Probabilistic program analysis
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
This paper provides a survey of recent work on adapting techniques for program analysis to compute probabilistic characterizations of program behavior. We survey how the frameworks of data flow analysis and symbolic execution have incorporated information about input probability distributions to quantify the likelihood of properties of program states. We identify themes that relate and distinguish a variety of techniques that have been developed over the past 15 years in this area. In doing so, we point out opportunities for future research that builds on the strengths of different techniques.
Date Issued
2017-06-29
Date Acceptance
2015-08-23
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10223, pp.1-25
ISBN
9783319600734
ISSN
0302-9743
Publisher
Springer
Start Page
1
End Page
25
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
10223
Copyright Statement
© Springer International Publishing AG 2017
Source
GTTSE 2015
Subjects
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2015-08-23
Finish Date
2015-08-29
Coverage Spatial
Braga, Portugal