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.

Since Markov chain model based software testing does not consider the software structural information and has low ability of path coverage and fault detection, a new software testing model called software hierarchical testing model was proposed based on the combination of statistical testing and Markov chain model based testing. The software hierarchical testing model contains the interaction between software and external environment, and also describes the internal structural information of software. Besides, the algorithm for generating test data set was put forward:firstly, the test sequences conforming to the actual usage of software were generated; then the input data which covered software internal structure was generated for the test sequences. Finally, in the comparison experiments with software testing based on Markov chain, the new model satisfies the software testing sufficiency and improves the test data set's ability of path coverage and fault detection.