/Tests

Tests Are Bad For Developers

- Stephan Schmidt tl;dr: Stephan argues that while tests are crucial for software development, they are perceived negatively by developers because they highlight mistakes without immediate benefits. He suggests that tests become a burden when under business pressure, as they can lead to blame for missed deadlines. Stephan advocates for tests as non-negotiable and part of professional engineering, urging QA to support developers in creating effective tests.

featured in #463

Include Only Relevant Details In Tests

- Dagang Wei tl;dr: "A good test should include only details relevant to the test, while hiding noise:" Dadang shows us an example of this by presenting an embedded function where there is a lot of noise, making it hard to tell which details are relevant to the assert statement and testing function.

featured in #461

10 Things We've Learned About A/B Testing For Startups

- Ian Vanagas tl;dr: “In this week’s issue, we explore the secrets of running truly successful A/B tests (and some pitfalls to avoid).” These include: (1) You need to embrace failure. (2) Good A/B tests have 5 traits. (3) Use the “right place, right time” rule. (4) Create a proposal system. (5) Understanding significance. And more.

featured in #454

Simplifying Fluffy Constructors In Unit Tests

- Brian Kihoon Lee tl;dr: Brian discusses the challenges of writing unit tests that become bloated with unnecessary details. “A very common problem is that, over time, objects accumulate fields and subobjects, until it takes significant effort just to construct an object.” To address this, he proposes two solutions: (1) Factory methods: hide irrelevant details, making it easier to write and read tests. (2) Domain-Specific Languages: reduce syntactic fluff, making the code more readable and maintainable.

featured in #451

When, Why, And How GitHub And GitLab Use Feature Flags

- Ian Vanagas tl;dr: Ian discusses several benefits, such as reduced stress on developers, fewer failed deployments, and a higher rate of shipping features. GitLab calculated that fixing an issue without flags is as time-consuming as "developing a whole new feature." The article explores the advantages of feature flags over long-living feature branches for collaboration. Feature flags keep code changes small, make reviews easier, and limit merge conflicts. Both GitHub and GitLab use feature flags not just based on users but also on "actors" like organizations, teams, and repositories to create consistent experiences.

featured in #445

Keeping Figma Fast

- Slava Kim Laurel Woods tl;dr: Figma's journey in evolving its performance testing system as the company scaled. Initially, Figma used a single MacBook for all its in-house performance testing. However, as the codebase grew more complex and the team expanded, this approach became unsustainable. The article outlines the challenges Figma faced, such as the need for more granular performance tests and the limitations of running tests on a single piece of hardware. To address these issues, Figma adopted a two-system approach: a cloud-based system for mass testing and a hardware system for more targeted, precise tests. Both systems are connected by the same Continuous Integration system and aim to catch performance regressions early in the development cycle.

featured in #444

TDD With GitHub Copilot

- Paul Sobocinski tl;dr: The article explores the relationship between Test-Driven Development and AI coding assistants like GitHub Copilot. It argues that TDD remains essential even with AI assistance, as it provides fast and accurate feedback and helps in dividing and conquering problems. The article  shares tips for using GitHub Copilot with TDD, including starting with context, following the Red-Green-Refactor cycle, backfilling tests, and recognizing Copilot's limitations in refactoring.

featured in #441

Fuzz Testing Is the Best Thing To Happen To Our Application Tests

- Andrei Pechkurov tl;dr: The team at QuestDB faced challenges with segfaults, data corruption, and concurrency bugs. To address these, the team implemented fuzz testing, an automated software testing technique that provides invalid or unexpected data to a program to monitor for exceptions. This article details the process of introducing fuzz testing, revealing critical issues and leading to more robust database performance. The team also collaborated with SQLancer, a tool for testing SQL Database Management Systems, to uncover issues in their SQL engine.

featured in #441

A/B Testing Examples From Airbnb And YC's Top Companies

- Ian Vanagas tl;dr: Ian provides a comprehensive look at A/B testing examples from various successful companies, including Monzo, Instacart, Coinbase, Airbnb, and Convoy. It explores different approaches to A/B testing, such as Monzo's low-risk "pellets" strategy, Instacart's complex sampling problem-solving, Coinbase's scaling of tests, Airbnb's interleaving and dynamic p-values, and Convoy's Bayesian approach.

featured in #437

A Software Engineer's Guide To A/B Testing

- Lior Neu-ner tl;dr: This guide provides an introduction to A/B testing for software engineers. It explains the basics of A/B testing, including how to devise, implement, monitor and analyze tests, and answers common questions about A/B testing. The guide also lists conditions under which you may want to avoid A/B testing, such as lack of traffic, high implementation costs, and ethical considerations. The post concludes with a launch checklist for A/B tests.

featured in #434