Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.

On the Design and Optimization of Test Cases Using an Improved Artificial Bee Colony Algorithm-Based Swarm Intelligence Approach

In this research work, a swarm intelligence-based approach, namely an improved artificial bee colony (IABC), has been proposed to design and optimize the test cases during the software testing process. The novelty of the proposed IABC algorithm is that it has three major improvement heuristics over the general ABC algorithm: (1) it replaces random population generation during the initial phase into a systematic initial solution generation by means of a novel heuristic, namely ‘Chaotic Map'; (2) to eliminate the redundant test cases, another novel heuristic, namely ‘Euclidean Distance', is applied to maintain the diversity of population; (3) to increase the convergence speed, the fitness value of the previous solution is used in the new solution generation. Further, the proposed algorithm has been evaluated with several case studies and compared with the existing works using path coverage-based test adequacy criterion. Hence, the proposed work is improved, and it outperforms the existing works and provides optimal or near optimal test case generation for efficient software testing.