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Specification Inference and Invariant Generation: A Machine Learning Perspective

1 pagesPublished: July 28, 2013


Computing good specification and invariants is key to effective
and efficient program verification. In this talk, I will describe
our experiences in using machine learning techniques (Bayesian
inference, SVMs) for computing specifications and invariants
useful for program verification. The first project Merlin uses
Bayesian inference in order to automatically infer security
specifications of programs. A novel feature of Merlin is that it
can infer specifications even when the code under analysis gives
rise to conflicting constraints, a situation that typically
occurs when there are bugs. We have used Merlin to infer security
specifications of 10 large business critical web
applications. Furthermore, we show that these specifications can
be used to detect new information flow security vulnerabilities
in these applications.

In the second project Interpol, we show how interpolants can be
viewed as classifiers in supervised machine learning. This view
has several advantages: First, we are able to use off-the-shelf
classification techniques, in particular support vector
machines (SVMs), for interpolation. Second, we show that SVMs can
find relevant predicates for a number of benchmarks. Since
classification algorithms are predictive, the interpolants
computed via classification are likely to be relevant predicates
or invariants. Finally, the machine learning view also enables us
to handle superficial non-linearities. Even if the underlying
problem structure is linear, the symbolic constraints can give an
impression that we are solving a non-linear problem. Since
learning algorithms try to mine the underlying structure
directly, we can discover the linear structure for such
problems. We demonstrate the feasibility of Interpol via
experiments over benchmarks from various papers on program

In: Jacques Fleuriot, Peter Höfner, Annabelle McIver and Alan Smaill (editors). ATx'12/WInG'12: Joint Proceedings of the Workshops on Automated Theory eXploration and on Invariant Generation, vol 17, pages 54--54

BibTeX entry
  author    = {Aditya Nori},
  title     = {Specification Inference and Invariant Generation: A Machine Learning Perspective},
  booktitle = {ATx'12/WInG'12: Joint Proceedings of the Workshops on Automated Theory eXploration and on Invariant Generation},
  editor    = {Jacques Fleuriot and Peter H\textbackslash{}"ofner and Annabelle McIver and Alan Smaill},
  series    = {EPiC Series in Computing},
  volume    = {17},
  pages     = {54},
  year      = {2013},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/tx1s}}
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