Compositional solution space quantification for probabilistic software analysis
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2184
- Type
- E - Conference contribution
- DOI
-
10.1145/2594291.2594329
- Title of conference / published proceedings
- ACM Sigplan Notices
- First page
- 123
- Volume
- 49
- Issue
- 6
- ISSN
- 1523-2867
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
10.1145/2666356.2594329
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First to combine program semantics, logical structure of path conditions, and interval constraint propagation to create a program-analysis tailored, compositional Monte Carlo estimation of the probability of programs processing floating-point inputs to exhibit relevant behaviours during execution. Extensions and evaluation on cryptographic problems in https://dl.acm.org/doi/10.1145/2786805.2786832 and https://doi.org/10.1007/978-3-319-57288-8_9. Artifact evaluated at PLDI, open source (https://goo.gl/bcK1Tr), and implemented on NASA's Java PathFinder, floating-point solver for ISSTAC project (DARPA:FA8750-15-2-0087). Invited lectures at PhD summer school RioCuarto 2018 (https://bit.ly/2QxOsov), Oxford and Edinburgh. Used as core solver for code-level performance analysis by Yang (https://doi.org/10.1145/2884781.2884794). PLDI'14 acceptance rate: 18%/287.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
