Adaptive Random Testing

MSc dissertation project at the University of Sheffield: Testing and Diversity

Overview

1. Implement Adaptive random testing by static partitioning proposed by Sabor K K. and Thiel S.

2. Implement Adaptive Random Testing incorporating a local search technique by Schneckenburger C and Schweiggert F.

3. Implement Select Test From Candidate. Distribution and Select Strategy from Candidate Set can be modified here.

Uniform distribution & Non-uniform distribution; Average distance & Maximum distance selection were implemented.

And Random Testing as the baseline.

The failure Region is the block(strip) pattern.

The result printed in .csv contains F-measure and multiple metrics to evaluate test case distribution.

F-measure of methods in different failure rate

Method	Failure Rate
	0.01	0.005	0.002	0.001
Random Testing	97	198	501	1014
Select Test from Candidate	37	105	218	423
Static Partitioning	84	143	383	770
Hill Climbing	92	189	496	1374

Parameter adjustment

The failure rate and the size of the input domain can be adjusted here.

Usage

1. Clone the project.
2. Open it with IntelliJ IDEA.
3. Run the main function.