Adaptive Random Testing with Q-grams: The Illusion Comes True

TL;DR

This paper proposes a new adaptive random testing framework using q-grams to replace costly distance calculations, significantly improving scalability and fault detection in software testing.

Contribution

It introduces a novel q-gram based aggregation method for ART that reduces computational complexity from quadratic to linear, enhancing efficiency and effectiveness.

Findings

ART with q-grams covers 4x more unique targets than random testing

It outperforms traditional ART by covering 3.5x more targets

The approach scales efficiently to large web applications

Abstract

Adaptive Random Testing (ART) has faced criticism, particularly for its computational inefficiency, as highlighted by Arcuri and Briand. Their analysis clarified how ART requires a quadratic number of distance computations as the number of test executions increases, which limits its scalability in scenarios requiring extensive testing to uncover faults. Simulation results support this, showing that the computational overhead of these distance calculations often outweighs ART's benefits. While various ART variants have attempted to reduce these costs, they frequently do so at the expense of fault detection, lack complexity guarantees, or are restricted to specific input types, such as numerical or discrete data. In this paper, we introduce a novel framework for adaptive random testing that replaces pairwise distance computations with a compact aggregation of past executions, such as counting the q-grams observed in previous runs. Test case selection then leverages this aggregated data to measure diversity (e.g., entropy of q-grams), allowing us to reduce the computational complexity from quadratic to linear. Experiments with a benchmark of six web applications, show that ART with q-grams covers, on average, 4x more unique targets than random testing, and 3.5x more than ART using traditional distance-based methods.