Code Readability
Engineering Metrics
Oct 23, 2023
In the vast landscape of software engineering, amidst the buzzwords like "optimization", "automation", and "devops", there lies a silent yet powerful concept: code readability. At its core, code readability is the ease with which a developer can understand and navigate through a piece of code. But why is it so crucial, and how does it impact the broader engineering metrics and the development process? Let's dive in.
The Importance of Code Readability
Efficiency in the Development Process: A readable code reduces the cycle time. When code is easily understandable, team members spend less time deciphering it and more time implementing new features or optimizing existing ones.
Reduced Downtime: Bugs and errors are easier to spot in readable code. This means fewer disruptions and reduced downtime for the engineering team.
Enhanced Team Performance: When code is readable, team members can collaborate more efficiently. It fosters a culture of knowledge sharing and collective decision-making.
Stakeholder Satisfaction: Readable code translates to a more maintainable product. This ensures that the software meets business goals and leads to higher customer satisfaction.
Factors Contributing to Cognitive Complexity
Cognitive complexity is a metric that gauges how difficult a piece of code is to understand. Several factors can increase this complexity:
Nested Loops and Conditions: Deeply nested structures can be hard to follow and understand.
Long Methods: Methods that span hundreds of lines of code can be daunting and challenging to comprehend.
Lack of Comments: Comments provide context. Without them, developers might struggle to grasp the purpose of a particular code segment.
Inconsistent Naming Conventions: Consistency in naming variables, methods, and classes is crucial for readability.
Tightly Coupled Code: Code that's heavily dependent on other parts of the codebase can be challenging to understand and modify.
Enhancing Code Readability
Regular Code Reviews: Encourage team members to conduct thorough code reviews. This not only ensures code quality but also promotes a culture of continuous improvement.
Use Descriptive Naming: Variable and function names should be descriptive enough to convey their purpose.
Limit Function Length: As a baseline, functions should be concise. If a function is doing too much, consider breaking it into smaller, more focused functions.
Adopt Frameworks: Using established frameworks can standardize the development process, making the codebase more uniform and readable.
Training and Workshops: Organize regular training sessions for the engineering team on best practices in coding. This ensures that all team members are on the same page when it comes to writing readable code.
In Conclusion
Code readability might not make headlines, but its impact on the engineering organization is profound. It influences various engineering metrics, from cycle time to team performance. As engineering leaders and managers, it's imperative to prioritize code readability and provide the necessary tools and training to the team to uphold it. BuildPulse Engineering Metrics measures code readability, and enables action through alerting when readability drops. After all, in the world of software development, it's not just about how fast you can code, but also about how well others can understand and build upon your code.
FAQ
What is the difference between a flaky test and a false positive?
A false positive is a test failure in your test suite due to an actual error in the code being executed, or a mismatch in what the test expects from the code.
A flaky test is when you have conflicting test results for the same code. For example, while running tests if you see that a test fails and passes, but the code hasn’t changed, then it’s a flaky test. There’s many causes of flakiness.
What is an example of a flaky test?
An example can be seen in growing test suites - when pull request builds fail for changes you haven’t made. Put differently, when you see a test pass and fail without any code change. These failed tests are flaky tests.
What are common causes of flakiness?
Broken assumptions in test automation and development process can introduce flaky tests - for example, if test data is shared between different tests whether asynchronous, high concurrency, or sequential, the results of one test can affect another.
Poorly written test code can also be a factor. Improper polling, race conditions, improper event dependency handling, shared test data, or timeout handling for network requests or page loads. Any of these can lead to flaky test failures and test flakiness.
End-to-end tests that rely on internal API uptime can cause test flakiness and test failures.
What's the impact of flaky tests?
Flaky tests can wreck havoc on the development process - from wasted developer time from test retries, to creating bugs and product instability and missed releases, time-consuming flaky tests can grind your development process to a halt.
What is the best way to resolve or fix flaky tests?
Devops, software engineering, and software development teams will often need to compare code changes, logs, and other context across test environments from before the test instability started, and after - adding retries or reruns can also help with debugging. Test detection and test execution tooling can help automate this process as well.
BuildPulse enables you to find, assess impact metrics, quarantine, and fix flaky tests.
What are some strategies for preventing flaky tests?
Paying attention and prioritizing flaky tests as they come up can be a good way to prevent them from becoming an issue. This is where a testing culture is important - if a flaky test case is spotted by an engineer, it should be logged right away. This, however, takes a certain level of hygiene - BuildPulse can provide monitoring so flaky tests are caught right away.
What type of tests have flaky tests?
Flaky tests can be seen across the testing process - unit tests, integration tests, end-to-end tests, UI tests, acceptance tests.
What if I don't have that many flaky tests?
Flaky tests can be stealthy - often ignored by engineers and test runs are retried, they build up until they can’t be ignored anymore. These automated tests slow down developer productivity, impact functionality, and reduce confidence in test results and test suites. Better to get ahead while it’s easy and invest in test management.
It’s also important to prevent regressions to catch flakiness early while it’s manageable.
What languages and continuous integration providers does BuildPulse work with?
BuildPulse integrates with all continuous integration providers (including GitHub Actions, BitBucket Pipelines, and more), test frameworks, and workflows.
Combat non-determinism, drive test confidence, and provide the best experience you can to your developers!
How long does implementation/integration with BuildPulse take?
Implementation/integration takes 5 minutes!