A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search

TY - JOUR

T1 - A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search

AU - Harman, Mark

AU - McMinn, Phil

PY - 2010

Y1 - 2010

N2 - Search-based optimization techniques have been applied to structural software test data generation since 1992, with a recent upsurge in interest and activity within this area. However, despite the large number of recent studies on the applicability of different search-based optimization approaches, there has been very little theoretical analysis of the types of testing problem for which these techniques are well suited. There are also few empirical studies that present results for larger programs. This paper presents a theoretical exploration of the most widely studied approach, the global search technique embodied by Genetic Algorithms. It also presents results from a large empirical study that compares the behavior of both global and local search-based optimization on real-world programs. The results of this study reveal that cases exist of test data generation problem that suit each algorithm, thereby suggesting that a hybrid global-local search (a Memetic Algorithm) may be appropriate. The paper presents a Memetic Algorithm along with further empirical results studying its performance.

AB - Search-based optimization techniques have been applied to structural software test data generation since 1992, with a recent upsurge in interest and activity within this area. However, despite the large number of recent studies on the applicability of different search-based optimization approaches, there has been very little theoretical analysis of the types of testing problem for which these techniques are well suited. There are also few empirical studies that present results for larger programs. This paper presents a theoretical exploration of the most widely studied approach, the global search technique embodied by Genetic Algorithms. It also presents results from a large empirical study that compares the behavior of both global and local search-based optimization on real-world programs. The results of this study reveal that cases exist of test data generation problem that suit each algorithm, thereby suggesting that a hybrid global-local search (a Memetic Algorithm) may be appropriate. The paper presents a Memetic Algorithm along with further empirical results studying its performance.

U2 - 10.1109/TSE.2009.71

DO - 10.1109/TSE.2009.71

M3 - Article

SN - 1939-3520

SN - 2326-3881

VL - 36

SP - 226

EP - 247

JO - IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

JF - IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

IS - 2

M1 - 5342440

ER -