Preventing performance review bias
Engineering Metrics
Oct 18, 2023
In the dynamic world of software engineering, performance reviews play a pivotal role in gauging the productivity and efficiency of an engineering team. However, these evaluations, if not conducted meticulously, can be tainted by various biases. From the recency bias, where the most recent actions overshadow months of work, to gender or similarity bias, where unconscious preferences can skew perceptions, biases can lead to unfair and unrepresentative assessments.
But there's a solution: engineering metrics. By harnessing the power of objective data, engineering leaders can ensure that performance reviews are both just and accurate.
Objective Over Subjective: The Metrics Revolution
Metrics, by their very nature, offer a quantifiable measure of performance. In the realm of software engineering, these metrics can range from the cycle time, which measures the speed of task completion, to the depth and quality of code reviews. By relying on these metrics, engineering leaders can provide feedback rooted in tangible evidence, significantly reducing the influence of subjective biases.
Cycle Time: A Comprehensive Insight
Cycle time, a crucial engineering metric, gauges the time taken from the initiation to the completion of a task. While a shorter cycle time might seem indicative of better performance, it's vital to juxtapose this with the complexity and quality of tasks. By analyzing cycle time alongside other metrics like code quality or the intricacies of pull requests, a holistic view of a team member's contributions emerges.
Code Review: Quality Matters
Code reviews form the backbone of the software development process. But it's not just the quantity, but the quality of these reviews that truly counts. Metrics related to review depth can help engineering leaders discern the thoroughness with which team members are scrutinizing code. This ensures potential issues are identified, and constructive feedback is consistently provided.
Investment Profile: KTLO vs. New Initiatives
An engineer's investment profile can shed light on the nature of tasks they're undertaking. Are they predominantly working on new features, or are they ensuring the smooth running of existing systems (known as KTLO - Keeping The Lights On)? By understanding this balance, engineering leaders can recognize the diverse contributions of their team members, preventing any bias that might unduly favor more conspicuous tasks.
Tackling Biases Head-On with Metrics
Recency Bias: By examining metrics over a broader timeframe, the overshadowing effect of recent events can be mitigated. This ensures team members are evaluated on their consistent contributions throughout the period.
Gender and Similarity Bias: Objective data acts as an equalizer. When evaluations are rooted in metrics, team members are judged based on performance, not gender or any unconscious biases.
Holistic Evaluation: By integrating project management metrics with code metrics, stakeholders get a comprehensive picture of both the development process and the end results. This can be instrumental in decision-making and in setting future benchmarks for the engineering team.
BuildPulse: The Game-Changer
BuildPulse Engineering Metrics stands out as an indispensable tool for engineering leaders aiming for unbiased performance reviews. With its detailed reporting and developer copilot feature, BuildPulse ensures that pull requests, both new and stale, are addressed promptly. Reviews are conducted in a timely manner, and repetitive tasks are seamlessly automated. By offering a clear snapshot of each team member's contributions, BuildPulse plays a pivotal role in ensuring evaluations are both fair and objective.
In Conclusion
In the ever-evolving domain of software engineering, objective and unbiased performance reviews are of paramount importance. By leveraging engineering metrics, leaders can effectively combat inherent biases, ensuring that evaluations are a true reflection of a team member's contributions. As the field continues to grow and diversify, the emphasis on objective data will be instrumental in fostering a just and inclusive engineering organization.
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!