Dynamic Slicing of Python Programs

Abstract

Python is widely used for web programming and GUI development. Due to the dynamic features of Python, Python programs may contain various unlimited errors. Dynamic slicing extracts those statements from a program which affect the variables in a slicing criterion with a particular input. Dynamic slicing of Python programs is essential for program debugging and fault location. In this paper, we propose an approach of dynamic slicing for Python programs which combines static analysis and dynamic tracing of the Python byte code. It precisely handles the dynamic features of Python, such as dynamic typing of variables, heavy usage of first-class objects, and dynamic modifications of classes and instances. Finally, we evaluate our approach on several Python programs. Experimental results show that the whole dynamic slicing for each subject program spends at most about 13 seconds on the average and costs at most 7.58 mb memory space overhead. Furthermore, the average slice ratio of Python source code ranges from 9.26% to 59.42%. According to it, our dynamic slicing approach can be effectively and efficiently performed. To the best of our knowledge, it is the first one of dynamic slicing for Python programs.