼𝗱𝗮𝘆 • You rarely start from scratch (huge package ecosystems). • Scripting languages (Python/JS) are extremely expressive and powerful. • AI accelerates scaffolding, tests, and learning unfamiliar APIs. • Cloud gives infra, data, and AI services on demand (or run it all locally if you prefer). • Containers/devcontainers and Git‑centric workflows reduce “works on my machine.” …𝗮𝗻𝗱 𝗵𝗮𝗿𝗱𝗲𝗿 • Scripting languages generate a lot more edge cases generating more maintenance and requiring solid commenting / unit testing / integration testing. • Distributed by default (microservices, k8s) → higher cognitive load & ops overhead. • Supply‑chain risk (package compromises) → pin versions, use SCA, sign releases. • AI isn’t magic: assistants can propose insecure/incorrect code (~40% vulnerable in one security‑critical test set). Guardrails matter (tests, code review, SAST). • Hydrid cloud choice/cost complexity → flexibility can create overruns without strong platform/FinOps. ✨I adhere to Andrej Karpathy's #Software3 vision with AI, but we must consider how much easier and complex software development has become in 20 years! #SDLC #AI #BCGX Dr. Jan Ittner

Redefining Productivity in Software Development: Beyond Lines of Code In the fast-paced world of software development, productivity often gets measured in lines of code, features shipped, and bugs fixed. But, is this truly the hallmark of our best developers? 1. **Innovative Problem-Solving**: The most valuable developers are those who solve problems in ways that prevent future issues, potentially reducing the need for additional code. Encourage a culture where innovative solutions are celebrated over sheer output. 2. **Mentorship and Collaboration**: Exceptional developers elevate the skills of those around them. By mentoring juniors, they multiply their own productivity across the team. Recognise and reward the role of mentorship in your team’s success metrics. 3. **Technical Debt Reduction**: Often overlooked, the effort to reduce technical debt is a long-term investment in productivity. Developers who focus on clean, maintainable code ensure faster and more reliable output in the future. Shift your performance metrics to value quality and sustainability. 4. **Strategic Contributions**: The strategic insight that senior developers bring to a project can far outweigh the immediate productivity of coding. Their contributions to architecture, technology selection, and process improvement can set the stage for exponential growth. Let’s start valuing the qualities that truly enhance our teams. Foster an environment where strategic thinking, mentorship, and innovation are as prized as coding speed.

For decades, engineering teams have been measured by lines of code, commit counts, and PRs merged—but does more code actually mean more productivity? 🚀 Some of the best developers write LESS code, not more. 🚀 The fastest-moving teams focus on outcomes, not just output. 🚀 High commit counts can mean inefficiency, not impact. Recent research from DORA, GitHub, and real-world case studies from IT Revolution debunk the myth that developer activity = developer productivity. Here’s why: 🔹 DORA Research: After studying thousands of engineering teams, DORA (DevOps Research & Assessment) found that the best teams optimize for four key engineering performance metrics: ✅ Deployment Frequency → How often do we ship value to users? ✅ Lead Time for Changes → How fast can an idea go from code to production? ✅ Change Failure Rate → Are we improving quality, or just shipping fast? ✅ MTTR (Mean Time to Restore) → Can we recover quickly when things go wrong? → Notice what’s missing? Not a single metric is based on lines of code, commits, or individual developer output. 🔹 GitHub’s Data: GitHub found that developers working remotely during 2020 pushed more code than ever—but many felt less productive. Why? Longer workdays masked inefficiencies. More commits ≠ meaningful work; some were just fighting bad tooling or slow reviews. Teams that automated workflows (CI/CD, code reviews) merged PRs faster and felt more productive. 🔹 IT Revolution case studies: High-performing engineering orgs measure outcomes, not just outputs. The best teams: Shift from tracking commit counts → to measuring customer value. Use DORA metrics to improve DevOps flow, not micromanage engineers. View engineering productivity as a team effort, not an individual scoreboard. If you want a high-performing engineering org, don’t just push developers to write more code. Instead, ask: ✅ Are we shipping value faster? ✅ Are we reducing friction in our workflows? ✅ Are our developers able to focus on meaningful work? 🚨 The takeaway? Great engineering teams don’t write the most code—they deliver the most impact. 📢 What’s the worst “productivity metric” you’ve ever seen? Drop a comment below 👇 #DeveloperProductivity #SoftwareDevelopment #DORA #GitHub #EngineeringLeadership

Systems Thinking: The Productivity Paradox Imagine a riverside town struggling with seasonal flooding. To mitigate, they build higher levees, allowing them to expand housing and businesses into previously flood-prone areas. At first, flooding stops, and the town thrives. But over time, wetlands downstream erode, and floodwaters have nowhere to spread. When a major storm overwhelms the levees, the flooding is worse than ever. More levees and expansion seemed like progress… but the system fought back. Now, imagine a software company struggling with slow delivery. Customers complain. Revenue and reputation are at risk. Executives need a fix. Their answer? Hire more developers. At first, velocity increases. Features ship faster. But soon, delivery is slower than before the new team members were hired. Adding devs was supposed to speed things up (and did, briefly). But… the system fought back. Why Adding Developers Slows Things Down Fred Brooks wrote, “Adding manpower to a late software project makes it later.” Why? Communication grows exponentially. With 5 devs, you manage 10 communication links. With 10, it’s 45. At 20, it’s 190. More meetings, more dependencies, slower decisions. New hires aren't immediately productive. Senior devs have to help with onboarding. As the team grows, more code is written, leading to merge conflicts, longer pull request cycles, and WIP stuck in queues. More teams means unclear ownership, more handoffs, and more rework. What's The Solution? 1) Reduce WIP Too much WIP slows teams down. Before hiring, ask: Are we prioritizing finishing over starting? Can we reduce batch sizes? Can we use Kanban and/or Scrum to improve flow without adding people? 2) Think Structure, Not Size More devs means higher coordination costs. Instead, use Team Topologies. Stream-aligned teams own end-to-end delivery. Enabling teams improve developer focus. Platform teams reduce cognitive load. 3) Automate and Improve Code Quality Don't maximize team size, optimize the pipeline. Faster CI/CD reduces delays, automated testing prevents bug creep, and refactoring improves maintainability. 4) Use Modular Architecture A tightly coupled system slows everyone down. Shift to microservices or modular monoliths (single deployable units), use feature flags for incremental deployment, and apply domain-driven design (DDD) to define team boundaries. 5) Measure Outcomes, Not Headcount Leaders may wrongly assume more developers means more output, but real productivity is about flow efficiency. Instead of team size, track: Cycle time (how long it takes to ship), Deployment frequency (how often we deliver), and Lead time for changes (how fast we adapt). Systems, Not Silos When teams slow down, hiring feels like the obvious fix. But it should be the last resort, not the first instinct. Without systems thinking, hiring may have unintended consequences that ironically make things worse. Rethink how your system works... before the next storm.