---

Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) and variable-strength testing strategies. Many AI-based strategies have been developed, such as Ant Colony, Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most existing AI-based strategies adopt complex search processes and require heavy computations. For this reason, existing AI-based strategies have been confined to small interaction strengths (i.e., t 3) and small test configurations. Recent studies demonstrate the need to go up to t=6 in order to capture most faults. This book presents the design and implementation of a new interaction test generation strategy, known as the Particle Swarm-based Test Generator (PSTG), for generating t-way and variable-strength test suites. Unlike other existing AI-based strategies, the lightweight computation of the particle swarm search process enables PSTG to support high interaction strengths of up to t=6. The performance of PSTG is evaluated using several sets of benchmark experiments. PSTG consistently outperforms its AI counterparts.

---

Obtained his BSc degree in Electrical Engineering from the Salahuddin University-Hawler in 2004 and his MSc degree from University Putra Malaysia in 2009 and his PhD degree from University Sains Malaysia, Software Engineering, in 2012. He is now a lecturer and IT director in the college of engineering, Salahaddin University-Hawler, Software Dep.

---