Source URL: https://www.assembled.com/blog/how-we-saved-hundreds-of-engineering-hours-by-writing-tests-with-llms
Source: Hacker News
Title: Using LLMs to enhance our testing practices
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text discusses the transformative impact of Large Language Models (LLMs) on software testing practices, particularly for code generation and test writing. It emphasizes how LLMs, like OpenAI’s models, streamline the testing process, reduce manual efforts, and enhance code quality by providing comprehensive test suites efficiently.
Detailed Description: The article outlines how Assembled leverages LLM technology to improve engineering efficiency in writing tests, highlighting several key points:
– **Rapid Test Generation**: LLMs can generate comprehensive tests for software functions where traditional methods might take hours.
– **Productivity Increase**: The use of LLMs has resulted in engineers reallocating hundreds of hours from test writing to feature development and refinement.
– **Practical Implementation**: The article details a case study involving an e-commerce function (CalculateOrderSummary) where an effective prompt led to generating a thorough test case suite using LLM capabilities.
– **Iterative Refinement**: While LLMs generate tests efficiently, there is a need for refinement through iterations to ensure completeness and adherence to coding standards.
– **Customization and Context**: Tailoring prompts based on specific contexts (like coding standards or specific libraries) significantly improves the output quality from LLMs.
– **Importance of Good Examples**: The authors note that LLMs perform best when provided with quality examples of existing tests and code structures to follow.
– **Best Practices**: Recommendations include checking generated tests for logic and compilation errors, and considering refactoring code for better testability.
Key Takeaways:
– LLMs effectively reduce testing backlog and improve code quality by lowering the barriers to writing comprehensive tests.
– Engineers are encouraged to remain critical and iterative in their approach to the tests generated by LLMs, ensuring they align with the standards of the codebase.
– The implications of adopting LLMs in testing extend beyond mere automation; they foster a culture of quality in engineering practices.
This exploration into LLM usage in software testing highlights substantial benefits for professionals in software security and compliance, emphasizing how advancements in AI can integrate with DevSecOps to enhance overall system reliability and security.