Uncertainty-Driven Black-Box Test Data Generation

TL;DR

This paper introduces an uncertainty-driven approach to generate test data for black-box systems by inferring behavioral models and selecting tests that reduce uncertainty, improving over traditional random testing methods.

Contribution

It applies a machine learning query strategy framework to test data generation, using genetic programming for model inference and uncertainty sampling to target uncertain system behaviors.

Findings

Uncertainty sampling outperforms conventional testing methods.

The approach effectively targets uncertain parts of the system.

Evaluation on real-world systems shows improved test efficiency.

Abstract

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.