Testing Strategies: Reconsidering the Unit and Overcoming Resistance to Change

Testing is a key part of software development. It ensures that code changes are safe, reduces mean time to resolution (MTTR), and improves the developer experience. However, for faster and more efficient delivery, it’s important to pay attention to testing strategies and how they can be optimized.

The Importance of a Good Testing Strategy.

Ensuring Safe Code Changes

Testing provides the confidence necessary to make changes without fear of introducing bugs that could disrupt a system. The lack of testing can lead to severe consequences, such as data loss, system failures, or user privacy breaches.

Delivering fast and reducing MTTR

Testing helps reduce MTTR, which is the average time required to repair an issue. By catching bugs early, testing enables developers to fix problems before they escalate, thus improving system performance and user satisfaction.

Improving the Developer Experience

Testing improves the developer experience by reducing stress and increasing productivity. With effective testing practices, developers can focus on creating new features rather than having to spend time fixing bugs.

Especially important for iterative development, uncertainty, or changing requirements

Good testing strategies are particularly important for teams that develop in iterations, work in environments with high uncertainty, or have frequently changing requirements. Without testing, these teams may struggle to maintain system reliability and performance.

Redefining the Unit for More Efficient Testing

Traditionally, the “unit” for testing has been defined as a “class or method”. However, this approach may not be optimal for testing increasingly complex systems. Testing small units of functionality or small modules may be a more efficient way to test since they reduce the time spent implementing changes.

Moving towards small units of functionality

There is a shift towards smaller units of functionality in testing, which is often referred to as “microtesting.” Using smaller units can help developers track down bugs more quickly, which reduces the time it takes to fix issues.

Reducing the Time Required for Implementing Changes

Small units of functionality also reduce the time it takes to implement changes, which can help improve delivery times. As codebases grow in complexity, making changes can become time-consuming and error-prone. With microtesting, changes can be made more efficiently and with fewer errors.

The Cost of End-to-End Tests

End-to-end tests are comprehensive tests that examine the entire system, from the user interface to the backend. While these tests provide an accurate picture of how the system performs, they are often very expensive both in terms of time and money.

Adopting Changes and Overcoming Resistance

The need to adopt changes in testing strategies may be met with resistance from developers. However, there are ways to overcome this resistance.

Willpower is not always enough

Willpower alone is often not enough to encourage developers to adopt new testing practices. Changes can be difficult to make, especially when old habits are entrenched.

Developing an immune system for changes

As humans, we have a natural immune system that defends our bodies against harmful viruses and bacteria. Similarly, we can develop an “immune system” for changes in testing strategies. This involves recognizing the benefits of testing, embracing new approaches, and letting go of old habits.

The Importance of Testing Over Time

Tests only give us confidence over time. Consistent testing builds confidence in the reliability of a system and can reduce the frequency of bugs emerging over time.

Hacks provide illusions of speed

Hacks can create an illusion of fast results, but they can actually harm a system’s long-term health. Consistent testing is necessary to ensure long-term reliability and performance.

The Consequences of an Inappropriate Testing Strategy

An inappropriate testing strategy can slow down delivery, deteriorate developer happiness and experience. Teams need to ensure that their testing strategies are appropriate for the complexity of their systems and the needs of their users.

An inappropriate testing strategy can slow down delivery by introducing bugs that need to be fixed, leading to longer development lead times.

Deteriorating Developer Happiness and Experience

An inappropriate testing strategy can harm the developer experience, leading to frustration and burnout. Developers need tools and strategies that help them focus on delivering features rather than getting bogged down in fixing bugs.

Rethinking the concept of the unit

The concept of the testing unit could benefit from its own refactoring. The traditional “unit” for testing may need to be redefined for more efficient and effective testing.

The Need for Refactoring

Refactoring is the process of improving code quality by making it more readable, maintainable, and efficient. Similarly, the concept of “unit” may need to be refactored to reflect the realities of modern software development.

Testing strategies are critical to successful software development. By adopting a testing strategy that is appropriate for the complexity of a system, teams can ensure the safety and reliability of code changes, reduce MTTR, and improve the developer experience. The concept of the unit may need to be rethought to keep pace with the evolving nature of software development. Finally, overcoming resistance to changes and embracing new approaches is essential for effective testing strategies.

Explore more

AI and Generative AI Transform Global Corporate Banking

The high-stakes world of global corporate finance has finally severed its ties to the sluggish, paper-heavy traditions of the past, replacing the clatter of manual data entry with the silent, lightning-fast processing of neural networks. While the industry once viewed artificial intelligence as a speculative luxury confined to the periphery of experimental “innovation labs,” it has now matured into the

Is Auditability the New Standard for Agentic AI in Finance?

The days when a financial analyst could be mesmerized by a chatbot simply generating a coherent market summary have vanished, replaced by a rigorous demand for structural transparency. As financial institutions pivot from experimental generative models to autonomous agents capable of managing liquidity and executing trades, the “wow factor” has been eclipsed by the cold reality of production-grade requirements. In

How to Bridge the Execution Gap in Customer Experience

The modern enterprise often functions like a sophisticated supercomputer that possesses every piece of relevant information about a customer yet remains fundamentally incapable of addressing a simple inquiry without requiring the individual to repeat their identity multiple times across different departments. This jarring reality highlights a systemic failure known as the execution gap—a void where multi-million dollar investments in marketing

Trend Analysis: AI Driven DevSecOps Orchestration

The velocity of software production has reached a point where human intervention is no longer the primary driver of development, but rather the most significant bottleneck in the security lifecycle. As generative tools produce massive volumes of functional code in seconds, the traditional manual review process has effectively crumbled under the weight of machine-generated output. This shift has created a

Navigating Kubernetes Complexity With FinOps and DevOps Culture

The rapid transition from static virtual machine environments to the fluid, containerized architecture of Kubernetes has effectively rewritten the rules of modern infrastructure management. While this shift has empowered engineering teams to deploy at an unprecedented velocity, it has simultaneously introduced a layer of financial complexity that traditional billing models are ill-equipped to handle. As organizations navigate the current landscape,