/Tests

5 Strategies To Address Android Emulator Instability During Automated Testing

- John Gluck tl;dr: Android emulators are powerful, flexible, and essential for scaling mobile test automation — especially when you're running thousands of E2E tests across environments. But like any tool, they need the right setup.In this blog post, QA Wolf shares 5 key strategies that help you: Reduce flakiness, maximize emulator performance, keep tests running fast and reliably at scale. If you're running automated tests for Android, this is how to get the most out of your emulators.

featured in #618

Semantic Unit Testing

- Alex Molas tl;dr: “Semantic unit testing is a testing approach that evaluates whether a function’s implementation aligns with its documented behavior. The code is analyzed using LLMs to assess if the implementation matches the expected behavior described in the docstring. It’s basically having an AI review your code and documentation together to spot discrepancies or bugs, without running the code.”

featured in #615

Swarm Testing Data Structures

- Alex Kladov tl;dr: Alex explains how to use compile-time reflection to automatically extract a data structure's public API for comprehensive testing. He introduces "swarm testing" - randomly selecting subsets of features to test intensively rather than testing uniformly. This approach ensures complete API coverage and fails when new methods are added, prompting developers to add corresponding tests.

featured in #612

Meet Your New API Testing Agent That Never Sleeps

tl;dr: Stop discovering API contract breaks post merge. Signadot's AI-powered testing automatically detects API integration issues between microservices before merging code. No brittle mocks needed — test with real dependencies in lightweight Kubernetes sandboxes. Signadot’s AI testing agent identifies only meaningful API changes, eliminating false alarms. Join forward-thinking teams and try it for free today.

featured in #611

Underusing Snapshot Testing

- Alex Kladov tl;dr: “The idea of snapshot testing is simple. First, you convert the outcome of a test to a textual representation. Then, you compare it with expected value, specified as an inline string literal, using textual diff. Finally, there’s a tool that will automatically update the literal in the source code to match the value actually observed.”

featured in #608

Optimizing Our E2E Pipeline

tl;dr: “In the world of DevOps and Developer Experience, speed and efficiency can make a big difference on an engineer’s day-to-day tasks. Today, we’ll dive into how Slack’s DevXP team took some existing tools and used them to optimize an end-to-end testing pipeline. This lowered build times and reduced redundant processes, saving both time and resources for engineers at Slack.”

featured in #608

Vertical Integration For Superior QA

- Jon Perl tl;dr: Traditional outsourced QA relies on inefficient, costly tech stacks that fall short of QA engineers' needs. QA Wolf took a smarter approach. They built proprietary technology that aligns with customers’ needs, enabling their QA engineers to deliver 80%+ automated test coverage for their clients in just 4 months. In this free webinar, CEO Jon Perl reveals how QA Wolf is redefining QA automation.

featured in #605

Why Staging Is A Bottleneck For Microservice Testing

- Arjun Iyer tl;dr: Staging environments create painful bottlenecks in microservices testing - one bug blocks everyone with untraceable failures. Instead of costly duplicate environments, "sandboxes" use smart traffic routing on shared infrastructure, letting teams test simultaneously without interference. Teams catch issues earlier, ship faster, and eliminate waiting - while drastically cutting infrastructure costs and improving developer experience.

featured in #605

You Make Your Evals, Then Your Evals Make You.

- Tongfei Chen Yury Zemlyanskiy tl;dr: The post introduces AugmentQA, a benchmark for evaluating code retrieval systems using real-world software development scenarios rather than synthetic problems. AugmentQA uses codebases, developer questions, and keyword-based evaluation outperforming open-source models that excel on synthetic benchmarks but struggle with realistic tasks.

featured in #603

Making Uber’s ExperimentEvaluation Engine 100x Faster

tl;dr: This blog post describes how we made efficiency improvements to Uber’s Experimentation platform to reduce the latencies of experiment evaluations by a factor of 100x, milliseconds to microseconds. We accomplished this by going from a remote evaluation architecture to a local evaluation architecture.

featured in #603