Automatic Test-Data Generation: An Immunological Approach Kostas Liaskos Marc Roper {Konstantinos.Liaskos, TAIC PART 2007

CIS, Software Systems Group2 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 Group3 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 Group4 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 Group5 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 Group6 Human immune system low affinity no selection high affinity selected activation selection proliferation differentiation cell death

CIS, Software Systems Group7 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 Group8 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 Group9 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 Group10 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