Dynamic Random Testing: Technique and Experimental Evaluation

Abstract

A particularly good software testing strategy is to achieve the underlying testing goal while solving the problems of tradeoffs between testing effectiveness and efficiency. To improve the fault detection effectiveness of software testing, the principle of feedback control theory was adopted, which motivated the proposal of dynamic random testing (DRT). The main idea behind DRT is using the testing results to guide the test case selection to increase the selection probabilities of the subdomains with higher fault detection rates. Previous works show that DRT strategy can achieve better effectiveness than random testing strategy and random partition testing strategy, and has significantly lower computational costs than adaptive testing strategy. However, the essential factors that affect the performance of DRT, i.e., adjusting parameters, initial profile, and test case classification have not been thoroughly investigated. Besides, some experimental assumptions are inconsistent with real scenarios. Therefore, this paper gives a series of investigations on DRT with a set of practical subject programs. More specifically, the effectiveness and efficiency of DRT are presented, and the extended experiments on DRT with relevant factors are conducted. The results indicate that the effectiveness of DRT is robust to different initial profiles and affected noticeably by the adjusting parameter settings and test case classification methods.