In the world of software development, we've grown accustomed to rapid iteration. New emojis appear on our phones with startling regularity, each one vetted through a sophisticated process of continuous integration and delivery. Yet when it comes to the engines that power our cars, rockets, and industrial machinery, we're often stuck in a testing paradigm that feels positively archaic.
This disparity isn't just an academic concern. It has real-world consequences that can be measured in billions of dollars and, sometimes, human lives. Consider the stark contrast between NASA's Space Launch System (SLS) and SpaceX's Starship. The SLS, intended to return astronauts to the moon, has been in development since 2011. After more than a decade and over $23 billion spent on development, it successfully completed its first uncrewed test flight, Artemis I, on November 16, 2022.
Meanwhile, SpaceX's Starship has demonstrated remarkable progress through rapid iteration in a fraction of that time. SpaceX began serious development of Starship around 2016, and by 2024, it had already completed four major test flights. After its third test flight on March 14, 2024, SpaceX quickly analyzed the data and implemented improvements. Less than three months later, on June 6, 2024, Starship completed its fourth test flight, achieving a set of ambitious goals including the first successful return from space.
This rapid cycle of test, analyze, improve, and retest showcases SpaceX's agile approach to rocket development, allowing them to make significant advancements in a matter of months rather than years.
This rapid cycle of test, analyze, improve, and retest showcases SpaceX's agile approach to rocket development, allowing them to make significant advancements in a matter of months rather than years. What accounts for this difference? It's not that SpaceX is reckless or cutting corners. Rather, they've brought the principles of modern software development—continuous integration, continuous delivery, and crucially, continuous testing—to the world of hardware.
Testing-like-you-fly
This approach, often referred to as "testing like you fly," represents a paradigm shift in hardware development. But what does it mean in practice, and why is it so powerful?
Traditionally, hardware development follows a linear path. Engineers spend months writing code, then run a test, followed by weeks of data review. This process is so time-consuming that teams often skip releases or, worse, test in production. It's as if we're building a car by designing the entire vehicle on paper, constructing it in full, and only then taking it for a test drive.
The 'testing like you fly' philosophy, transforms this process. It's not just about frequent, incremental changes—though that's part of it. The real breakthrough is using the same data review tool across all stages: from simulation to hardware testing to live operations. This consistency is crucial. By training the data review tool on real-world flight data, engineers can leverage that deep, operational knowledge even in early simulations.
The real breakthrough is using the same data review tool across all stages: from simulation to hardware testing to live operations.
It's a virtuous cycle: each stage of development informs and improves the others, creating a seamless continuum of testing and refinement. This approach doesn't just enable agile engineering; it fundamentally enhances the fidelity and relevance of every test, at every stage.
The benefits
This approach yields two levels of benefits. The first-order effects are straightforward and easily quantifiable: time savings. Automated data review can shave weeks off each development cycle. But it's the second-order effects that truly revolutionize the development process.
When testing becomes easy and routine, engineers start doing it more often and earlier in the development process. They use the same tools and processes across all stages of development, from pure simulation to actual flight. This consistency creates a virtuous cycle: more testing leads to better data, which in turn enables more accurate simulations and more effective testing. Moreover, this approach allows for the development of more sophisticated testing regimes. Simple threshold-based rules give way to machine learning models that can detect subtle anomalies.
The implications of this shift are profound. It allows companies to iterate and scale quickly without sacrificing safety or reliability. It enables them to tackle more complex challenges, like autonomous systems, with confidence. And perhaps most importantly, it dramatically reduces the risk of catastrophic failures in the field.
But implementing this approach isn't trivial. It requires a fundamental rethinking of development processes and a significant upfront investment in tools and infrastructure.
Sift’s role
This is where Sift comes in. We've built a platform that enables the "test like you fly" philosophy, bringing the agility of modern software development to the world of hardware. By automating data review and providing a unified environment for testing across all stages of development, Sift dramatically reduces the time and effort required for each iteration cycle.
Sift doesn't just facilitate testing—it turns testing into a strategic asset, enabling companies to innovate faster, scale more efficiently, and build more reliable products.
Sift isn’t just a tool for catching errors, but a repository of institutional knowledge, constantly learning and improving. This allows for increasingly sophisticated analysis, from simple threshold checks to machine learning models that can detect subtle anomalies. In essence, Sift doesn't just facilitate testing—it turns testing into a strategic asset, enabling companies to innovate faster, scale more efficiently, and build more reliable products.
The vast potential
The potential of this approach extends far beyond rockets and spacecraft. Imagine cars receiving updates as seamlessly as our phones, constantly improving in performance and safety. Consider how more efficient development processes could accelerate our transition to sustainable energy.
As our machines grow exponentially complex, so must our ability to test and refine them. The pioneers who bring the agility of modern software development to hardware will lead us into the future. They'll build the spacecraft for Mars, the vehicles reshaping our cities, and the systems powering a sustainable world.
The future belongs to those who can bridge the gap between our current practices and this ideal. It requires new tools, certainly, but more crucially, a shift in mindset. We must view hardware and software not as separate entities, but as deeply interconnected systems evolving in tandem. This transition demands courage: the willingness to upend established processes, invest in new capabilities, and embrace constant iteration. But the rewards—in innovation, efficiency, and safety—are immeasurable.
