A Case Study on Continuous Software Development

In the early days of software engineering, teams followed a rigid, step-by-step “waterfall” model. Projects took years, were delivered late, and often missed the mark entirely. Development and testing were siloed. Decision-making was top-down. And change was seen as a threat.

In the 1990s and 2000s, software teams began looking elsewhere for inspiration — notably to Toyota’s lean manufacturing system. Toyota had revolutionized production with just-in-time delivery, root-cause analysis, small batch sizes, and respect for workers as problem-solvers. Software teams began asking: what would it look like if we built software this way?

The result was a new mindset — and with it, a revolution in practices: Agile development, DevOps, continuous integration, and continuous delivery.

The Transformation¶

Software teams didn’t just copy lean manufacturing. They adapted it to fit the radically different world of code. They built continuous systems that worked iteratively, in small, meaningful units, giving teams the ability to release changes at any time, with high confidence—and to keep improving.

Some of the foundational practices of continuous software development include:

Working in small batches

Avoiding long delays and bloated releases; shipping often to get feedback fast.

Continuous integration

Regularly merging code into production to remove painful reintegration,reduce defects, and increase velocity.

Test Automation

Making tests part of development, not an afterthought — enabling safe, confident, rapid change.

Continuous Delivery

Automating the deployment pipeline so that value can flow directly from idea to end user.

Information Visibility

Making requirements, blockers, progress, and metrics visible across teams.

Empowered teams

building cross functional teams, talking directly to users, identifying problems, proposing solutions, and improving the system.

The shift wasn’t just technical. It was cultural and organizational. It treated change as constant, valued learning, and viewed speed and quality not as trade-offs, but as outcomes of a healthy system.

Key Insight¶

Change is not a disruption to be managed. It’s a constant to be designed for. Continuous development didn’t just speed things up — it fundamentally reframed the goals of the system: from documentation to delivery, from control to trust, from silos to collaboration.

Inspiration for Science: Redesigning How We Create and Share Knowledge¶

Just as Agile reframed how software teams work, continuous and integrated practices in science invites us to rethink how knowledge is produced, validated, and shared. Science today often mimics waterfall: long timelines, disconnected teams in charge of deployment (publishing) vs development (the science!), infrequent releases (publications), and huge rework at the end.

What could change if science adopted a continuous mindset?

The Challenge¶

Today’s scientific system assumes that work must be polished before it is shared. But this model creates bottlenecks:

• Months (or years) of work go unseen until publication and even preprints.
• Valuable feedback comes too late to be corrective and instructive.
• Collaboration is limited to narrow, formal channels and is often adversarial.
• Scientists waste time on administrative burden (reformating, entering metadata), slow processes, and rigid expectations.
• Reproducibility suffers because the artifacts of the process — code, data, intermediate results — are missing or fragmented.

The Parallel Opportunity¶

Agile and continuous methods reshaped software by empowering teams to experiment, release, and improve in short loops. What if science worked more like that? Not copying software, but asking: what are the principles that matter most — and what new practices could support them?

Designing for continuity in science might include:

• Sharing research-in-progress to invite early feedback and foster collaboration.
• Modular publishing: Breaking monolithic papers into smaller, structured, reusable components.
• Continuous peer review: Making peer feedback an ongoing conversation, not a single checkpoint.
• Versioned datasets and methods: Treating data like code — version-controlled, testable, and reusable.
• Automating tedious tasks (like formatting or submission) so scientists can focus on science.
• Cross-functional teams: Bridging disciplines and roles — bringing statisticians, developers, domain experts, and designers into the research process early.

The Big Idea¶

Just as Agile transformed software from a fragile, delay-prone endeavor to a fast-moving, quality-centered practice, continuous practices in science could redefine the pace and impact of research — starting with a change in mindset, and reinforced by changes in systems.

In software, continuous practices emerged from a recognition that the old ways weren’t working — and from a willingness to experiment, reflect, and improve. That same opportunity is in front of us in science.

We’re not asking scientists to be developers. We’re asking: what are the practices that could support a more responsive, rigorous, and inclusive scientific system?

The transformation isn’t about doing the same things faster. It’s about redesigning the system to be more human, more scalable, and more effective — just like continuous software development did for tech.

Questions¶

• What are the practices of a more responsive, rigorous, and inclusive scientific system?
• What mindsets can we reframe with this lens?
• How can we look at our systems differently?
• What is continuous science?