Genetic Micro-Programs use the principles of genetic algorithms (a type of evolutionary computing) to generate micro-programs that test software automatically. Genetic algorithms simulate the process of natural selection, evolving solutions to problems by “breeding” better solutions over successive generations. This concept is applied to automated testing by generating test inputs that explore the full range of possible scenarios for a given program.
How It Works:
- Micro-Programs: Instead of having human testers or traditional tools manually create test cases, genetic micro-programs are generated by an evolutionary algorithm. These programs are small and focused on exploring specific input spaces or software paths.
- Genetic Evolution: Test cases evolve over generations, starting with a population of random test inputs. Through crossover (combining successful test inputs) and mutation (altering inputs slightly), new test inputs are created. Over time, the most successful inputs—those that exercise the most important or complex paths—are prioritized.
- Fitness Function: A fitness function is used to evaluate the quality of each test case. This function checks how well the test input helps explore the system’s behavior or identify defects. Inputs that maximize the coverage of different parts of the software are considered the best.
Benefits:
- Enhanced Test Coverage: This method explores the full parameter space of the software, improving test coverage.
- Automation: Once set up, the process is highly automated, reducing the manual effort required to create extensive test suites.
- Uncovering Edge Cases: Genetic algorithms are excellent at finding edge cases or unusual combinations of inputs that traditional methods might overlook.