Multi-objective Test Case Selection Through Linkage Learning-based Crossover

TL;DR

This paper introduces L2-NSGA, a novel multi-objective evolutionary algorithm that employs linkage learning to improve test case selection, resulting in more cost-effective test suites that detect more faults.

Contribution

It presents a new linkage learning-based crossover operator for NSGA-II, enhancing its effectiveness in test case selection for regression testing.

Findings

L2-NSGA produces less expensive test suites.

L2-NSGA detects more faults than traditional MOEAs.

The proposed method outperforms existing approaches in effectiveness.

Abstract

Test Case Selection (TCS) aims to select a subset of the test suite to run for regression testing. The selection is typically based on past coverage and execution cost data. Researchers have successfully used multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and its variants, to solve this problem. These MOEAs use traditional crossover operators to create new candidate solutions through genetic recombination. Recent studies in numerical optimization have shown that better recombinations can be made using machine learning, in particular link-age learning. Inspired by these recent advances in this field, we propose a new variant of NSGA-II, called L2-NSGA, that uses linkage learning to optimize test case selection. In particular, we use an unsupervised clustering algorithm to infer promising patterns among the solutions (subset of test suites). Then, these patterns are used in the next iterations of L2-NSGA to create solutions that preserve these inferred patterns. Our results show that our customizations make NSGA-II more effective for test case selection. The test suite sub-sets generated by L2-NSGA are less expensive and detect more faults than those generated by MOEAs used in the literature for regression testing.