Improving the efficiency of symbolic execution by clustering the input data based on the complexity of test generation

Abstract

The object of research is means to increase computational effectiveness for automatic unit test generation process. It provides arguments for developing new method to achieve wider use of symbolic execution in commercial software development. The main task of the research is to create adaptive code clustering method that considers test generation complexity for structural source code elements and available computational resources that will increase effectiveness of computations. It is achieved by estimating test generation complexity and balancing the it for produced clusters during clusterization. As a result, proposed clustering method is adaptive to hardware and source code variability. It is shown that developed approach provides up to 30 % increase in computation effectiveness compared to clustering based on code structural properties alone for selected samples and up to 250 % in separate cases. This is caused by balanced estimated test generation complexity within generated clusters. It limits path explosion to expected levels that match computational resources for every cluster. Estimate of test generation complexity makes it possible to stop the computation when the spent time exceeds the corresponding complexity limit. Consequently, it makes it possible to prevent performing unnecessary computations. Proposed method makes it possible to use symbolic execution in commercial software development due to higher adaptability for source code and hardware variations. It will allow to reduce expenses on early-stage software testing and provide means for determining feasibility of symbolic execution for commercial projects