Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Abstract

The potentially many software system input combinations make exhaustive testing practically impossible. To address this issue, combinatorial t-way testing (where t indicates the interaction strength, i.e. the number of interacting parameters (input)) was adopted to minimize the number of cases for testing. Complimentary to existing testing techniques (e.g. boundary value, equivalence partitioning, cause and effect graphing), combinatorial testing helps to detect faults caused by the faulty interaction between input parameters. In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. Particle Swarm Optimization, Cuckoo Search, Flower Pollination Algorithm, and Hyper-Heuristics (HHH), to name a few). Supporting the new free lunch theorem, as well as potentially offering new insights into the whole process of t-way generation, this paper proposes a new strategy with constraint support based on the Whale Optimization Algorithm (WOA). Our work is the first attempt to adopt the WOA as part of a search-based software engineering (SBSE) initiative for t-way test suite generation with constraint support. The experimental results of the test-suite generation indicate that WOA produces competitive outcomes compared to some selected single-based and population-based metaheuristic algorithms.

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

“…Therefore, finding the right combination of algorithms, better update schemes, smart scheduling, and the determining of operating modes are all open to optimization within itself. Moreover, design of a superior hybrid structure requires the knowledge of pros and cons of each individual algorithm depending on the problem and the understanding of the best fit when combined [40]- [42]. We can conclude that our proposed hybrid structure is brand new, well organized, and configured with smart scheduling methods developed to solve advanced optimization problems such as off-grid microgrid designs.…”

“…In order to imitate the natural behavior of the hawks in its simplest form, the average position is used for the position update within the search range. The average position of the current population of hawks in iteration is given in equation (42). The transition from the exploration phase to the exploitation phase is related to the energy of the prey chased by the Harris' hawks.…”

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

“…We include the pseudo-code (Algorithm 3) of the metaheuristic [91] for a better understanding of what was previously stated. Update the position of the current search agent using of Equation (31).…”

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

“…The first category uses a single meta-heuristic algorithm as the search engine for the test case. Example of this category includes SA [1], GA [1,2], ACA [2], PSO [3], HS [4], FPA [7], Whale Optimization Algorithm [47] and CS [5]. The second category uses adaptive or hybridization of meta-heuristics algorithms as the search engine.…”