Focusing on the issues that the effectiveness and efficiency of existing Adaptive Random Testing (ART) algorithms are not as good as Random Testing (RT) for point failure pattern, an improved ART algorithm based on the concept of crowding level of failure region, namely CLART, was proposed to improve the traditional ART algorithm: Fixed Sized Candidate Set (FSCS) and Restricted Random Testing (RRT), etc. Firstly, the main crowding level was estimated according to the input region to determine the local search region. Secondly, some Test Cases (TCs) were generated by traditional ART algorithms in the local region. Finally, if no failure was found, a new local region was re-selected and some TCs were generated again until the first failure was found. The simulation results show that the effectiveness of the proposed CLART algorithm is about 20% higher than that of FSCS algorithm, and the efficiency is about 60% higher than that of FSCS algorithm. The experimental results indicate that the CLART algorithm can quickly locate the concentrated failure regions by searching several regions one by one to improve the effectiveness and efficiency.

To solve the Oracle problem of method sequence call in object-oriented software testing, a method of metamorphic relations constructing for object-oriented software testing based on algebraic specification was proposed. Firstly, metamorphic relations constructing criteria for object-oriented testing was defined based on the algebraic specification. Then the normal form metamorphic relations constructing method in the Generating a Finite number of Test cases (GFT) algorithm was improved according to these criteria. Finally, the improved method was verified through constructing IntStack class metamorphic relations. The experimental results showed that, compared with the normal form metamorphic relations constructing method, the metamorphic relations redundancy was reduced by 66% at the same mutation score. The results indicate that the new method has a low metamorphic relations redundancy and improves the efficiency of software testing.

A fuzzy multi-objective software reliability allocation model was established, and bacteria foraging optimization algorithm based on estimation of distribution was proposed to solve software reliability redundancy allocation problem. As the fuzzy target function, software reliability and cost were regarded as triangular fuzzy members, and bacterial foraging algorithm optimization based on Gauss distribution was applied. Different membership function parameters were set up, and different Pareto optimal solutions could be obtained. The experimental results show that the proposed swarm intelligence algorithm can solve multi-objective software reliability allocation effectively and correctly, Pareto optimal solution can help the decision between software reliability and cost.