Software Engineering Standards of a Successful Tech Team

In the realm of software development, adhering to established engineering standards is not just beneficial; it’s essential for enhancing the quality, longevity, and efficiency of your solutions. Regardless of the project phase, implementing the practices discussed in this article can significantly boost team velocity, improve overall contributions, and enhance team satisfaction.

Coding Standards

Enforcing Clean Code Practices:

Maintaining clean code is crucial but challenging. Over past two decades of working with engineering teams, I’ve noticed that even with an initial commitment to SOLID coding principles, project code quality often degrades over time. This degradation typically stems from the initial software architecture’s lack of adaptability and the team’s varying familiarity with the established patterns.

Adopting a coding pattern that inherently enforces SOLID principles can address these challenges. Patterns like the Mediator, implemented through libraries such as MediatR, compel engineers to develop decoupled, testable, maintainable code, simplifying the learning curve and ensuring consistency across the board.

Code Testability:

Experience has shown that skipping unit tests to accelerate development adversely affects both the quality of the codebase and the maintainbility and velocity of new feature releases. Enforcing unit testability encourages developers to adopt SOLID coding patterns consistently.
While various other testing strategies may apply depending on the code and product type, making unit testability a mandatory standard is essential. Employing a coding pattern conducive to unit testing, like the Mediator pattern, minimizes the challenges associated with maintaining unit testability across different scenarios.

Data Design Standards

Non-Relational Structures as the Default:

The default choice of relational databases and SQL often stems from uncertainty about future project requirements. However, this choice can lead to complex, tightly coupled solutions. For projects expected to grow significantly, opting for non-relational data structures as the standard practice is advisable unless there is a clear justification for a relational approach.

Choosing the Right Data Store:

It’s crucial to select the appropriate database technology based on the specific needs of the data being stored. Relying on a single type of database for all scenarios is inefficient, particularly as the scale and complexity of the data increase.

Data Isolation Between Processes:

In architectures ranging from monoliths to microservices, each process should maintain its own persistence layer and avoid data sharing outside its domain. This practice helps eliminate single points of failure and reduces dependencies between systems.

Architectural Patterns

Embracing a Shared-Nothing Architecture:

Whether you opt for a monolithic or microservices architecture, adopting a shared-nothing approach can significantly enhance the decouplability and maintainability of your solutions. This broad topic extends beyond the scope of this article but is foundational to achieving high standards in software architecture.

Conclusion

Following these standards not only sets a solid foundation for current projects but also positions your tech team for future success. By committing to these practices, you ensure that your solutions are robust, scalable, and maintainable.