Multi-label software behavior learning

Yang Feng, Zhenyu Chen

Abstract: Software behavior learning is an important task in software engineering. Software behavior is usually represented as a program execution. It is expected that similar executions have similar behavior, i.e. revealing the same faults. Single-label learning has been used to assign a single label (fault) to a failing execution in the existing efforts. However, a failing execution may be caused by several faults simultaneously. Hence, it needs to assign multiple labels to support software engineering tasks in practice. In this paper, we present multi-label software behavior learning. A well-known multi-label learning algorithm ML-KNN is introduced to achieve comprehensive learning of software behavior. We conducted a preliminary experiment on two industrial programs: flex and grep. The experimental results show that multi-label learning can produce more precise and complete results than single-label learning.