BCFA: Bespoke Control Flow Analysis for CFA at Scale

By: Ramanathan Ramu, Ganesha B. Upadhyaya, Hoan Anh Nguyen, and Hridesh Rajan

Abstract

Many data-driven software engineering tasks such as discovering programming patterns, mining API specifications, etc., perform source code analysis over control flow graphs (CFGs) at scale. Analyzing millions of CFGs can be expensive and performance of the analysis heavily depends on the underlying CFG traversal strategy. State-of-the-art analysis frameworks use a fixed traversal strategy. We argue that a single traversal strategy does not fit all kinds of analyses and CFGs and propose bespoke control flow analysis (BCFA). Given a control flow analysis (CFA) and a large number of CFGs, BCFA selects the most efficient traversal strategy for each CFG. BCFA extracts a set of properties of the CFA by analyzing the code of the CFA and combines it with properties of the CFG, such as branching factor and cyclicity, for selecting the optimal traversal strategy. We have implemented BCFA in Boa, and evaluated BCFA using a set of representative static analyses that mainly involve traversing CFGs and two large datasets containing 287 thousand and 162 million CFGs. Our results show that BCFA can speedup the large scale analyses by 1%-28%. Further, BCFA has low overheads; less than 0.2%, and low misprediction rate; less than 0.01%.

ACM Reference

Ramu, R. et al. 2020. BCFA: Bespoke Control Flow Analysis for CFA at Scale. ICSE: 42nd International Conference on Software Engineering, Seoul, South Korea (2020), 1037–1048.

BibTeX Reference

@inproceedings{RamuUpadhyayaNguyenRajan2020,
  author = {Ramanathan Ramu and Ganesha B. Upadhyaya and Hoan Anh Nguyen and Hridesh Rajan},
  title = {BCFA: Bespoke Control Flow Analysis for CFA at Scale},
  booktitle = {ICSE: 42nd International Conference on Software Engineering, Seoul, South Korea},
  pages = {1037--1048},
  year = {2020},
  publisher = {{ACM}},
  editor = {Gregg Rothermel and Doo{-}Hwan Bae},
  doi = {10.1145/3377811.3380435},
  abstract = {
  Many data-driven software engineering tasks such as discovering
  programming patterns, mining API specifications, etc.,
  perform source code analysis over control flow graphs (CFGs) at scale.
  Analyzing millions of CFGs can be expensive and
  performance of the analysis heavily depends on the underlying CFG
  traversal strategy. State-of-the-art analysis frameworks
  use a fixed traversal strategy. We argue that a single traversal strategy
  does not fit all kinds of analyses and CFGs and propose
  bespoke control flow analysis (BCFA). Given a control flow analysis (CFA)
  and a large number of CFGs, BCFA selects the most
  efficient traversal strategy for each CFG. BCFA extracts a set of
  properties of the CFA by analyzing the code of the CFA and
  combines it with properties of the CFG, such as branching factor and
  cyclicity, for selecting the optimal traversal strategy. We
  have implemented BCFA in Boa, and evaluated BCFA using a set of
  representative static analyses that mainly involve traversing
  CFGs and two large datasets containing 287 thousand and 162 million CFGs.
  Our results show that BCFA can speedup the
  large scale analyses by 1%-28%. Further, BCFA has low overheads; less
  than 0.2%, and low misprediction rate; less than
  0.01%.},
}