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Selective Context-Sensitivity for k-CFA with CFL-Reachability

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Jingbo Lu
Dongjie He
Jingling Xue
Published in
Static Analysis
Publisher
Springer International Publishing

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Abstract

The chapter introduces Selectx, a new method for selective context-sensitivity in k-CFA, which leverages CFL-reachability to optimize pointer analysis. Selectx systematically selects context-sensitive nodes based on their impact on precision and efficiency, outperforming existing heuristic-based methods. The approach is implemented in Soot and evaluated on popular Java benchmarks, demonstrating significant performance improvements with minimal precision loss. The chapter also discusses challenges and future research directions in scaling k-CFA to large codebases.
AI Generated

This summary of the content was generated with the help of AI.

Thanks to all the reviewers for their constructive comments. This work is supported by Australian Research Council Grants (DP170103956 and DP180104069).

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previous chapter Toward Neural-Network-Guided Program Synthesis and Verification
next chapter Selectively-Amortized Resource Bounding
Title
Selective Context-Sensitivity for k-CFA with CFL-Reachability
Authors
Jingbo Lu
Dongjie He
Jingling Xue
Copyright Year
2021
Book
Static Analysis

Print ISBN: 978-3-030-88805-3

Electronic ISBN: 978-3-030-88806-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-88806-0_13
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