Feedback-controlled random test generation

Abstract

Feedback-directed random test generation is a widely used technique to generate random method sequences. It leverages feedback to guide generation. However, the validity of feedback guidance has not been challenged yet. In this paper, we investigate the characteristics of feedback-directed random test generation and propose a method that exploits the obtained knowledge that excessive feedback limits the diversity of tests. First, we show that the feedback loop of feedback-directed generation algorithm is a positive feedback loop and amplifies the bias that emerges in the candidate value pool. This over-directs the generation and limits the diversity of generated tests. Thus, limiting the amount of feedback can improve diversity and effectiveness of generated tests. Second, we propose a method named feedback-controlled random test generation, which aggressively controls the feedback in order to promote diversity of generated tests. Experiments on eight different, real-world application libraries indicate that our method increases branch coverage by 78% to 204% over the original feedback-directed algorithm on large-scale utility libraries.