An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

In this study, after applying the exponential family distribution (Goel-Okumoto, Rayleigh, Erlang) which are widely used in the field of reliability to the finite failure NHPP software reliability model, we compared and analyzed the reliability property based on shape parameters of the lifetime distribution. Software failure time data was used to identify software failure phenomena, and parametric estimation was applied to the maximum likelihood estimation method. As a result, in terms of the intensity function, the Rayleigh model was more efficient than the other models because the intensity function significantly decreased as the failure time passed. In the pattern of the mean value function, the Rayleigh model showed a slightly overestimated pattern compared to the true value, but it was more efficient than the Erlang model because of the smaller error. Also, as a result of comparing reliability by applying future mission time, the Rayleigh model was high and stable together with the Erlang model, but the Goel-Okumoto model showed a decreasing tendency. In conclusion, we found that the Rayleigh model is an efficient model with the best performance among the proposed models. Through this study, we have newly analyzed the property of software reliability model with the exponential family lifetime distribution without existing research case, and it was able to present new research information that software developers could use as basic guidelines.