What is tech debt?
Engineering Metrics
Oct 18, 2023
In the bustling world of software engineering, there's a term that often hovers like a cloud over the heads of developers, engineering managers, and stakeholders alike: tech debt. But what is tech debt? Why does it matter? And how can engineering teams navigate its challenges? Let's dive deep into the world of tech debt, its common forms, and the ways it can accumulate over time.
Understanding Tech Debt
Tech debt, in its essence, is the future cost of taking shortcuts today. Imagine borrowing time from the future to meet present needs. It's like opting for a quick fix instead of a more comprehensive solution that would take longer. Just as financial debt accumulates interest, tech debt, when left unaddressed, can compound, leading to longer cycle times, decreased team performance, and potential downtime.
The Many Faces of Tech Debt
Tech debt isn't a monolithic entity. It manifests in various forms:
Code Debt: This arises when the codebase has shortcuts, often resulting from rushed coding sessions or bypassing the code review process.
Design Debt: Here, the software's design isn't scalable or maintainable, perhaps due to neglecting best practices or not leveraging the right frameworks.
Testing Debt: This form emerges when tests are skipped or when they're not comprehensive, leading to potential functionality or performance issues.
Documentation Debt: A lack of adequate documentation can make it challenging for team members to understand or modify the codebase, slowing down the development process.
The Accumulation of Tech Debt
Several factors can lead to the buildup of tech debt:
Tight Deadlines: Pressures to deliver new features or meet specific business goals within a constrained timeframe can push engineering teams to take shortcuts.
Resource Limitations: Whether it's a shortage of team members, tools, or time, limited resources can lead to suboptimal solutions.
Lack of Knowledge: If the engineering team isn't up-to-date with the latest software development practices or frameworks, they might inadvertently introduce tech debt.
Metrics: The Compass in Navigating Tech Debt
Metrics serve as a guiding light for engineering organizations. By closely monitoring specific engineering metrics, such as pull request size, coding times, and review times, teams can identify signs of tech debt early on. Some crucial metrics include:
Lead Time: The duration from the creation of work until its completion.
Code Churn: The frequency with which code is modified after its initial release.
Pull Request Size: The amount of code (lines of code) involved in a pull request.
Review Time: The time between a pull request review starts and code is merged.
Addressing Tech Debt
Confronting tech debt requires a concerted effort from the entire engineering team. Here are some strategies:
Regular Code Reviews: Ensuring that every piece of code undergoes a thorough code review process can help identify potential issues early on.
Refactoring: Periodically revisiting and refining the codebase can enhance its maintainability and reduce tech debt.
Continuous Improvement: Cultivating a culture where the team is always on the lookout for optimization opportunities can prevent tech debt from piling up.
BuildPulse: Your Ally in Managing Tech Debt
For teams aiming to manage and mitigate tech debt effectively, BuildPulse Engineering Metrics emerges as a game-changer. With its comprehensive metrics and developer copilot feature, BuildPulse not only aids teams in pinpointing tech debt but also automates routine tasks, ensuring timely and efficient reviews.
In conclusion, while tech debt is an inherent aspect of software engineering, it doesn't have to be a looming specter. With a clear understanding, proactive measures, and the right tools, engineering teams can manage and reduce tech debt, ensuring their software remains efficient, scalable, and maintainable.
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!