Efficient Provenance Alignment in Reproduced Executions

Authors: 

Yuta Nakamura and Tanu Malik, DePaul University; Ashish Gehani, SRI International

Abstract: 

Reproducing experiments entails repeating experiments with changes. Changes, such as a change in input arguments, a change in the invoking environment, or a change due to nondeterminism in the runtime may alter results. If results alter significantly, perusing them is not sufficient—users must analyze the impact of a change and determine if the experiment computed the same steps. Making fine-grained, stepwise comparisons can be both challenging and time-consuming. In this paper, we compare a reproduced execution with recorded system provenance of the original execution, and determine provenance alignment. The alignment is based on comparing the specific location in the program, the control flow of the execution, and data inputs. Experiments show that the alignment method has a low overhead to compute a match and realigns with a small look-ahead buffer.

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BibTeX
@inproceedings {255020,
author = {Yuta Nakamura and Tanu Malik and Ashish Gehani},
title = {Efficient Provenance Alignment in Reproduced Executions},
booktitle = {12th International Workshop on Theory and Practice of Provenance (TaPP 2020)},
year = {2020},
url = {https://www.usenix.org/conference/tapp2020/presentation/nakamura},
publisher = {USENIX Association},
month = jun,
}