The concept of Minimum Viable Product (MVP) has become a cornerstone of agile and lean development methodologies. For years, a simple yet powerful metaphor has illustrated this concept: evolving from a skateboard to a car. This analogy effectively captures the essence of iterative and incremental development. However, to truly resonate with the modern software landscape, particularly in agile coding environments, it’s time to refine this imagery. Let’s explore how the journey from a bike to a car provides an even more nuanced and relevant understanding of agile principles in “Bike To Car Coding Agile”.
This visual, depicting product evolution from basic to complex, has gained widespread recognition within the agile community. It’s been featured in numerous articles, presentations, and even books like Jeff Patton’s “User Story Mapping,” highlighting its effectiveness in explaining iterative and incremental development, lean startup principles, and the essence of MVP. Yet, like any metaphor, it’s prone to misinterpretation when detached from its original context and sometimes criticized for oversimplification. It’s crucial to remember that this is a metaphor, not a literal blueprint for car manufacturing, but a representation of product development in general.
Given the widespread use and occasional misinterpretations, it’s essential to delve deeper into the thinking behind this metaphor and how it applies, especially when considering “bike to car coding agile” strategies in software development.
The Pitfalls of Linear, Non-Agile Coding: The “Not Like This” Anti-Pattern
The top row of the image vividly illustrates a common misconception about iterative and incremental product development, often mistakenly labeled as “agile.” This approach, or rather misapplication, represents a significant anti-pattern in agile coding.
Many software projects, even today, stumble due to a “Big Bang” delivery approach. This involves extensive development until the software is deemed “100% complete” according to the initial plan, followed by a single, large delivery. The history of software development is littered with failed projects due to this very reason. While agile methodologies emerged as an alternative, the concept of delivering an “unfinished” product in iterations can be initially jarring to stakeholders. The reaction might be, “Who wants half a car? Or in coding terms, who wants half a feature?”.
Imagine presenting a client with the first iteration of a car project:
Image alt text: First iteration in the “Not Like This” approach: a car tire, representing unusable increments in software development.
“Here’s our first iteration, a front tire for your car coding project. What are your thoughts?”
The client’s likely response: “Why are you giving me a tire? I asked for a CAR! What am I supposed to do with a single tire in my coding project?”. Here, “customer” is used broadly to include product managers, product owners, and early-adopter users in a software development context.
As development progresses through iterations, the product gets closer to completion in a technical sense, but the client remains frustrated. Each delivery is still just a partial, unusable car – or in coding, a set of disconnected code modules.
Finally, when the project is declared “complete” and the full car is delivered, the client’s reaction might be a relieved, “Finally! Thanks! Why couldn’t you just deliver this in the first place and skip all the useless intermediate deliveries?”.
Image alt text: Final iteration in the “Not Like This” approach: a complete car, delivered after a long delay and without user feedback, likely missing user needs.
In this idealized scenario, the client is eventually satisfied because they received what they initially ordered. However, in reality, this linear approach often leads to dissatisfaction even at the end. Significant time has passed without real user testing or feedback integration. Consequently, the final product is often riddled with design flaws and based on incorrect assumptions about user needs in the evolving software landscape. The smiley face at the end of this sequence is therefore overly optimistic.
This “Not Like This” approach represents a “bastardized agile” methodology. While it may technically involve incremental delivery, the critical feedback loop inherent to true agility is absent. This lack of feedback makes it exceptionally risky and fundamentally non-agile, especially in the dynamic world of software coding.
Embracing Agile Iteration: The “Like This” Bike-to-Car Coding Approach
Now, let’s examine the second row of the image, which embodies a truly agile and iterative approach to product development, and more specifically, “bike to car coding agile.”
This approach begins with the same initial context: the client requests a car. However, instead of solely focusing on building a car directly, we shift our focus to the underlying need the client wants to fulfill. It turns out the core requirement is “I need to get from point A to point B faster.” A car is just one potential solution to this problem. Remember, the car is a metaphor; in software development, this could represent any customized software solution.
The agile team’s first step is to deliver the smallest possible increment that allows the client to start testing and providing feedback. This is often referred to as an MVP (Minimum Viable Product), but as we’ll discuss later, terms like Earliest Testable Product might be more descriptive.
Image alt text: First iteration in the “Like This” approach: a skateboard, representing an early testable product that addresses the core need of mobility.
This initial deliverable, let’s call it the “skateboard version” in line with the metaphor, is unlikely to fully satisfy the client. It’s far from the car they initially envisioned. But that’s perfectly acceptable at this stage. The crucial point is: we are not aiming for immediate client delight in this initial iteration. While some early adopters or pragmatists might find some value, the primary objective at this stage is to learn. Transparent communication with the client about this learning-focused approach is essential to manage expectations.
Unlike the isolated front tire in the previous scenario, the skateboard is a functional product. It allows the client to move from A to B, albeit imperfectly. We communicate to the client: “This is just the first iteration. We’re still aiming for a car-like solution, but in the meantime, please use this skateboard and give us your feedback.” This embodies the principle of thinking big while delivering small, functionally viable increments in agile coding.
Imagine the feedback loop. The client might say, “I dislike the skateboard; I hate the color.” We inquire further, “Just the color?”. The client responds, “Yes, make it blue! Otherwise, it works fine!”. By deploying this early “skateboard,” we’ve potentially saved significant resources by avoiding building a full car based on potentially flawed initial assumptions. While this color-centric feedback is simplified for illustration, it highlights the value of early feedback in agile coding.
The key question in this agile approach is: “What is the quickest and most cost-effective way to start learning?”. Could we deliver something even earlier than a skateboard? Perhaps a bus ticket?
Will a bus ticket solve the client’s need? Perhaps partially, or perhaps not at all in the desired way. But by putting it in the hands of the user (even conceptually), we gain valuable learning. Lean Startup methodologies emphasize formulating hypotheses about users and systematically validating or invalidating them through such early interactions.
Testing doesn’t necessitate a fully built product or a large user base. Even testing a prototype or a conceptual solution with a single user can yield significantly more insights than proceeding without any user feedback.
Returning to the skateboard example, after initial use, the client might feedback, “The skateboard is fun for short distances, but unstable, and I fall off easily.”
In the next iteration, the agile coding team addresses this feedback, focusing on stability.
Image alt text: Second iteration in the “Like This” approach: a scooter, improving stability and usability based on user feedback from the skateboard iteration.
Now, the client can navigate more reliably without falling. Are they fully satisfied? Not yet; the desire for a car remains. However, they are actively using the evolving product and providing continuous feedback. Their primary complaint might now be the limited range and speed for longer distances, like traveling between buildings, due to small wheels and no brakes. The next iteration then logically evolves into a bicycle.
Image alt text: Third iteration in the “Like This” approach: a bicycle, further improving range and speed, addressing limitations identified with the scooter.
With the bicycle, the client can now efficiently travel across the campus. Through this iterative process, valuable insights emerge: the client appreciates open-air travel, and the primary transportation need is campus-wide mobility.
The bicycle might actually prove to be a superior solution to the originally envisioned car. User testing may reveal that campus paths are too narrow for cars anyway. This agile “bike to car coding agile” approach has potentially saved significant time and resources while delivering a more suitable solution faster.
One might argue, “Shouldn’t thorough upfront analysis have revealed these needs?”. Valid point. However, in real-world software development, extensive upfront analysis, while valuable, often falls short of predicting actual user behavior and needs once a tangible product is in their hands. No matter how detailed the initial analysis, user interaction with even a basic release inevitably reveals surprises and invalidates some initial assumptions.
Therefore, upfront analysis is essential, but it should be balanced with rapid prototyping and iterative releases. Don’t over-invest in analysis at the expense of early user feedback. Real learning occurs through user interaction with working software.
Continuing the story, if the client’s needs evolve to include inter-city travel, a bicycle is insufficient. The next iteration might introduce an engine, transforming the bicycle into a motorcycle.
Image alt text: Fourth iteration in the “Like This” approach: a motorcycle, adding an engine for longer distances while retaining the open-air experience, evolving based on user needs.
This iterative model is particularly well-suited for software due to its inherent flexibility. Software can be “morphed” and adapted more readily than hardware, reducing the need for complete rebuilds with each iteration. However, even in hardware development, prototyping and user observation are invaluable. While hardware iterations may be longer (months instead of weeks), the principle of early feedback remains crucial. Even automotive giants like Toyota and Tesla heavily rely on prototyping (sketches, 3D models, clay models) before full-scale car development.
At this stage, the motorcycle might fully satisfy the client’s evolving needs. The project could potentially conclude earlier than initially planned. Many software products are burdened with features that are rarely or never used. Agile iteration helps identify and eliminate unnecessary complexity, focusing on the simplest, most efficient solution to the core problem – minimizing the path to delivering value.
Alternatively, the client might choose to continue iterating, potentially with revised requirements based on their experience with the motorcycle. Ultimately, the project might indeed culminate in a car, perhaps resembling the initial vision. However, it’s more probable that the iterative journey yields a slightly different, and often better, outcome.
Image alt text: Final iteration in the “Like This” approach: a convertible car, representing a refined solution that incorporates user feedback and delivers more value than the original plan.
The client is now delighted with a convertible. Why? Because the iterative process revealed their appreciation for open air. They received a car, but a car better suited to their evolved needs than the originally conceived closed-roof model. This “bike to car coding agile” journey exemplifies how iterative development can lead to superior outcomes.
Defining Your “Skateboard” in Agile Coding Projects
The “Not Like This” scenario, delivering unusable increments like a single tire, fails because it lacks user feedback and delivers no immediate value. If a project is simple, low-risk, and builds upon well-established patterns, a Big Bang approach might be acceptable.
However, most software development, especially in “bike to car coding agile” contexts involving innovation, is inherently complex and risky. The Big Bang approach in such scenarios often leads to costly failures. The critical question then becomes: “What is your skateboard equivalent in this coding project?”.
In product development, particularly in agile coding, one of the initial key steps after defining the problem and target user is to identify the “skateboard” – the Earliest Testable Product. The skateboard metaphor represents the smallest, functional increment that can be placed in the hands of real users to gather actionable feedback. Or, if more appropriate to the context, consider the “bus ticket” as an even more minimal starting point for validation.
This early deliverable establishes a vital feedback loop, granting both the development team and the client control over the project’s direction. It enables learning and adaptation, replacing rigid adherence to an initial plan with iterative refinement based on real-world user interaction, which is the heart of “bike to car coding agile.”
Let’s examine real-world examples to solidify this concept in practice, particularly within the realm of software and technology.
Case Study 1: Spotify’s Music Streaming Revolution – From Prototype to Platform
“With over 75 million users, it’s hard to remember a time without Spotify. But there was. A time when we were all mulling the aisles of Target for new CDs. A time in our lives where we all became thieves on Napster. A time when iTunes forced us to buy songs for $2/piece. And then came Spotify.” – TechCrunch
Spotify, now a ubiquitous music streaming platform, began as a startup in 2006, built upon several key assumptions: user willingness to stream music instead of owning it, music industry acceptance of legal streaming, and the technical feasibility of fast and stable streaming. Remember, in 2006, music streaming was often plagued by buffering issues, and digital music piracy was widespread. The core technical challenge was: “Can we create a client that streams music instantly upon pressing ‘Play,’ eliminating the frustrating ‘Buffering’ progress bar?”.
Thinking Big, Starting Small: Spotify’s founders had a grand vision, captured in early sketches of a comprehensive music platform.
However, instead of years spent building the entire envisioned platform upfront, the developers focused on a core technical prototype. They loaded ripped music onto laptops and experimented intensely to optimize playback speed and stability. The key metric was latency: “How quickly does music play after pressing ‘Play’?”. The goal was near-instantaneous playback without interruptions.
“We spent an insane amount of time focusing on latency, when no one cared, because we were hell bent on making it feel like you had all the world’s music on your hard drive. Obsessing over small details can sometimes make all the difference. That’s what I believe is the biggest misunderstanding about the minimum viable product concept. That is the V in the MVP.” – Daniel Ek, co-founder and CEO
Once a functional prototype was achieved, internal testing began, expanding to family and friends.
This initial version was far from release-ready. It lacked polish, features beyond basic playback, legal agreements, and a monetization model. It was their “skateboard” – an Earliest Testable Product in the “bike to car coding agile” journey of Spotify.
But they boldly put this “skateboard” in the hands of real users – their inner circle – and quickly gained crucial validation. The technical feasibility of instant streaming was confirmed, and user enthusiasm for the concept was overwhelming. These validated hypotheses proved instrumental in securing music industry agreements and investor funding, paving the way for Spotify’s global success.
Case Study 2: Minecraft’s Block-by-Block Rise – Iteration in Game Development
Minecraft stands as a monumental success in game development, particularly considering its minimal initial development cost. It’s a prime example of “release early and often” in action. The first public release occurred after just six days of coding by Markus Persson – a truly minimal viable product. This initial version was rudimentary: a blocky 3D landscape allowing users to dig and place blocks to build simple structures.
This was Minecraft’s “skateboard.” User engagement was immediate and intense, with developer-user communication largely happening on Twitter, fostering a direct feedback loop. Early users, including the author’s children, were captivated. Over a hundred releases followed in the first year alone. Game development hinges on “finding the fun,” and early user interaction was paramount. Thousands of users who purchased early access had a vested interest in contributing to the game’s improvement.
A small development team gradually formed around Minecraft (initially just two developers), and the game became a global phenomenon. Minecraft’s iterative development, driven by constant user feedback, ultimately led to its acquisition by Microsoft for $2.5 billion, demonstrating the power of agile principles in even seemingly unconventional domains like game development.
Case Study 3: Swedish Police PUST Project – Agile in Government Software
Around 2010, the Swedish Police initiated PUST (Polisens Utrednings STöd) to enhance field operations by providing real-time system access via laptops in patrol cars – a crucial step in modernizing law enforcement technology.
Previous attempts to build similar systems using Big Bang approaches had failed dramatically, with one project taking seven years to reach initial release, rendering it obsolete upon deployment. PUST aimed for a different approach.
The 60-person “PUST Java” project achieved surprising success, even earning recognition as “Project of the Year.” A key factor was avoiding monolithic development. The project was strategically divided along two axes:
- Region: Phased rollout, starting with a single region instead of nationwide deployment.
- Crime Type: Initial focus on 1-2 crime types, gradually expanding coverage.
Version 1.0 was their “skateboard.” It was a limited system supporting a few crime types, field-tested by a small group of officers in Östergötland. These officers were aware of the experimental nature of the system but were eager to participate, having experienced the failures of previous waterfall projects. They finally had a chance to shape a system during development.
Feedback was direct and honest, often challenging initial assumptions and even pre-defined Use Case specifications – a significant shift for an organization accustomed to extensive upfront analysis.
This early feedback drove continuous product improvements. As officers in Östergötland embraced the system, more crime types and regions were incrementally added. By the time Version 1.4, the nationwide rollout for 12,000 officers, was reached, confidence was high due to extensive user testing and iterative refinement.
However, a subsequent project, “PUST Siebel,” unfortunately reverted to waterfall practices, abandoning agile principles. Two years of analysis and development without user releases culminated in a Big Bang nationwide deployment of the “next-generation” system. It was a disaster, failing within months and costing Swedish society an estimated €1 Billion, a stark reminder of the risks of abandoning agile iteration and user feedback.
Case Study 4: Lego’s Playful Prototyping – Agile in Toy Design
Working at Lego, the author observes a consistent pattern of successful product development driven by prototyping and early user testing. Kids are frequently seen in Lego’s offices, and designers collaborate with kindergartens, schools, and families to test new product ideas.
Image alt text: Lego Nexo Knights product line, illustrating Lego’s iterative design process incorporating user feedback from children throughout development.
The Nexo Knights product line exemplifies this approach. Initial paper prototypes tested with children revealed a critical issue: difficulty distinguishing between “good” and “bad” characters. Designers iterated based on this feedback, continuously testing until designs resonated with children. Imagine the consequences if Lego had mass-produced based on initial assumptions without this early user feedback.
However, Lego also experienced failures when deviating from this user-centric, iterative approach. Lego Universe, a massively multiplayer online Lego world, suffered from overambition and a perfectionist approach, delaying user feedback.
Around 250 people worked for 4-5 years on Lego Universe, driven by scope creep and a pursuit of perfection. Despite using Scrum internally, releases remained internal, lacking real user feedback. The finished game, while visually impressive, lacked expected “fun” and was shut down after two years.
Lego Universe lacked a “skateboard” for external user testing. Lego’s guiding principle, “Only the best is good enough,” while generally beneficial, was misapplied in this case. The pursuit of perfection delayed vital user feedback, leading to flawed assumptions about user preferences. This contrasted sharply with Minecraft’s user-driven iterative approach.
While Lego Universe teams used Scrum and iterative development internally, the lack of external user feedback proved detrimental. Lego learned from this expensive failure and continues to refine its early testing and user feedback processes, reinforcing the importance of agile principles even in creative domains like toy design.
Beyond MVP: Earliest Testable, Usable, and Lovable Products
The term MVP, while widely used, often leads to confusion. Clients may perceive “Minimum Viable Product” as “Minimum Releasable Crap,” fearing a buggy, unfinished product and project abandonment after the initial release.
While this perception often stems from poor management rather than the term itself, “MVP” can be ambiguous. “Minimum” and “Viable” are subjective and open to misinterpretation.
A more explicit alternative framework replaces “Minimum Viable Product” with a progression of “Earliest” releases:
First, replace “Minimum” with “Earliest.” The core goal of early releases is to gain early feedback. “Early” is generally more appealing to clients than “minimum.”
Minimum => Earliest
Second, replace “Viable,” which is too vague, with more specific stages of product evolution: Testable, Usable, and Lovable.
Earliest Testable Product (ETP): Like the skateboard or bus ticket, this is the initial release for feedback gathering. It enables users to interact with a basic version and provide initial reactions. Learning is the primary goal, with user value as a secondary bonus.
Image alt text: Earliest Testable Product visualized as a skateboard, emphasizing its role in early feedback and learning in agile development.
Earliest Usable Product (EUP): Represented by the bicycle, this is the first release that early adopters willingly use. It’s not feature-complete or polished, but it offers tangible value and improvement over existing solutions.
Image alt text: Earliest Usable Product visualized as a bicycle, highlighting its usability and value for early adopters in agile iteration.
Earliest Lovable Product (ELP): Like the motorcycle, this is the first release that users genuinely love, recommend to others, and are willing to pay for. Further improvements are still possible, but it’s a marketable product with strong user appeal.
Image alt text: Earliest Lovable Product visualized as a motorcycle, representing a marketable product with strong user appeal and a solid foundation for further iteration.
While “Earliest Testable/Usable/Lovable” can still be misinterpreted, it provides greater clarity than the ambiguous MVP. An even earlier stage, “Earliest Feedbackable Product,” could represent paper prototypes or conceptual mockups used for initial feedback, but the three-stage model offers a practical and understandable framework.
Key Takeaways for Agile Coding Success
In conclusion, to maximize success in agile coding and product development:
- Embrace Iteration, Reject Big Bang: For complex, innovative projects, avoid Big Bang delivery. Adopt iterative and incremental development. This is a fundamental agile principle, but consistent implementation is key.
- Identify Your “Skateboard” (ETP): Define the Earliest Testable Product for your project. Start with a minimal, functional increment to initiate the feedback loop.
- Consider ETP/EUP/ELP over MVP: Use more explicit terminology like Earliest Testable, Usable, and Lovable Product to clarify iterative stages and manage expectations.
Remember, the bike-to-car metaphor is a guide, not a rigid formula. Adapt these principles to your specific context. By embracing iterative development and prioritizing early user feedback, especially in “bike to car coding agile” projects, you significantly increase the likelihood of delivering successful and valuable software solutions.
