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Published on: September 20, 2016
Evolutionary testing using an extended Chaining Approach
1University of Sheffield, Department of Computer Science, Regent Court, 211 Portobello Street, Sheffield, S1 4DP, UK. p.mcminn@dcs.shef.ac.uk
Evolutionary Computation
|March 16, 2006
Summary
This study enhances evolutionary software testing by integrating a chaining approach to guide test data generation. This hybrid method improves the discovery of test data for complex software, including the libpng library.
Area of Science:
- Computer Science
- Software Engineering
- Artificial Intelligence
Background:
- Evolutionary Testing (ET) often struggles with inadequate fitness functions for test data generation.
- Lack of search guidance in ET stems from ignoring data dependencies and execution prerequisites.
Purpose of the Study:
- To improve evolutionary software test data generation by addressing limitations in fitness functions.
- To enhance search guidance by incorporating data dependencies and execution sequences.
Main Methods:
- Hybridizing Evolutionary Testing with an extended Chaining Approach.
- The Chaining Approach identifies and chains data dependencies for program statements.
- Performing targeted test data searches for each generated event sequence.
Main Results:
- The hybrid approach successfully generates test data for goals previously unattainable with standard ET.
- Demonstrated effectiveness on a test goal from the libpng library.
- Improved search direction into unexplored input domain areas.
Conclusions:
- Hybridizing ET with the Chaining Approach significantly enhances test data generation capabilities.
- This method overcomes limitations of traditional ET by considering program structure and data flow.
- Offers a more effective strategy for white-box software testing.
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