Conversational Unit Test Generation for Pull Request Coverage
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Solution Overview
Problem
Existing version-control systems often fail to ensure that all changes in a pull request are adequately tested, as developers may not test changes or the tests may not sufficiently cover all modifications.
Innovation Solution
A conversational unit test generation system using a large language model to predict the location and content of unit tests for changes in a pull request, through an ordered sequence of prompts to determine testworthiness, placement, and generation of unit tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers manually test changes before submitting pull requests, then test coverage may be improved, but time consumption and productivity are reduced
Solution Approach 1:
The system enables automatic test generation where the development system itself produces test cases without human intervention. The large language model automatically analyzes code changes and generates appropriate unit tests, allowing the system to serve its own testing needs without requiring developer time for manual test creation.
Solution Approach 2:
The system performs test generation as a preliminary action before code submission and merging. By automatically generating tests during the pull request review process rather than requiring developers to complete testing beforehand, the system ensures test coverage is established in advance of merging without impacting development velocity.
2Reliability
If comprehensive manual testing is performed on all changes, then test thoroughness is improved, but time and resource consumption increase
Solution Approach 1:
The system changes the parameter of test generation from human-executed manual testing to automated AI-based generation. This fundamental parameter change transforms the testing process from a time-intensive manual activity to an automated process that operates at machine speed, maintaining thoroughness while eliminating time loss.
Solution Approach 2:
The system replaces the mechanical process of manual test creation with an automated large language model. The mechanical action of developers writing and executing tests is substituted by an AI system that automatically generates, executes, and evaluates tests, eliminating the time burden while maintaining or improving test quality.
3Productivity
If automated test generation is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The system introduces a large language model as an intermediary between code changes and test generation. This intermediary component handles the complexity of analyzing code diffs and generating appropriate tests, isolating the complexity from the core development workflow and allowing productivity gains without directly increasing user-facing system complexity.
Solution Approach 2:
The test generation process is segmented into discrete steps: analyzing the pull request, identifying changed files, determining test requirements, generating test cases, and executing tests. This segmentation allows each component to be independently managed and optimized, reducing overall system complexity while maintaining high productivity through automated orchestration of these separate functions.
Data Source
AI summary
A large language model, trained on source code and natural language text generates a unit test for a change to a file in a pull request of a code repository. An ordered sequence of prompts is created and each is applied serially to the large language model to perform an individual task that leads to the generation of the unit test. The unit test may be added to an existing file or generated as a newly-created file. Each prompt includes the data from a previously-issued prompt of the ordered sequence in order for the model to retain contextual knowledge learned previously. The model generates the unit test as update commands when the unit test is added to an existing file.