Uncertainty-Driven Black-Box Test Data Generation

We can never be certain that a software system is correct simply by testing it, but with every additional successful test we become less uncertain about its correctness. In absence of source code or elaborate specifications and models, tests are usually generated or chosen randomly. However, rather...

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Bibliographic Details
Published inICST 2017 : proceedings : 10th IEEE International Conference on Software Testing, Verification and Validation : 13-17 March, Tokyo, Japan pp. 253 - 263
Main Authors Walkinshaw, Neil, Fraser, Gordon
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2017
Subjects
black-box testing
Data models
Genetic programming
machine learning
Machine learning algorithms
Software systems
Subspace constraints
test generation
Testing
Uncertainty
uncertainty sampling
Online AccessGet full text
DOI10.1109/ICST.2017.30

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Summary:We can never be certain that a software system is correct simply by testing it, but with every additional successful test we become less uncertain about its correctness. In absence of source code or elaborate specifications and models, tests are usually generated or chosen randomly. However, rather than randomly choosing tests, it would be preferable to choose those tests that decrease our uncertainty about correctness the most. In order to guide test generation, we apply what is referred to in Machine Learning as "Query Strategy Framework": We infer a behavioural model of the system under test and select those tests which the inferred model is "least certain" about. Running these tests on the system under test thus directly targets those parts about which tests so far have failed to inform the model. We provide an implementation that uses a genetic programming engine for model inference in order to enable an uncertainty sampling technique known as "query by committee", and evaluate it on eight subject systems from the Apache Commons Math framework and JodaTime. The results indicate that test generation using uncertainty sampling outperforms conventional and Adaptive Random Testing.
DOI:10.1109/ICST.2017.30