Engineering Principles for Quality Software Delivery

By Richard Brown, Technical Director at Audacia.

Engineering principles are often written down, printed out, and quickly forgotten. To be effective, they need to be a working framework - something that actively guides decisions, structures conversations with teams and stakeholders, and ultimately shapes the quality of the software you deliver.

At Audacia, we use eight engineering principles in delivery. These principles provide a practical way for our engineering teams, and the wider business, to consistently deliver high-quality, maintainable, performant, and secure systems.

I did a talk previously discussing engineering principles, which we have put together here as a written version for people to refer back to. Following my talk, this will explain why we use engineering principles, how they help with trade-offs, long-term thinking, and stakeholder alignment, and how we embed them in the day-to-day running of projects.

Why Use Engineering Principles?

A Framework for Guiding Decisions

In any software project, countless technical decisions must be made throughout the lifecycle of the system. Most of these decisions involve trade-offs rather than clear-cut answers. Improving one aspect of the system often comes at a cost elsewhere. For example, enhancing performance might reduce code readability, or building in higher quality might require more development time.

This is where engineering principles provide value. Our eight principles give engineers a structured way to reason through these trade-offs. They help teams weigh what they stand to gain or lose with each decision, and ensure those decisions align with broader goals for quality, maintainability, and user experience.

A High-Level Counterpart to Coding Standards

Coding standards play an important role in software delivery. They help ensure readability and consistency within a codebase - things like naming conventions and formatting rules matter, especially as a codebase grows. But no project ever succeeded or failed because of the casing of a method name.

If we focus only on low-level standards, we risk missing the bigger picture. This is where engineering principles come in. They serve as the high-level counterparts to coding standards, helping to guide the decisions that genuinely influence project success or failure.

Maintaining Long-Term Focus

It’s easy to get lost in the day-to-day of a project - the next sprint, the next feature. Engineering principles force us to lift our heads up and think about the long-term health of the system.

Are we building something that will still work well in one, two, or five years? Are we making it easy to maintain and extend?

Principles help to provide a structure for that thinking, and help teams add that long-term focus.

Measuring Technical Health

Project metrics often focus on budget, time, and immediate customer satisfaction. These are important, but they don’t tell you if the system is technically healthy.

Our engineering principles give us a way to measure this. We regularly score projects against each principle. This gives us objective data on where we’re strong, and where we need to improve.

Identifying Skills Gaps

Measuring technical health across our principles also helps us identify skills gaps in a structured way.

Take user experience as an example: a key part of this is ensuring accessibility.

Even with the best intentions, if a team lacks the skills to implement and test accessibility effectively, progress will stall. Tracking project scores makes this visible. If user experience consistently scores low, it may reflect a need to strengthen front-end or accessibility expertise - either through hiring, team rotation, or targeted training.

Not Just for Engineering

Beyond the core engineering team, these principles must be understood across the wider delivery team and stakeholders.

Product owners, business analysts, scrum masters, all need to be aware that investing in accessibility, performance, or observability may add delivery complexity.

Similarly, discussing principles with clients early ensures we can frame the right conversations. For example, gathering data on expected performance loads or browser support requirements. If principles aren’t considered during scoping, the resulting backlog may be unrealistic or incomplete. By embedding them throughout the project lifecycle, we avoid this disconnect and build systems that truly meet user and business needs.

Eight Engineering Principles

1. Deliver Value Frequently and Reliably

A feature has no value until it's in the hands of users. This principle is about removing the friction between writing code and delivering it. It’s not enough to commit code to a repository - it only matters once it’s live in a user-facing environment. That means optimising every step between idea and impact.

Automated build and deployment pipelines

Your delivery speed is only as fast as your slowest process. If it takes three hours for a build to complete, you're already behind. Fast, automated pipelines are essential. They help you get features to customers quickly and safely, without bottlenecks or delays.

Code reviews

Code reviews should be both efficient and effective. A good pull request process doesn’t block delivery, and it doesn’t become a formality either. If it takes a week to review code that took an hour to write, you’re not delivering value. Make reviews a priority—treat them as a way to share knowledge and catch real issues early.

Infrastructure as code

Spinning up environments manually is slow and error-prone. Using tools like Terraform or Bicep allows you to quickly create reliable, repeatable environments. This helps testers and clients see progress faster, without the cost of always-on environments.

Reversible, reproducible deployments

Moving fast is only safe if you can reverse changes easily. Big tech companies move quickly not because they ignore risk, but because they have systems in place to handle failure. Reproducible, one-click rollback is critical. If something breaks in production, you should be able to back it out in minutes and investigate without pressure.

Representative test environments

Bugs are part of software development, but they shouldn’t be surprises. If an issue only appears in production, your test environments aren’t realistic enough. Creating environments that accurately reflect real-world use helps you catch problems early and deliver with confidence.

2. Care About Quality

Quality isn’t just about writing clean code. It’s about building systems that are easy to change, reliable under load, and fit for purpose - not just today, but in the future.

Architect for change

Architectures should enable change, not block it. Systems need to evolve as requirements change. If architecture is too rigid, every new feature will become painful to implement.

Automate standards enforcement

Automating the basics such as naming conventions and formatting, using tools like linters and static analysis frees up code reviews to focus on more important issues, like architecture and logic.

Maintain a test strategy

Every project starts with a test strategy and that strategy evolves as the project does. A test strategy should cover API testing, UI testing where needed, exploratory testing, performance and security testing. The closer actual delivery aligns with this strategy, the better the project’s quality.

Track and manage technical debt

Technical debt is normal. What matters is managing it actively. Doing things like raising backlog items, and dedicating time in sprints to addressing debt, can stop it from quietly growing out of control.

Document architectural decisions.

Document why decisions were made, not just what was done. If you come back to a project two years later, knowing why a framework or approach was chosen is far more valuable than knowing which one was picked.

3. Write Documentation

Good documentation is about capturing the ‘why’, not just the ‘what’. It should give future engineers enough context to understand why certain decisions were made—especially when those decisions were trade-offs shaped by constraints the team faced at the time.

Systems architectures documented

System architectures should always be documented, ideally using the clear visuals provided by an architecture diagram. This means more than simply recording what frameworks or tools are in use. What matters is recording why the architecture is the way it is, and what factors influenced that design.

Technical documentation accessible and up to date

Documentation needs to be kept current and easy to access. Out-of-date or hard-to-find documentation isn’t helpful. Teams should be able to refer to technical documents and quickly understand the system’s design and intent.

Test cases document business logic

Tests don’t just validate that code works, they also document the business logic behind features. Well-structured test cases provide an extra layer of documentation, showing how the system is expected to behave and why.

Architecture Decision Records document key technical decisions

Architectural Decision Records (ADRs) are one of the most valuable forms of documentation we use. They capture why key technical decisions were made, with not just what was chosen, but why. Without this context, future engineers can easily second-guess decisions, or assume they were made arbitrarily. Documenting the reasoning helps avoid that. Often the decision made was the best option under the constraints at the time, whether those were time pressures, lack of knowledge, or other factors. Recording this helps future teams approach these decisions objectively and avoid wasting time or making incorrect assumptions.

4. Build Observable Systems

As systems become more complex, using microservices, distributed architectures and cloud platforms, observability becomes critical. You need to know what’s happening in your environment at any point in time, and be able to diagnose issues quickly and confidently.

Meaningful logging

Logging is essential, but more logging isn’t always better. If you can’t diagnose an issue from your logs, your observability isn’t working. Too little logging leaves you blind; too much makes it impossible to see what matters. Getting the right amount of useful logging is key. And this is something you need to start from the very beginning of a project, not bolt on at a later point.

Monitoring and alerting

Monitoring and alerting need to be designed to help you detect issues and act on them fast. It’s not enough to know that something is broken, you need to know where and why. This is especially important when things go wrong in a test pipeline. If a test that’s been passing for weeks suddenly fails, your logs and alerts should give you enough information to quickly understand the cause.

Engage support teams

Support teams need to be involved early in deciding what will be logged and what will trigger alerts. They’re the ones who will be using these systems once the software is live. The goal is to give them actionable, useful insights, rather than noise.

Monitor cloud hosting costs

The cloud is powerful and flexible, but with that can come risk. It’s very easy to spin up new environments or scale out services, and just as easy to forget they’re running. Without proper monitoring and alerting on hosting costs, you can quickly rack up unexpected bills. Observability isn’t just about software performance, it’s also about understanding the overall health and efficiency of your hosting environment.

5. Write Automated Tests

Automated testing is essential to delivering software at speed and with confidence. It’s a core part of how we work, not an afterthought, and not the responsibility of one role.

All engineers write automated tests

Everyone involved in building software is responsible for automated testing. Developers write unit tests and integration tests. Test engineers contribute to UI and API automation. Automation must run throughout the test pyramid, not just in isolated parts of the system.

Automated tests provide frequent feedback

Automated tests give us rapid feedback on the health of our system. They must be in place early and continuously maintained. The goal is to detect issues quickly, not at the end of a manual testing cycle. If it takes a week to manually verify a release, you’ll never deliver at the pace customers expect.

Failing tests are addressed immediately

Failing tests are treated as top priority. Ignoring failed tests undermines the value of the entire test suite. A failing test is a sign of risk and is one that needs immediate investigation and resolution.

Tests catch regression bugs

The automated regression pack is key to maintaining confidence in existing functionality. Without it, regression testing becomes slow and unreliable. Automated tests ensure that as we deliver new features, we aren’t breaking what already works, giving teams and customers confidence with every release.

6. Care About User Experience

Delivering a great user experience starts with understanding who will use the system, in what context, and on what devices. All engineers need to build and test with this real-world understanding in mind.

All engineers understand system users

It’s essential for every engineer to know how end users will interact with the system. What devices will they use? What network conditions will they face? What constraints will they experience? Testing on high-spec laptops in perfect conditions doesn’t reflect the reality for many users. Teams must test under realistic constraints to surface real issues.

User experience is a central consideration

User experience isn’t an add-on or a nice-to-have. It’s a core consideration in how we design, build, and test systems. Every decision, from architecture to UI, should take into account the impact on the user experience.

UI components are consistent in look and feel

Consistency across the UI is critical. Users shouldn’t have to relearn the interface as they move through the system. UI components should behave consistently and follow established patterns, so users feel comfortable and in control.

Accessibility is always considered

Accessibility is always on the agenda. Whether or not it’s a formal requirement, we discuss it with clients early in the project. Building in accessibility benefits all users, not just those with specific needs. It’s about creating systems that are inclusive, robust, and usable for everyone.

7. Build Secure and Available Systems

Security and availability should always be built into the system from the start and maintained throughout its lifetime. Security and availability is definitely not something to be bolted on late in the project or revisited only after an incident.

Security scans performed and actioned

Security scans and penetration tests are essential. Regular scanning helps us identify vulnerabilities early before attackers do. Just running scans isn’t enough - identified issues must be prioritised and addressed in a timely and structured way.

Dependencies monitored and vulnerabilities patched

Third-party dependencies are a common source of vulnerabilities. We actively monitor them and patch known issues as soon as possible. Supply chain attacks are on the rise, with recent high-profile incidents showing how dangerous outdated or hijacked dependencies can be.

Major platforms on supported versions

Keeping major platforms, such as .NET, on supported versions is critical. Older versions not only miss out on performance improvements, they also expose the system to unpatched security vulnerabilities.

Data automatically backed up and restorable

Reliable data backup and restore capabilities are essential when building large, complex systems. Systems should be designed so that data is automatically backed up and can be restored quickly and confidently in the event of a failure or incident.

Documented business continuity and disaster recovery plans

Having clear, documented business continuity and disaster recovery (BC/DR) plans is part of building future-proof systems These plans ensure that the team knows how to recover the system and restore service if the unexpected happens.

8. Build Performant and Scalable Systems

Performance and scalability are not things to think about after launch. They must be considered from the start and throughout the lifetime of the system.

Architect scalable systems

Scalability begins with architecture. Systems should be designed to scale as demand grows, not just to meet initial requirements. We architect with flexibility, so that when user numbers or data volumes increase, the system can handle them without rework or performance degradation.

Understand the performance of the system under load

We need to know how the system behaves under load - not just today, but based on realistic future expectations. It’s not enough to test for today’s traffic levels if we know that usage is expected to grow substantially. Understanding and planning for this growth is key to long-term success.

Performance testing with realistic data volumes

Performance testing must reflect real-world scenarios. It’s not enough to test with small data sets and a few users. Testing should be focused on using realistic data volumes and anticipated concurrent usage, based on how the system is expected to grow in production over time.

Database maintenance tasks performed regularly

Databases require ongoing maintenance. Without it, performance will degrade as data volumes grow. Regular tasks such as re-indexing and archiving are essential to keeping the system performant. This maintenance is a key part of running any production system at scale.

Putting Principles into Practice

Engineering principles become valuable when they are used in real day-to-day project work.

At Audacia, we make them an active part of how we run projects, structure reviews, and measure technical health. They give teams a shared language and clear focal points for self-reflection, improvement, and decision making:

Structuring Technical Reviews

We run regular technical reviews for each project, led by the project’s technical and test leads, with peer review from leads on other projects. These reviews are structured around the eight principles, ensuring consistency and shared focus across teams.

Measuring Technical Health

We ask project teams to self-score against each principle (on a scale of 1 to 5). Peer reviewers validate these scores. This isn’t about judgement, it’s about self-reflection and continuous improvement. Leadership can also use this information to track trends across projects and teams.

Aligning Technical Standards

We map our technical standards to the eight principles. This helps us spot gaps. For example, if observability is a principle, do we have clear standards for logging and monitoring? If not, we create them.

Identifying Skills Gaps

By tracking scores across projects, we can identify organisational skills gaps. If performance scores are consistently low, we may need more expertise in performance engineering or testing.

Considering Project Scoping

It’s vital to consider engineering principles during project scoping. If stakeholders don’t understand the implications of, say, prioritising accessibility or performance, they won’t budget or plan for them. Having these conversations early ensures alignment and realistic expectations.

Final Thoughts

Engineering principles become effective as living tools, rather than static documents. They can inform how your teams work, how you structure projects, and how you engage with stakeholders.

Used well, they help you:

• Make better technical decisions.
• Maintain focus on long-term quality.
• Measure and improve technical health.
• Align teams around shared goals.

At Audacia, our eight principles continue to evolve as we learn. They provide a common language and framework that helps us build better software, together.