William Sumner and Xiangyu Zhang

Purdue University, USA

Track: Technical Research
Session: Analysis
We propose a novel fine-grained causal inference technique. Given two executions and some observed differences between them, the technique reasons about the causes of such differences. The technique does so by state replacement, i.e. replacing part of the program state at an earlier point to observe whether the target differences can be induced. It makes a number of key advances: it features a novel execution model that avoids undesirable entangling of the replaced state and the original state; it properly handles differences of omission by symmetrically analyzing both executions; it also leverages a recently developed slicing technique to limit the scope of causality testing while ensuring that no relevant state causes can be missed. The application of the technique on automated debugging shows that it substantially improves the precision and efficiency of causal inference compared to state of the art techniques.