ProofInUse is coming!
For lovers of verification tools and critical system (we know you're out there!), we are very excited to present ProofInUse!
For lovers of verification tools and critical system (we know you're out there!), we are very excited to present ProofInUse!
While attribute Old allows expressing inside postconditions the value of objects at subprogram entry, this is in general not enough to conveniently express how record and array objects are modified by a procedure. A special attribute Update is defined in SPARK to make it easy to express such properties.
If you have a passion for Ada, need more information on our technology or would just like to have a chat, there are a couple of upcoming events where we'd love to meet up. What's more, we'll be launching our brand new product QGen at Embedded World!
Object Oriented Programming is known for making it particularly difficult to analyze programs, because the subprograms called are not always known statically. The standard for civil avionics certification has recognized this specific problem, and defines a specific verification objective called Local Type Consistency that should be met with one of three strategies. SPARK allows using one of these strategies, by defining the behavior of an overridden subprogram using a special class-wide contract and checking that the behavior of the overriding subprogram is a suitable substitution, following the Liskov Substitution Principle.
Tetris is a well-known game from the 80's, which has been ported in many versions to all game platforms since then. There are even versions of Tetris written in Ada. But there was no version of Tetris written in SPARK, so we've repaired that injustice. Also, there was no version of Tetris for the Atmel SAM4S ARM processor, another injustice we've repaired.
I'm proud, if not a bit nervous, to be the one firing the very first post on this brand new blog. Why are we starting a corporate blog at this time? There are many reasons for this. The main one is that there are many things happening at AdaCore and around the GNAT technology and we are seeking better ways to make them widely known while offering interested recipients the possibility to react and interact with us.
A common situation when proving properties about a program is that you end up writing additional code whose only purpose is to help proving the original program. If you're careful or lucky enough, the additional code you write will not impact the program being verified, and it will be removed during compilation, so that it does not inflate binary size or waste execution cycles. SPARK provides a way to get these benefits automatically, by marking the corresponding code as ghost code, using the new Ghost aspect.
In the first release of SPARK 2014, GNATprove only provided support for automatic provers, in particular Alt-Ergo. Automatic provers are very handy when it comes to perform a big numberof simple proof. But they can fail to prove valid formulas when the proof involves some advanced reasoning. As mentioned in a previous post, one check left unproved might invalidate assumptions on which are based the proofs of multiple other checks. This is a case where manual proof may be useful for SPARK 2014 users. The development version of GNATprove now supports Coq to perform manual proof.
The messages issued by the SPARK toolset will change a bit in the next version of both SPARK Pro and SPARK GPL. This post explains the change and the motivation behind it.
Correctness of robot software is a challenge. Just proving the absence of run-time errors (AoRTE) in robot software is a challenge big enough that even NASA has not solved it. Researchers have used SPARK to do precisely that for 3 well-known robot navigation algorithms. Their results will be presented at the major robotics conference IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014) this coming September.
In this article, we provide a short introduction to our paper at the Test and Proof 2014 conference in York, UK.
There are cases expressing all the specification of a package in SPARK is either impossible (for example if you need to link them to elements of the mathematical world, like trigonometry functions), cumbersome (especially if they require concepts that cannot easily be described using contracts, like transitivity, counting, summation...), or simply inefficient, for big and complex data structures like containers for example. In these cases, a user can provide directly a manually written Why3 translation for an Ada package using a feature named external axiomatizations. Coming up with this manual translation requires both a knowledge of the WhyML language and a minimal understanding of GNATprove's mechanisms and is therefore reserved to advanced users.
Guiding automatic solvers by adding intermediate assertions is a commonly used technique. We can go further in this direction, by adding complete pieces of code doing nothing, generally called ghost code, to guide the automated reasoning. This is an advanced feature, for people willing to manually guide proofs. Still, it is all in SPARK 2014 and thus does not require the user to learn a new language. We explain here how we can achieve inductive proofs on a permutation function.
New to SPARK? Want to "see" what's new in SPARK 2014? It's all in this 5 mn video demo!
Using SPARK or any other formal method in a certification requires that the applicant agrees with the certification authority on the verification objectives that this use of formal methods allows to reach, and how this is obtained and documented. In order to facilitate this process, the participants to the workshop on Theorem Proving in Certification have produced a draft set of guidelines, now publicly available.
In SPARK 2014, we can write a function F and annotate it with a pre and a postcondition. In this post, we explain how the SPARK 2014 proof tool handles a call to such a function.
In a previous blog post we described how aspect Global can be used to designate the specific global variables that a subprogram has to read and write. So, by reading the specification of a subprogram that has been annotated with aspect Global we can see exactly which variables, both local and global, are read and/or written each time the subprogram is called. Based purely on the Global aspect, this pretty much summarizes the full extent of our knowledge about the flow of information in a subprogram. To be more precise, at this point, we know NOTHING about the interplay between the inputs and outputs of the subprogram. For all we know, all outputs could be randomly generated and the inputs might not contribute in the calculation of any of the outputs. To improve this situation, SPARK 2014 uses aspect Depends to capture the dependencies between a subprogram's outputs and inputs. This blog post demonstrates through some examples how aspect Depends can be used to facilitate correct flow of information through a subprogram.
We have implemented a new feature in GNATprove for analyzing local subprograms in the context of their calls. This makes it possible to benefit from the most precise analysis for local subprograms, without incurring the cost of adding contracts to these subprograms.
The article explains how we improved the performance of the SPARK 2014 toolset when multiple CPU cores are available for proof.
Two recent research papers focus on how program contracts are used in practice in open source projects, in three languages that support contracts (Eiffel obviously, Java with JML contracts and C# with Code Contracts). I'm reporting what I found interesting (and less so) in these two studies.

