This is a curious story of how a bug found by a GNAT user in the runtime library of the compiler lead us to formally verify the well-known function Ada.Text_IO.Get_Line, which reads a line of text from an input file, and to find 3 more bugs in the process.
RSSR is a new conference focused on the development and verification of railway systems. We will present there how SPARK can be used to write abstract software specifications, whose refinement in terms of concrete implementation can be proved automatically using SPARK tools.
Hristian Kirtchev, who leads the developments of the GNAT compiler frontend, gave a very clear presentation of SPARK at the last AdaCore Tech Days in Boston. This was recorded, here is the video.
The new version 16 of SPARK Pro toolset is now available for AdaCore's customers. See demo online.
AdaCore University is hosting a 5-lessons course on SPARK 2014, which gives a complete overview of the technology.
To participate in the worldwide effort against global warming, Santa Claus has decided this year to retire his sleight pulled by 1024 reindeers (whose gas emitted at high altitude was threatening to put a premature end to winter season). I have been training some his Christmas elfs to build safe drones which will enter chimneys all over the world to deliver toys to kids. At least some of it is true...
SPARK supports two ways of encoding reals in a program: the usual floating-point reals, following the standard IEEE 754, and the lesser known fixed-point reals, called this way because their precision is fixed (contrary to floating-points whose precision varies with the magnitude of the encoded number). This support is limited in some ways when it comes to proving properties of computations on real numbers, and these limitations depend strongly in the encoding chosen. In this post, I show the results of applying GNATprove on simple programs using either floating-point or fixed-point reals, to explain these differences.
As presented in a recent post by Pavlos, the upcoming release of SPARK Pro will support concurrency features of Ada, with the restrictions defined in the Ravenscar profile of Ada. This profile restricts concurrency so that concurrent programs are deterministic and schedulable. SPARK analysis makes it possible to prove that shared data is protected against data races, that deadlocks cannot occur and that no other run-time errors related to concurrency can be encountered when running the program. In this post, I revisit the example given by Pavlos to show SPARK features and GNATprove analysis in action.
The new big feature of the SPARK 2016 release is the support of the Ravenscar profile. Users can now use protected objects and tasks to write concurrent code. On uniprocessor computers the toolset can ensure that no deadlocks or data races will occur and that no tasks will terminate. Read this blog post to learn more and see the new feature in practice.
by in Formal Verification – November 3, 2015
While the analysis of failed proofs is one of the most challenging aspects of formal verification, it would be much easier if a tool would automatically find values of variables showing why a proof fails. SPARK Pro 16, to be released in 2016, is going to introduce such a feature. If a proof fails, it attempts to generate a counterexample exhibiting the problem. This post introduces this new feature, developed in the scope of the ProofInUse laboratory.