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!
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.
AdaCore has a long history of providing tools and support to develop mission critical applications for Space. Check out this video we made and showed at the conference to see which ones!
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.
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?
It is with great sadness that I have to announce the death of Robert Dewar...
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 .
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.
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.
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.
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.
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.
Forget about the black box - we want you to see what's in the AdaCore box! Our machine room, the life blood of the company, is centered in the office space in a glass box and is pure eye candy for geeks and cable aficionados. The machine room is 204 sq ft (19 sq meters), contains 73 machines (running the gamut from VxWorks, Linux, Windows, Android and more) 1.5 miles of cables and a state of the art fire suppression mechanism! Oh and the modern air intake system gives it a green energy seal of approval. In the unlikely event of a fire; the smoke detectors allow 30 seconds before dispersing an agent called FM200 which will suppress the fire but not damage the equipment. The economizer takes advantage of winters' freeze and draws cool air from the outside to cool the machine room and the heated air is then spilled to common office areas thus reducing heating costs. We're proud to show-off the hardware side of our software!
Project-P Open Workshop
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.
Despite her famously sharp analytical mind, it’s unlikely Ada Lovelace could have predicted the durability of her legacy as the world’s first computer programmer and pioneer for women in computing.
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.
I recently joined AdaCore as a Technical Account Manager with an initial focus on the UK and Scandinavian regions, but for the last 12 months I've been busy working on the AdaCore University. The most recent addition to which is a course on Mixed Language Programming with Ada, and it includes lectures on the integration of Ada with C, C++ and Java. The course covers some advanced topics like mixed language object orientation, techniques for using Ada strong typing to combat known issue with C pointers and the pitfalls that are encountered when mixing native Ada code with Java at runtime. This course clearly demonstrates that Ada has strong support for integration with C, C++ and Java and it proves there are no technical barriers to its adoption in modern mixed language software systems.
In a recent article in Communications of the ACM, Carl Landwehr, a renowned scientific expert on security, defends the view that the software engineering community is doing overall a poor job at securing our global information system and that this is mostly avoidable by putting what we know works to work, to the point that most vulnerabilities could be completely avoided by design if we cared enough. Shocking! Or so it should appear.
As automatic proof is time consuming, it is important that rework following a change in source code is minimized. GNATprove uses a combination of techniques to ensure that, both for a single user, and when working in a team.
