Automatic Test-Data Generation: An Immunological

Approach

Kostas Liaskos

Marc Roper

{Konstantinos.Liaskos, Marc.Roper}@cis.strath.ac.uk

TAIC PART 2007

CIS, Software Systems Group 2

Problem

Automated data-flow coverage of OO programs

Particularly challenging – Need to generate program as well as test data

Example of test case formatCUT 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 algorithmsSelection mechanismMutation

• Differences:No crossover is used

CIS, Software Systems Group 6

Human immune system

low affinityno selection

high affinityselected

activation

selectionproliferation differentiation

cell death

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 targetsAffinity 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