Automatic Test-Data Generation: An Immunological Approach

1. Automatic Test-Data Generation: An Immunological Approach Kostas Liaskos Marc Roper {Konstantinos.Liaskos, Marc.Roper}@cis.strath.ac.uk TAIC PART 2007

2. Problem • Automated data-flow coverage of OO programs • Particularly challenging – Need to generate program as well as test data • Example of test case format CUT cut = new CUT(3); A a = new A(); a.meth2(4, 6); cut.meth(a, 9); CIS, Software Systems Group

3. Initial study • 6 classes from the standard Java library were tested • Good levels of data-flow coverage, but always lower than branch/statement • 2 categories of problematic test targets were identified • Equivalent: d-u pairs that correspond to the same code structure • Subsequent: the satisfaction of a test target is strongly related with another CIS, Software Systems Group

4. Proposed solution • Utilization of an Artificial Immune System (AIS) algorithm: • learning and adaptation implemented by affinity maturation combination of global & local search  may be beneficial to tackle subsequent test targets • immunological memory using memory cells  good solutions are stored for future use  may be beneficial to tackle both types of problematic test targets CIS, Software Systems Group

5. Clonal selection algorithm • Key features: • Mutation rate inversely proportionate to affinity • Cloning rate proportionate to affinity • Memory cells • AIS Algorithm vs. GA • Similarities: Population-based algorithms Selection mechanism Mutation • Differences: No crossover is used CIS, Software Systems Group

6. selection proliferation activation differentiation high affinity selected cell death low affinity no selection Human immune system CIS, Software Systems Group

7. Main challenge Built a generic framework • How do we mathematically represent immune cells and molecules? • How do we quantify their interactions or recognition? • How do we form the procedures of the variety of the observed functions in the human immune system? CIS, Software Systems Group

8. Proposed framework Representation: • B-cells & T-cells represented as the encoded test-cases • Receptors of the immune cells represented as encoded executed paths • Antigens represented as test targets Affinity computation: • Binary distance between a receptor and an antigen CIS, Software Systems Group

9. Preliminary experiment • Test object: triangle classification program • Aim: validate our framework & experiment with the parameters: • N (size of the Ab repertoire) = 20 • m (size of the memory set) = 18 • r (remaining Ab repertoire) = 2 • d (set of d lowest affinity Ab’s that will be replaced by new individuals) = 1 • Ngen (maximum number of generation) = 100 • n (number of highest affinity individuals to be chosen) = 10 • β (multiplying factor for the total number of clones) = 1 • Full path coverage for these parameter values • The algorithm failed to cover the “equilateral” test-target in all cases with different settings CIS, Software Systems Group

10. Conclusions & Future Work • Our paper introduces a framework for the application of AIS algorithms to the problem of automatic test-data generation • A prototype has been implemented • The next step is to run an extended experiment using 6 Java classes • Compare the results with GAs • Our ultimate goal is to propose a hybrid AIS&GA algorithm CIS, Software Systems Group