This article introduces Just-in-Time Tests (JiTTests), a novel approach to automated software testing where Large Language Models (LLMs) generate tests on-the-fly for specific code changes. This method aims to address the challenges of traditional testing in the era of rapid agentic development by eliminating manual test authoring, maintenance, and review, thereby accelerating the detection of regressions.
Read original on Meta EngineeringThe rapid adoption of agentic software development, where AI agents contribute significantly to code generation and delivery, has exposed limitations in traditional software testing paradigms. Manual test creation and maintenance struggle to keep pace with the increased velocity of code changes, leading to inefficiencies, high false positive rates, and significant operational overhead. This article proposes JiTTesting as a solution to this evolving challenge.
JiTTests represent a fundamental departure from static, manually authored test suites. Instead of maintaining a persistent collection of tests, JiTTests are dynamically generated and executed in response to each specific code change (e.g., a pull request). This on-demand generation, powered by LLMs, allows tests to be highly tailored and relevant to the immediate context of the change, significantly reducing the likelihood of false positives and eliminating the burden of test maintenance.
Key Advantages for System Design
From a system design perspective, JiTTesting introduces a highly automated, adaptive, and scalable testing infrastructure. It shifts the burden of test creation and maintenance from human engineers to AI, allowing faster feedback loops and improving the overall efficiency of the development pipeline, especially in large-scale, continuously evolving systems. This impacts CI/CD pipelines, developer experience, and the overall robustness of large codebases.
Implementing a JiTTesting system requires a robust architecture capable of integrating LLMs, code analysis tools, and mutation testing frameworks into the CI/CD pipeline. Key considerations include the performance of LLM inference for test generation, the efficiency of mutant creation and execution, and the design of the assessment engine to accurately identify true positives without introducing significant latency to the development workflow. This setup also implies significant infrastructure for managing test environments and computational resources for LLM execution.
This approach highlights the increasing convergence of AI and software engineering, particularly in the realm of development tooling and infrastructure. Designing such a system necessitates careful consideration of data pipelines for code analysis, model serving for LLMs, and intelligent feedback mechanisms to refine test generation over time.