Healy, Andrew
(2016)
Predicting SMT solver performance
for software verification.
Masters thesis, National University of Ireland Maynooth.
Abstract
The approach Why3 takes to interfacing with a wide variety of interactive
and automatic theorem provers works well: it is designed to overcome
limitations on what can be proved by a system which relies on a single
tightly-integrated solver. In common with other systems, however, the degree
to which proof obligations (or “goals”) are proved depends as much on
the SMT solver as the properties of the goal itself. In this work, we present a
method to use syntactic analysis to characterise goals and predict the most
appropriate solver via machine-learning techniques.
Combining solvers in this way - a portfolio-solving approach - maximises
the number of goals which can be proved. The driver-based architecture of
Why3 presents a unique opportunity to use a portfolio of SMT solvers for
software verification. The intelligent scheduling of solvers minimises the
time it takes to prove these goals by avoiding solvers which return Timeout
and Unknown responses. We assess the suitability of a number of machinelearning
algorithms for this scheduling task.
The performance of our tool Where4 is evaluated on a dataset of proof
obligations. We compare Where4 to a range of SMT solvers and theoretical
scheduling strategies. We find that Where4 can out-perform individual
solvers by proving a greater number of goals in a shorter average time.
Furthermore, Where4 can integrate into a Why3 user’s normal workflow -
simplifying and automating the non-expert use of SMT solvers for software
verification.
| Item Type: |
Thesis
(Masters)
|
| Keywords: |
Predicting; SMT solver performance; software verification; |
| Academic Unit: |
Faculty of Science and Engineering > Computer Science |
| Item ID: |
8770 |
| Depositing User: |
IR eTheses
|
| Date Deposited: |
07 Sep 2017 13:05 |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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