Craig Atkinson
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  • Five ways to be a force multiplier in software engineering

    Apr 24, 2022 career development

    As you move along in your software engineering career, one of the biggest ways you can have a larger impact is to be a force multiplier. What does it mean to be a force multiplier, you might ask? Having an outsized impact on others beyond your direct contributions. For example, helping level-up the skills of other …

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  • Stop lighting money on fire: Seven tips to improve developer productivity

    Oct 27, 2021 development productivity observability

    Software engineering teams are a critical component of many companies, where the effectiveness of these teams can have a significant impact on the company's success. Making investments in areas such as these in improving developer productivity will pay off significantly, giving development teams more time to focus on …

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  • Why write code comments?

    Aug 21, 2021 code development

    We've all seen it, code comments that are so obvious they add nothing but noise to the codebase. Redundant comments restating exactly what operation the code is performing. For example, this function's purpose is obvious from its name - adding a comment as well doesn't add any value: 1// Verifies if the auth token is …

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  • Code coverage stats - are they helpful?

    Jul 5, 2021 code-coverage metrics testing

    Code coverage - how many code lines and branches are accessed during a test - is easy to measure in many languages and test frameworks. And at first glance, measuring code coverage seems like a no-brainer. After all, who wouldn't want to make sure their code is sufficiently tested? But just because a metric is easy to …

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  • Testing OpenTelemetry JVM instrumentation with Kotlin

    Jun 29, 2021 OpenTelemetry observability Kotlin testing

    The OpenTelemetry Java instrumentation agent automatically instruments many common frameworks but there are times you may want to customize your application's telemetry by adding your own custom spans, adding attributes to existing spans, etc. In that case, it can be helpful to verify the custom instrumentation through …

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  • First steps into observability with OpenTelemetry, Honeycomb, and Kotlin

    Jun 6, 2021 OpenTelemetry Honeycomb observability Kotlin

    In this post we'll go through my initial journey to add observability to the Projektor test reporting app using OpenTelemetry and Honeycomb. Projektor already has traditional monitoring with metrics around p95 response times for each REST endpoint, counters for successful and failed operations, etc. But I was intrigued …

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  • Verifying order of React elements with React Testing Library

    Apr 6, 2021 React testing

    Recently I was working on sorting rows in a table in React and I wanted to be able to verify the rows sorted into the correct order. The table shows flaky tests (that is, tests that intermittently fail) and is built with Material Table. Material Table conveniently supports sorting out of the box by clicking on the …

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  • Customizing Recharts graphs for React and testing them with Cypress

    Mar 14, 2021 React Recharts testing Cypress

    After I added the ability to collect and store code coverage stats in the Projektor test reporting tool, I wanted to also add a graph that showed the trend of code coverage over time. Then users could track their repo's code coverage to see if was increasing, decreasing, or staying the same. The Projektor UI is built …

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  • Using the GitHub API with Kotlin

    Mar 10, 2021 github Kotlin

    Recently as part of Projektor test report project, I wanted to add the capability to comment directly on pull requests with links to the test report, code coverage stats, etc. That way users could get easier and faster access to the information than searching through the CI output to find the link to the Projektor test …

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  • Improving Postgres query performance with explain/analyze

    Mar 7, 2021 performance database Postgres

    Database queries slower than you'd like? With Postgres' built-in explain/analyze capability you can identify what's making the query slow and fix it. In this example, we'll analyze the performance of query called as part of an API GET endpoint. First, we'll seed our test database with enough data to get meaningful …

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Craig Atkinson

Software engineer, continually curious, highly caffeinated.
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Featured Posts

  • Ten communication tips for staff-plus engineers
  • Four keys I look for when reviewing software pull requests
  • Five ways to be a force multiplier in software engineering
  • Lessons learned from building an open-source side project

Recent Posts

  • Faster test failure debugging with GenAI?
  • Separate plumbing code from business logic
  • Faster Gradle builds through caching and parallelization
  • How many alerts are too many?
  • ChatGPT experiment - analyzing a team's oncall impact
  • You can't build everything: opportunity cost in software development
  • Ten communication tips for staff-plus engineers
  • Four keys I look for when reviewing software pull requests

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