Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation

by   Kamal Z. Zamli, et al.

Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem BestounKamalFuzzy2017. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.


page 1

page 2

page 3

page 4


Generating Pairwise Combinatorial Interaction Test Suites Using Single Objective Dragonfly Optimisation Algorithm

Combinatorial interaction testing has been addressed as an effective sof...

Code-Aware Combinatorial Interaction Testing

Combinatorial interaction testing (CIT) is a useful testing technique to...

A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic...

Dynamic Solution Probability Acceptance within the Flower Pollination Algorithm for t-way Test Suite Generation

Flower Pollination Algorithm (FPA) is the new breed of metaheuristic for...

Software Module Clustering based on the Fuzzy Adaptive Teaching Learning based Optimization Algorithm

Although showing competitive performances in many real-world optimizatio...

An Evaluation of Monte Carlo-Based Hyper-Heuristic for Interaction Testing of Industrial Embedded Software Applications

Hyper-heuristic is a new methodology for the adaptive hybridization of m...

Generation and Application of Constrained Interaction Test Suites Using Base Forbidden Tuples With Mixed Neighborhood Tabu Search

Nowadays, ensuring the quality becomes challenging for most modern softw...

Please sign up or login with your details

Forgot password? Click here to reset