Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

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Combinatorial interaction testing (CIT) 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 CIT (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomialtime (NP) hard computational problem. 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 (FIS), 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 discrete PSO (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.

Chapter Contents:

  • Abstract
  • 22.1 Introduction
  • 22.2 Combinatorial interaction testing
  • 22.2.1 Preliminaries
  • 22.2.2 Motivating example
  • 22.3 Related work
  • 22.4 PSO performance monitoring
  • 22.5 The strategy
  • 22.5.1 Fuzzy adaptive swarm VS-CIT
  • 22.5.2 The pair generation algorithm
  • 22.6 Empirical evaluation
  • 22.7 Observation
  • 22.8 Conclusions
  • References

Inspec keywords: program testing; search problems; fuzzy reasoning; combinatorial mathematics; computational complexity; particle swarm optimisation; evolutionary computation

Other keywords: fuzzy adaptive tuning; test cases; software testing technique; Mamdani fuzzy inference system; discrete PSO; input variables; VS-CIT problems; particle swarm optimization algorithm; DPSO; multiple synthetic; combinatorial interaction testing; fuzzy adaptation; exploration trade; variable strength test suites; nondeterministic polynomialtime hard computational problem; real-world software testing; stochastic population-based algorithms; input parameters; variable-strength combinatorial test suite generation; fuzzy adaptive selection; combined algorithm; benchmark problems; global search operations; local search operations; highly configurable software systems; mean test suite size; empirical evidence; variable strength CIT

Subjects: Diagnostic, testing, debugging and evaluating systems; Knowledge engineering techniques; Combinatorial mathematics; Other topics in statistics; Optimisation techniques

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