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Expanding Hot Code Path for Data Cleaning on Software Graph

Guang Sun1, 2, *, Xiaoping Fan1, Wangdong Jiang1, Hangjun Zhou1, Fenghua Li1, Rong Yang1

1 Institute of Big Data, Hunan University of Finance and Economics, Changsha, 410205, China.
2 School of Engineering, University of Alabama, Tuscaloosa, 35487, USA.

* Corresponding Author: Guang Sun. Email: .

Computers, Materials & Continua 2020, 63(2), 743-753.


Graph analysis can be done at scale by using Spark GraphX which loading data into memory and running graph analysis in parallel. In this way, we should take data out of graph databases and put it into memory. Considering the limitation of memory size, the premise of accelerating graph analytical process reduces the graph data to a suitable size without too much loss of similarity to the original graph. This paper presents our method of data cleaning on the software graph. We use SEQUITUR data compression algorithm to find out hot code path and store it as a whole paths directed acyclic graph. Hot code path is inherent regularity of a program. About 10 to 200 hot code path account for 40%-99% of a program’s execution cost. These hot paths are acyclic contribute more than 0.1%-1.0% of some execution metric. We expand hot code path to a suitable size which is good for runtime and keeps similarity to the original graph.


Cite This Article

G. Sun, X. Fan, W. Jiang, H. Zhou, F. Li et al., "Expanding hot code path for data cleaning on software graph," Computers, Materials & Continua, vol. 63, no.2, pp. 743–753, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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