On the role of diversity measures for multi-objective test case selection
Andrea De Lucia, Massimiliano Di Penta, Rocco Oliveto, Annibale Panichella · 2012
Test case selection has been recently formulated as multi-objective optimization problem trying to satisfy conflicting goals, such as code coverage and computational cost. This paper introduces the concept of asymmetric distance preserving, useful to improve the diversity of non-dominated solutions produced by multi-objective Pareto efficient genetic algorithms, and proposes two techniques to achieve this objective. Results of an empirical study conducted over four programs from the SIR benchmark show how the proposed technique (i) obtains non-dominated solutions having a higher diversity than the previously proposed multi-objective Pareto genetic algorithms; and (ii) improves the convergence speed of the genetic algorithms.