dylanscott

#!/usr/bin/env bash
set -euo pipefail

sudo sh -c 'echo "deb https://apt.dockerproject.org/repo ubuntu-$(lsb_release -cs) main" > /etc/apt/sources.list.d/docker.list'
curl -fsSL https://apt.dockerproject.org/gpg | sudo apt-key add -
sudo apt-key fingerprint 58118E89F3A912897C070ADBF76221572C52609D
sudo apt-get update
sudo apt-get -y install "docker-engine=1.13.1-0~ubuntu-$(lsb_release -cs)"

brendanzab

A graph-based core for a dependently typed language

Abstract

An overly-ambitious attempt to re-think the core calculus of dependent type theory by basing it on graphs as opposed to lambdas, Π-types, Σ-types, etc. The hope is that this might allow us to investigate dependency more closely, and allow us to refine programs to target different environments in an easier way than with traditional programming representations.

Introduction

xmnlab

serras

Program Analysis, a Big Happy Family

The idea behind program analysis is simple, right? You just want to know stuff about your program before it runs, usually because you don't want unexpected problems to arise (those are better in movies.) Then why looking at Wikipedia gives you headaches? Just so many approaches, tools, languages 🤯

In this article I would like to give a glimpse of an overarching approach to program analysis, based on ideas from abstract interpretation. My goal is not to pinpoint a specific technique, but rather show how they have common core concepts, the differences being due mostly to algorithmic challenges. In other words, static analysis have a shared goal, but it's a challenge to make them precise and performant.

Code is meant to be executed by a computer. Take the following very simple function:

fun cantulupe(x) = {