Natural Language UI Testing at DevBoost
Note: This article was written retrospectively, years after the project took place in 2015. While it captures my experiences and challenges from that time, it's enriched with insights and understanding I've gained since then.
TL;DR
- Technologies: Natural language processing for test specifications, UI testing frameworks, test parameterization
- Role: Intern developing a parameterizable UI test suite with natural language composition capabilities
- Key learning: The power of natural language test specifications and how they bridge the gap between technical and non-technical stakeholders
Fresh from my BMW internship, I arrived at DevBoost for what would become a pivotal moment in my career trajectory. What I didn't know then was that this internship would introduce me to the people who would later become my co-founders. Sometimes the most important connections happen when you least expect them.
The challenge: Making tests speak human
Our assignment was to develop a set of parameterizable UI tests that could be composed using natural language. This wasn't just about writing automated tests; it was about making testing accessible to people who think in requirements rather than code. The project represented my first real exposure to both UI testing and natural language test specifications.
The concept was elegant in its ambition. Instead of writing code like driver.findElement(By.id("submit")).click(), we aimed for specifications that read like "When the user clicks the submit button." This seemingly simple transformation required sophisticated mapping between natural language constructs and actual test implementations.
Breaking new ground
Working with UI tests for the first time revealed the complexity of frontend testing. Unlike backend unit tests with their predictable inputs and outputs, UI tests had to deal with timing issues, element visibility, dynamic content loading, and cross-browser inconsistencies. Each test needed to be robust enough to handle these variations while remaining maintainable and readable.
The natural language layer added another dimension of complexity. We had to design a system that could parse human-readable specifications, map them to corresponding test actions, and maintain the flexibility for parameterization. This meant creating a vocabulary that was both expressive enough for complex scenarios and constrained enough to be reliably parsed and executed.
The lasting impact
The ability to write frontend tests in natural language turned out to be more than just a technical achievement. It demonstrated how testing could become a collaborative activity between developers, testers, and business stakeholders. When tests are written in a language everyone understands, they become living documentation of system behavior.
This project introduced me to concepts that would prove invaluable throughout my career: the importance of test automation, the challenges of UI testing, and the power of domain-specific languages. More importantly, it showed me how technical solutions could bridge communication gaps between different roles in software development.
Looking back, the DevBoost internship was where I learned that good software development isn't just about writing code that works; it's about creating systems that people can understand, maintain, and trust. The relationships formed during this project would later evolve into something much bigger, but at that moment, we were just interns trying to make computers understand human language.