Johannes Bergsmann & Daniel Bauer
Are you still programming unit tests or already generating?
#1about 1 minute
Moving from manual unit testing to automated generation
The goal of test generation is to move from manual coding to a low-code platform that improves requirements coverage and collaboration between developers, testers, and requirements engineers.
#2about 2 minutes
Extending black box testing into the unit test level
The testing pyramid shows how black box testing, which focuses on requirements and interfaces, can be extended into the unit testing level, traditionally dominated by white box source code analysis.
#3about 4 minutes
Using equivalence classes and boundary values for testing
Black box testing relies on equivalence class partitioning and boundary value analysis to systematically test value ranges and their edges, where errors frequently occur.
#4about 2 minutes
A maturity model for unit testing beyond code coverage
A proposed maturity model for unit testing progresses from having no tests to implementing a full strategy with risk analysis and black box methods, moving beyond simple code coverage metrics.
#5about 2 minutes
Why 100% code coverage does not guarantee quality
High code coverage is the minimum requirement, not the goal, as it doesn't ensure that requirements are met or that the right tests have been written.
#6about 2 minutes
The workflow of the DevMate test code generator
DevMate parses a project's source code, stores its structure in an intermediate JSON model, allows users to define test data in a UI, and then generates standard, versionable test code.
#7about 7 minutes
Using systematic test data to find hidden code defects
A demonstration shows how to define valid and invalid test data based on requirements, automatically combine them into test cases, and generate code that reveals missing input validations.
#8about 2 minutes
Creating unit and integration tests with a visual canvas
Visit Test is a new standalone, web-based tool that provides a visual canvas for designing test scenarios, decision flows, and integration tests before generating code.
#9about 8 minutes
Q&A on test goals, AI generation, and tool specifics
The discussion covers setting better testing goals, the limitations of using ChatGPT for systematic test generation, how the tool handles code changes, and its local-only processing architecture.
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Using tests to manage AI-generated code and bugs
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24:02 MIN
Comparing techniques for automated test case generation
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11:57 MIN
Envisioning the future of testing with artificial intelligence
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