Calling inherited subprograms in Ada

This short post describes an idiom that can be used to help maintain complex hierarchies of tagged types, when methods need to call the parent types methods.

Make with Ada: Formal proof on my wrist

When the Pebble Time kickstarter went through the roof, I looked at the specification and noticed the watch was running on an STM32F4, an ARM cortex-M4 CPU which is supported by GNAT. So I backed the campaign, first to be part of the cool kids and also to try some Ada hacking on the device.

Modernizing Adacore's Open-Source Involvement

Through the adoption of GitHub we have taken our first step on the way to having a more collaborative and dynamic interaction with, both our users and open source technologies.

ARM TechCon and NBAA Conference 2015

We are continuing to develop tools for use within projects that require reliable and secure embedded software for ARM. Our engineering team have been busy creating demos running on ARM technology, such as Tetris in SPARK on ARM Cortex M4.

Using reference types to handle persistent objects

The Ada 2012 standard introduced user-defined references. The main idea behind this is simplifying the access to elements in a container. But you can use them to control the life-circle of your persistent objects. Let's see how it could work.

HIS Conference 2015, Bristol

We are excited to be sponsoring and exhibiting at the 2nd annual High Integrity Software conference, taking place on 5th November 2015 at The Royal Marriott Hotel in Bristol.

AdaCore Tech Days 2015

Traits-Based Containers

This post describes the design of a new containers library. It highlights some of the limitations of the standard Ada containers, and proposes a new approach using generic packages as formal parameters to make these new containers highly configurable at compile time.

New Book About SPARK 2014

I am very pleased to announce that a book is now available for those who want to learn formal verification with SPARK 2014. This book was written by Prof. John McCormick from University of Northern Iowa and Prof. Peter Chapin from Vermont Technical College. We've been interacting a lot with them since they started in 2013, and the result of these interactions is quite satisfying!

Make with Ada : From bits to music

I started out as an electronic musician, so one of my original motivations when I learnt programming was so that I could eventually *program* the sounds I wanted rather than just use already existing software to do it.

SPARK 2014 Rationale: Type Predicates

Preconditions and postconditions define a very strong mechanism for specifying invariant properties over the program's control. What about similar properties for the program's data? It turns out Ada 2012 defined such a construct, type predicates, which was not supported in SPARK until now. And now it is.

Make with Ada: "The Eagle has landed"

July 20, 1969, 8:18 p.m. UTC, while a bunch of guys were about to turn blue on Earth, commander Neil A. Armstrong confirms the landing of his Lunar Module (LM), code name Eagle, on the moon. Will you be able to manually land Eagle on the Sea of Tranquillity?

Make with Ada: All that is useless is essential

A few weeks ago I discovered the wonderful world of solenoid engines. The idea is simple: take a piston engine and replace explosion with electromagnetic field. In this article I will experiment a solenoid engine using a hacked hard drive and a software controller on a STM32F4 .

SPARKSkein: From tour-de-force to run-of-the-mill Formal Verification

In 2010, Rod Chapman released an implementation in SPARK of the Skein cryptographic hash algorithm, and he proved that this implementation was free of run-time errors. That was a substantial effort with the previous version of the SPARK technology. We have recently translated the code of SPARKSkein from SPARK 2005 to SPARK 2014, and used GNATprove to prove absence of run-time errors in the translated program. The difference between the two technologies is striking. The heroic effort that Rod put in the formal verification of the initial version of SPARKSkein could now be duplicated with modest effort and modest knowledge of the technology, thanks to the much greater proof automation that the SPARK 2014 technology provides, as well as various features that lower the need to provide supporting specifications, most notably contracts on internal subprograms and loop invariants.

How to prevent drone crashes using SPARK

The Crazyflie is a very small quadcopter sold as an open source development platform: both electronic schematics and source code are directly available on their GitHub and its architecture is very flexible. Even if the Crazyflie flies out of the box, it has not been developed with safety in mind: in case of crash, its size, its weight and its plastic propellers won’t hurt anyone! But what if the propellers were made of carbon fiber, and shaped like razor blades to increase the drone’s performance? In theses circumstances, a bug in the flight control system could lead to dramatic events. In this post, I present the work I did to rewrite the stabilization system of the Crazyflie in SPARK 2014, and to prove that it is free of runtime errors. SPARK also helped me to discover little bugs in the original firmware, one of which directly related with overflows. Besides the Crazyflie, this work could be an inspiration for others to do the same work on larger and more safety-critical drones.

How Our Compiler Learnt From Our Analyzers

Program analyzers interpret the source code of a program to compute some information. Hopefully, the way they interpret the program is consistent with the way that the compiler interprets it to generate an executable, or the information computed is irrelevant, possibly misleading. For example, if the analyzer says that there are no possible run-time errors in a program, and you rely on this information to compile with dynamic checking off, it is crucial that no run-time error could occur as a result of a divergence of opinion between the analyzer and the compiler on the meaning of an instruction. We recently discovered such an inconsistency in how our compiler and analyzers dealt with floating-point exponentiation, which lead to a change in how GNAT now compile these operations.

Count them all (reference counting)

Reference countingReference counting is a way to automatically reclaim unused memory. An element is automatically deallocated as soon as there are no more references to it in the program.

Larger than it looks (storage pools)

This post shows how to implement a special storage pool that allocates an extra header every time it allocates some memory. This can be used to store type specific information, outside of the type itself.

Verification on Ada code with Static and Dynamic Code Analysis - Webinar

One of the main challenges to get certification in Ada projects is the achievement of 100% code coverage but in most projects an amount of more than 95% structural coverage is hard to achieve. What can you do with the last 5% of code that can't be covered? DO-178C for example, provides a framework for the integration of various techniques in the development process to solve the problem. In this webinar you learn how static analysis and dynamic testing can help complete analysis for pieces of code that are not covered.

A quick glimpse at the translation of Ada integer types in GNATprove

In SPARK, as in most programming languages, there are a bunch of bounded integer types. On the other hand, Why3 only has mathematical integers and a library for bitvectors. Since bitwise operations can only be done on modular types in Ada, we currently translate arithmetic operations on signed integer types as operations on mathematical integers and arithmetic operations on modular types as operation on bitvectors. The only remaining question now is, how do we encode specific bounds of the Ada types into our Why3 translation ? In this post, I will present three different ways we tried to do this and explain which one we currently use and why.