Collaborative Development vs Solo Programming Approaches

Our Work in Progress (WIP) should be at least equal to the number of team members. If it is less, it means someone is idle ... So what? That so-called idle person might be helping out someone else ... Any leader telling this does not understand how knowledge work happens. This is not factory work. Even in the factory, Toyota discovered that collaboration leads to better quality. Having a WIP equal to the number of team members means team members work individually. There is no team. There is zero co-creation. Only a single mind thinks about the problem at hand. That means no possibility to innovate at all. Having a WIP equal to half of the team size indicates that team members work in pairs. We now have co-creation. Two minds are looking at the problem. We have different perspectives. This enables innovation. Having a WIP of one is magic. The whole team is collaborating to find the best solution for the problem. We now have a multitude of different perspectives. If the team is diverse enough, the results will even be better. What's more, we do not need any blocking activities any more to satisfy regulations. It is baked in. Better flow. Reduced cost of delay. But, but ... that is going to be expensive! Intuitively, that might look like that. However, research showed that it is less expensive than solo work and delivers higher quality.

What Can Pilots Teach Agile Teams About Productivity? "Pilot" and "copilot" are accountabilities, not titles. Any qualified pilot can fulfill either role depending on the flight. The captain holds ultimate responsibility, but the first officer supports the mission by managing tasks and providing oversight. This teamwork enhances safety, balances workloads, and creates redundancy. Both pilots are equally capable of flying, and research shows flights with copilots are safer. Agile teams can learn from this model. Pair programming mirrors the pilot-copilot dynamic. One developer (the "driver") writes code while the other (the "navigator") reviews it, catches errors and makes suggestions. They swap roles periodically, keeping both team members engaged while sharing responsibility. At first glance, pair programming seems wildly inefficient. Why assign two people to a task one could handle? The answer lies in hidden costs like debugging, rework, and knowledge silos - all of which pair programming helps mitigate. Why Pairs Work Pair programming basically serves as continuous code review, catching bugs early and minimizing more expensive downstream fixes. A 2020 study in IEEE Transactions on Software Engineering found it reduces defects by up to 50%. Consider a solo developer who introduces 10 bugs in a sprint. If each bug takes 4 hours to fix, that’s 40 hours of rework. A pair might introduce only 5 bugs, cutting debugging down to 10 hours and saving 30. Having two perspectives also resolves issues faster. If one developer gets stuck, the other may have a solution. Consider a complex API integration. A solo developer might spend 4 hours troubleshooting, but a pair might solve it in 2, reducing delays. Pair programming also reduces knowledge silos by spreading expertise across the team, improving resilience and reducing risks. Imagine a team of solo programmers deliver 40 story points in a sprint but require 20 hours of debugging in later iterations. A team using pair programming might deliver only 30 points but would avoid defects. By the second sprint, the pair programming team would be more productive than the solo team as defect prevention frees capacity for feature development. AI Copilot AI is emerging as a "digital copilot," because of course it is. AI can review code, suggest improvements, and catch errors in real time. I don't think it can replace human collaboration, but it can complement pair programming - just as autopilot assists flight crews. Take Off By sharing responsibilities, preventing errors, and providing fast feedback, copilots make air travel safer and more efficient. Pair programming achieves similar results for Agile teams. Pair programming is a strategic investment, not a resource drain. It cuts defects, accelerates problem-solving, and spreads knowledge - turning inefficiencies into advantages. Whether your first officer is human or AI, adopting a “copilot mindset” can help deliver better software faster.

Bad code quality? No one knows how to modify other people's code?  Just pair up! 𝗣𝗮𝗶𝗿 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: 𝗣𝗮𝗶𝗿 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 is a where two programmers work together at one workstation. One, the "driver," writes code while the other, the "navigator," reviews each line of code as it's typed, thinking strategically about the direction and spotting potential issues. T hey switch roles frequently to maintain engagement and share perspectives. 𝗠𝗼𝗯 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 extends this concept to more than two developers working on the same code simultaneously. One person is at the keyboard (the driver) while the rest of the team collaboratively navigates. The driver role typically rotates every 15-30 minutes. 𝗙𝗶𝗿𝘀𝘁 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀: 1. Continuous code review: Code is reviewed instantly as it's written, catching issues early 2. Knowledge sharing: Less experienced developers learn from veterans, while veterans gain fresh perspectives 3. Collective ownership: The code belongs to the team, not individuals 4. Real-time problem solving: Complex problems are tackled with multiple viewpoints simultaneously 5. Reduced cognitive load: The workload is shared between multiple minds But I know there is a lot of 𝗿𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 to pair programming. - Two developers doing one person's job - Slows down development and reduces output - Developers prefer working alone - Hard to coordinate across teams/time zones - Constant collaboration is exhausting Effective Implementation Tips: 1. Start gradually - perhaps with just a few hours per day 2. Rotate pairs regularly to spread knowledge 3. Create comfortable workstations designed for two people 4. Establish clear pairing protocols and communication guidelines 5. Use pairing selectively for complex tasks or knowledge transfer 6. Regularly retrospect on pairing effectiveness and adjust accordingly For companies considering pair programming, it's crucial to understand that it's not an all-or-nothing approach. Most successful implementations use a hybrid model where pairing is used strategically for complex tasks, onboarding, or critical features, while simpler tasks might be handled solo. Do you pair? Are you even allowed to pair? Or are you actively encourage to pair?

it may have escaped your notice when thinking things through, but there is a big difference between: 1. A solo task ticket 2. A pair programming ticket 3. A mob programming (teaming) ticket A solo task tends to be small, component focused, and often sliced out for an individual based on their skills. A pair ticket tends to be a story, often end-to-end, not a fractional task ticket. Having multiple skills and backgrounds involved, they are less fine-grained and more likely to be whole work. A teaming ticket tends to be a whole story or a whole feature. The work is larger, it covers more of the stack, and it works end-to-end and is deployed sooner to harvest customer value. Yes, it is stupid to have two people (or more) doing the solo developer's ticket, and it would be likewise unwise to give solo developers the whole-team ticket. If you don't consider that, then the "throughput" comparisons are not apples-to-apples and are misleading. Yes, a group of 10 individuals will do more tickets than a team or a group of pairing individuals, but they're not the same tickets and they don't have the same scatter-gather risks of late integration challenges. Make your own observations and choices, but don't base your decisions upon shakey and invalid assumptions. Do what makes sense for you, but first *make* *sense* of what you're deciding on. Don't choose foolishly.

"Pair programming takes twice the time for half the work” 🤔 I'm sure I believed a version of this at least once in my developer career. At the same time, pair programming seems like a great way to learn and catch bugs together. 🐞 There are merely my beliefs. I'm probably wrong. So I ventured into the world of scientific research to learn about the costs and benefits of pair programming. I spent a few weeks reading up on studies, cases, and reviews and compiled my learnings into an in-depth blog post. Here is what I learned: 1️⃣ Pair programming takes slightly more time than solo programming 2️⃣ Pair programming results in higher-quality code than solo programming, but not always 3️⃣ Pair programming results in more learning than solo programming 4️⃣ Other benefits of pair programming include improved team spirit, higher job satisfaction, and overall enjoyment. 💡 So what does this mean for the business case of pair programming? I think it’s reasonable to conclude that pair programming is more expensive than solo programming if we only consider person hours. Simply put, if one developer costs two person-hours for the completion of a task, paired developers cost a bit more than four hours for that same task. Results may vary case by case, but this is the broad pattern that is supported by the evidence. However, the increased costs are offset by higher quality code, more learning, and potentially increased speed in the future as developers become more familiar with pairing. And many academic studies have shown that low-quality code increases costs in the (near) future as bugs occur more easily and are harder to resolve (Khomh et. al. 2012, Li & Shatnawi, 2007, Politowski, 2020). So what can we do in practice? ✅ Focus on the improved quality and learning of pairing ✅ Encourage pairing on complex tasks ✅ Focus pairing sessions on the prevention of code smells ✅ Avoid big experience gaps in pairs ✅ Make work agreements to facilitate pairing Of course, the decision of whether or not to pair is always an individual one. No developer or team should be forced to do so. But perhaps this post can make for better, more evidence-based discussions on the merits and challenges of it. 👉 Read the full post here: https://bit.ly/4002iSf P.s. Please read the post before you comment here. There is a lot of nuance there that is hard to translate to this summary on LinkedIn.

Mob and pair programming, as collaborative development techniques, distribute cognitive load among team members, enhancing problem-solving, knowledge sharing, and code quality. Coding tasks are collectively tackled, reducing the cognitive burden on any individual. Continuous real-time code review prevents errors early, mitigates extensive debugging, and supports faster learning through direct interaction, thereby decreasing the need for solo research. This approach also limits context switching, allowing deeper focus on singular tasks and providing a support system that eases individual overload, ultimately boosting team efficiency and reducing mental strain. #CognitiveLoad #Mob #PairProgramming

Key Takeaways from Dragan Stepanović's talk below. #agiletoagility 💡 #FasterFeedback = Higher Quality: Shortening the time between writing code and discussing it drastically reduces defects and misunderstandings. Early feedback means you can fix issues before they become big problems. 💡 #OptimizeforFlow, Not Just “Developer Costs”: Having two people at one computer might look more expensive on paper, but it saves time overall by catching mistakes early, preventing rework, and speeding up the entire flow of features to users. 💡 #LongWaits Kill Momentum: Relying on asynchronous pull requests often means code sits idle waiting for review. This delay slows the whole team down and increases the risk of stale code or overlooked problems. 💡 #BigBatches → Minimal Feedback: Large pull requests (hundreds or thousands of lines) usually get a quick “Looks good!” instead of detailed, constructive feedback. Big changes are also riskier to merge and harder to understand. 💡 #SmallBatches Are Great—But Watch the Overhead: Small, frequent pull requests get better, more in-depth feedback. However, if the team is busy or uncoordinated, those small changes can still end up waiting a long time, nullifying the benefit. 💡 #ContinuousCollaboration Is a Game Changer: Techniques like pair programming or mob (ensemble) programming eliminate the waiting almost entirely. You review code in real time, ensuring immediate feedback and shared understanding. 💡 #PsychologicalSafety Builds Better Code: Working together closely (pair or mob) fosters trust and openness. When everyone sees mistakes and solutions in real time, the team learns collectively and avoids “us vs. them” mindsets. #mobprogramming