lkuper

Making Progress Under Uncertainty in SMT Solving, Research, and Life

Abstract

SAT and Satisfiability Modulo Theories (SMT) solvers have many important applications in PL, including verification, testing, type checking and inference, and program analysis – but they are often a mysterious black box to their users, even when those users are PL researchers with lots of solver experience! This talk will be partly a tutorial introduction to the inner workings of SAT and SMT solvers, and partly an extended analogy to navigating life as a researcher: making decisions when you have only incomplete information to go on, learning from decisions that turned out to be bad, and determining when to give up and when to try again. I’ll also highlight a variety of papers in this year’s POPL program that make use of SAT and SMT solving, and discuss why I think it’s worthwhile to learn about solver internals.

Outline

• Introduction
• Self-intro

lkuper

module Main where

import Data.IORef                        
                                         
data Counter = Counter { x :: IORef Int }

makeCounter :: Int -> IO Counter        
makeCounter i = do iref <- newIORef i   
                   return (Counter iref)

lkuper

$ git log -p --word-diff 2015-12-29-refactoring-as-a-way-to-understand-code.markdown | cat
commit 698f4140d1f141b339731f8fe8c984137f93880e
Author: Lindsey Kuper <lindsey@composition.al>
Date:   Fri Jun 9 14:00:31 2017 -0700

    Tweak a bunch of tags, mostly!

diff --git a/source/_posts/2015-12-29-refactoring-as-a-way-to-understand-code.markdown b/source/_posts/2015-12-29-refactoring-as-a-way-to-understand-code.markdown
index 8cafa552c1..e3ccd2b267 100644
--- a/source/_posts/2015-12-29-refactoring-as-a-way-to-understand-code.markdown

lkuper

t = 0.0, t/dt = 0.0, mod(t/dt, 10) = 0.0
t = 0.0001, t/dt = 1.0, mod(t/dt, 10) = 1.0
t = 0.0002, t/dt = 2.0, mod(t/dt, 10) = 2.0
t = 0.0003, t/dt = 2.9999999999999996, mod(t/dt, 10) = 2.9999999999999996
t = 0.0004, t/dt = 4.0, mod(t/dt, 10) = 4.0
t = 0.0005, t/dt = 5.0, mod(t/dt, 10) = 5.0
t = 0.0006, t/dt = 5.999999999999999, mod(t/dt, 10) = 5.999999999999999
t = 0.0007, t/dt = 7.0, mod(t/dt, 10) = 7.0
t = 0.0008, t/dt = 8.0, mod(t/dt, 10) = 8.0
t = 0.0009, t/dt = 9.0, mod(t/dt, 10) = 9.0

lkuper

lvars
LVar's
LVars
elseif
EuroSys
tuples
Karpinski
Blandy's
multi
Amr

lkuper

julia> foo = 3
3

julia> function test(a::Type{Val{foo}}) return foo end
test (generic function with 2 methods)

julia> test(Val{foo})
3

julia> foo = 4

lkuper

# Dependencies: `gem install ruby-gmail`

require 'gmail'
require 'csv'

email_subject = "subject line"

email_body = File.open("email.txt", "rb").read

username = "username"

lkuper

#lang racket

;; A solver for the following puzzle:
;; Given 5 integers a, b, c, d, and e,
;; find an expression that combines a, b, c, and d with arithmetic operations (+, -, *, and /) to get e.

(require srfi/1)

(define ops '(+ - * /))

lkuper

;; permutations: takes a list and returns a list of lists,
;; where each is a permutation of the original.
;; I got the idea for the algorithm from http://stackoverflow.com/a/23718676/415518.
(define (permutations ls)
  (cond
    ;; base cases: lists of length 0, 1, or 2
    [(null? ls) '(())]
    [(equal? (length ls) 1) `((,(first ls)))]
    [(equal? (length ls) 2)
     `((,(first ls) ,(second ls))

lkuper

;; This code is AWFUL and I'm sorry.

;; idea from http://stackoverflow.com/a/23718676/415518
;; This works after I added the `concat`!
(define permutations
  (lambda (ls)
    (cond
     [(null? ls) '(())]
     [(equal? (length ls) 1) `((,(car ls)))]
     [(equal? (length ls) 2)