AdaCore Blog

171 entries tagged with #GNAT

by Paul Butcher

Finding Vulnerabilities using Advanced Fuzz testing and AFLplusplus v3.0

Some of you may recall an AdaCore blog post written in 2017 by Thales engineer Lionel Matias titled "Leveraging Ada Run-Time Checks with Fuzz Testing in AFL". This insightful post took us on a journey of discovery as Lionel demonstrated how Ada programs, compiled using GNAT Pro and an adapted assembler pass can be subjected to advanced fuzz testing. In order to achieve this Lionel demonstrated how instrumentation of the generated assembly code around jump and label instructions, could be subjected to grey-box (path aware) fuzz testing (using the original AFL v2.52b as the fuzz engine). Lionel explained how applying the comprehensive spectrum of Ada runtime checks, in conjunction with Ada's strong typing and contract based programming, enhanced the capabilities of fuzz testing beyond the abilities of other languages. Ada's advanced runtime checking, for exceptions like overflows, and the scrutiny of Ada's design by contract assertions allow corner case bugs to be found whilst also utilising fuzz testing to verify functional correctness.

#Security   

by Jon Andrew

CuBit: A General-Purpose Operating System in SPARK/Ada

Last year, I started evaluating programming languages for a formally-verified operating system. I've been developing software for a while, but only recently began work in high integrity software development and formal methods. There are several operating system projects, like the SeL4 microkernel and the Muen separation kernel, that make use of formal verification. But I was interested in using a formally-verified language to write a general-purpose OS - an environment for abstracting the underlying hardware while acting as an arbiter for running the normal applications we're used to.

by Pat Rogers

From Ada to Platinum SPARK: A Case Study for Reusable Bounded Stacks

This blog entry describes the transformation of an Ada stack ADT into a completely proven SPARK implementation that relies on static verification instead of run-time enforcement of the abstraction’s semantics. We will prove that there are no reads of unassigned variables, no array indexing errors, no range errors, no numeric overflow errors, no attempts to push onto a full stack, no attempts to pop from an empty stack, that subprogram bodies implement their functional requirements, and so on. As a result, we get a maximally robust implementation of a reusable stack abstraction providing all the facilities required for production use.

#SPARK    #Ada    #Transitioning to SPARK   

by Quentin Ochem

Witnessing the Emergence of a New Ada Era

For nearly four decades the Ada language (in all versions of the standard) has been helping developers meet the most stringent reliability, safety and security requirements in the embedded market. As such, Ada has become an entrenched player in its historic A&D niche, where its technical advantages are recognized and well understood. Ada has also seen usage in other domains (such as medical and transportation) but its penetration has progressed at a somewhat slower pace. In these other markets Ada stands in particular contrast with the C language, which, although suffering from extremely well known and documented flaws, remains a strong and seldom questioned default choice. Or at least, when it’s not the choice, C is still the starting point (a gateway drug?) for alternatives such as C++ or Java, which in the end still lack the software engineering benefits that Ada embodies..

by Joffrey Huguet , Johannes Kanig

Proving a simple program doing I/O ... with SPARK

The functionality of many security-critical programs is directly related to Input/Output (I/O). This includes command-line utilities such as gzip, which might process untrusted data downloaded from the internet, but also any servers that are directly connected to the internet, such as webservers, DNS servers and so on. In this blog post we show an approach that deals with error handling and reasoning about content, and demonstrate the approach using the cat command line utility.

#Formal Verification    #SPARK   

by Boran Car

Bringing Ada To MultiZone

C is the dominant language of the embedded world, almost to the point of exclusivity. Due to its age, and its goal of being a “portable assembler”, it deliberately lacks type-safety, opening up exploit vectors. Proposed solutions are partitioning the application into smaller intercommunicating blocks, designed with the principle of least privilege in mind; and rewriting the application in a type-safe language. We believe that both approaches are complementary and want to show you how to combine separation and isolation provided by MultiZone together with iteratively rewriting parts in Ada. We will take the MultiZone SDK demo and rewrite one of the zones in Ada.

#Ada    #embedded    #Embedded Development    #Security    #multizone    #Hex-Five   

by Allan Ascanius , Per Dalgas Jakobsen

Winning DTU RoboCup with Ada and SPARK

The Danish Technical University has a yearly RoboCup where autonomous vehicles solve a number of challenges. We participated with RoadRunner, a 3D printed robot with wheel suspension, based on the BeagleBone Blue ARM-based board and the Pixy 1 camera with custom firmware enabling real-time line detection. Code is written in Ada and formally proved correct with SPARK at Silver level.

#Robotics    #Ada    #SPARK   

by Pamela Trevino

Public Ada Training Paris June 3-7, 2019

This course is geared to software professionals looking for a practical introduction to the Ada language with a focus on embedded systems, including real-time features as well as critical features introduced in Ada 2012. By attending this course you will understand and know how to use Ada for both sequential and concurrent applications, through a combination of live lectures from AdaCore's expert instructors and hands-on workshops using AdaCore's latest GNAT technology. AdaCore will provide an Ada 2012 tool-chain and ARM-based target boards for embedded workshops. No previous experience with Ada is required.

by Quentin Ochem

Proving Memory Operations - A SPARK Journey

The promise behind the SPARK language is the ability to formally demonstrate properties in your code regardless of the input values that are supplied - as long as those values satisfy specified constraints. As such, this is quite different from static analysis tools such as our CodePeer or the typical offering available for e.g. the C language, which trade completeness for efficiency in the name of pragmatism. Indeed, the problem they’re trying to solve - finding bugs in existing applications - makes it impossible to be complete. Or, if completeness is achieved, then it is at the cost of massive amount of uncertainties (“false alarms”). SPARK takes a different approach. It requires the programmer to stay within the boundaries of a (relatively large) Ada language subset and to annotate the source code with additional information - at the benefit of being able to be complete (or sound) in the verification of certain properties, and without inundating the programmer with false alarms.

by Pamela Trevino

Public Ada Training Paris, France Dec 3 - 7, 2018

This course is geared to software professionals looking for a practical introduction to the Ada language with a focus on embedded systems, including real-time features as well as critical features introduced in Ada 2012. By attending this course you will understand and know how to use Ada for both sequential and concurrent applications, through a combination of live lectures from AdaCore's expert instructors and hands-on workshops using AdaCore's latest GNAT technology. AdaCore will provide an Ada 2012 tool-chain and ARM-based target boards for embedded workshops. No previous experience with Ada is required.

by Yannick Moy

Tokeneer Fully Verified with SPARK 2014

Tokeneer is a software for controlling physical access to a secure enclave by means of a fingerprint sensor. This software was created by Altran (Praxis at the time) in 2003 using the previous generation of SPARK language and tools, as part of a project commissioned by the NSA to investigate the rigorous development of critical software using formal methods. The project artefacts, including the source code, were released as open source in 2008. Tokeneer was widely recognized as a milestone in industrial formal verification. We recently transitioned this software to SPARK 2014, and it allowed us to go beyond what was possible with the previous SPARK technology. We have also shown how security vulnerabilities introduced in the code can be detected by formal verification.

#SPARK    #Formal Methods   

by Lionel Matias

Leveraging Ada Run-Time Checks with Fuzz Testing in AFL

Fuzzing is a very popular bug finding method. The concept, very simple, is to continuously inject random (garbage) data as input of a software component, and wait for it to crash. If, like me, you find writing robustness test tedious and not very efficient in finding bugs, you might want to try fuzzing your Ada code.Here's a recipe to fuzz-test your Ada code, using American Fuzzy Lop and all the runtime checks your favorite Ada compiler can provide.Let's see (quickly) how AFL works, then jump right into fuzzing 3 open-source Ada libraries: ZipAda, AdaYaml, and GNATCOLL.JSON.

#Testing    #Ada    #VerificationTools   

by Jamie Ayre

Welcoming New Members to the GNAT Pro Family

As we see the importance of software grow in applications, the quality of that software has become more and more important. Even outside the mission- and safety-critical arena customers are no longer accepting software failures (the famous blue screens of death, and there are many...). Ada has a very strong answer here and we are seeing more and more interest in using the language from a range of industries. It is for this reason that we have completed our product line by including an entry-level offer for C/C++ developers wanting to switch to Ada and reinforced our existing offer with GNAT Pro Assurance for programmers building the most robust software platforms with life cycles spanning decades.

#GNAT Pro    #Ada   

by J. German Rivera

Make with Ada 2017- A "Swiss Army Knife" Watch

SummaryThe Hexiwear is an IoT wearable development board that has two NXP Kinetis microcontrollers. One is a K64F (Cortex-M4 core) for running the main embedded application software. The other one is a KW40 (Cortex M0+ core) for running a wireless connectivity stack (e.g., Bluetooth BLE or Thread). The Hexiwear board also has a rich set of peripherals, including OLED display, accelerometer, magnetometer, gryroscope, pressure sensor, temperature sensor and heart-rate sensor. This blog article describes the development of a "Swiss Army Knife" watch on the Hexiwear platform. It is a bare-metal embedded application developed 100% in Ada 2012, from the lowest level device drivers all the way up to the application-specific code, for the Hexiwear's K64F microcontroller. I developed Ada drivers for Hexiwear-specific peripherals from scratch, as they were not supported by AdaCore's Ada drivers library. Also, since I wanted to use the GNAT GPL 2017 Ada compiler but the GNAT GPL distribution did not include a port of the Ada Runtime for the Hexiwear board, I also had to port the GNAT GPL 2017 Ada runtime to the Hexiwear. All this application-independent code can be leveraged by anyone interested in developing Ada applications for the Hexiwear wearable device.

by Yannick Moy , Martin Becker , Emanuel Regnath

Physical Units Pass the Generic Test

The support for physical units in programming languages is a long-standing issue, which very few languages have even attempted to solve. This issue has been mostly solved for Ada in 2012 by our colleagues Ed Schonberg and Vincent Pucci who introduced special aspects for specifying physical dimensions on types. This dimension system did not attempt to deal with generics though. As was noted by others, handling generics in a dimensional analysis that is, like in GNAT, a compile-time analysis with no impact on the executable size or running time, is the source of the problem of dimension handling. Together with our partners from Technical Universitat München, we have finally solved this remaining difficulty.

#GNAT     #typing   

by Yannick Moy

Proving Loops Without Loop Invariants

For all the power that comes with proof technology, one sometimes has to pay the price of writing a loop invariant. Along the years, we've strived to facilitate writing loop invariants by improving the documentation and the technology in different ways, but writing loops invariants remains difficult sometimes, in particular for beginners. To completely remove the need for loop invariants in simple cases, we have implemented loop unrolling in GNATprove. It turns out it is quite powerful when applicable.

#Formal Verification    #SPARK   

by Yannick Moy

Research Corner - Focused Certification of SPARK in Coq

The SPARK toolset aims at giving guarantees to its users about the properties of the software analyzed, be it absence of runtime errors or more complex properties. But the SPARK toolset being itself a complex tool, it is not free of errors. To get confidence in its results, we have worked with academic partners to establish mathematical evidence of the correctness of a critical part of the SPARK toolset. The part on which we focused is the tagging of nodes requiring run-time checks by the frontend of the SPARK technology. This work has been accepted at SEFM 2017 conference.

#SPARK   

by Yannick Moy

Applied Formal Logic: Searching in Strings

A friend pointed me to recent posts by Tommy M. McGuire, in which he describes how Frama-C can be used to functionally prove a brute force version of string search, and to find a previously unknown bug in a faster version of string search called quick search. Frama-C and SPARK share similar history, techniques and goals. So it was tempting to redo the same proofs on equivalent code in SPARK, and completing them with a functional proof of the fixed version of quick search. This is what I'll present in this post.

#Dev Projects    #Formal Verification    #SPARK   

by Rob Tice

The Adaroombot Project

The Adaroombot project consists of an iRobot CreateⓇ 2 and Ada running on a Raspberry Pi with a Linux OS. This is a great Intro-to-Ada project as it focuses on a control algorithm and a simple serial communications protocol. The iRobot CreateⓇ 2 platform was originally design for STEM education and has great documentation and support - making it very easy to create a control application using Ada. This blog looks at the creation of the project and some cool features of Ada that were learned along the way.

#Raspberry Pi    #ARM    #Linux    #Ada    #Roomba   

by Yannick Moy

Research Corner - FLOSS Glider Software in SPARK

Two years ago, we redeveloped the code of a small quadcopter called Crazyflie in SPARK, as a proof-of-concept to show it was possible to prove absence of run-time errors (no buffer overflows, not division by zero, etc.) on such code. The researchers Martin Becker and Emanuel Regnath have raised the bar by developing the code for the autopilot of a small glider in SPARK in three months only. Their paper and slides are available, and they have released their code as FLOSS for others to use/modify/enhance!

#Formal Verification    #Dev Projects    #SPARK   

by Yannick Moy

(Many) More Low Hanging Bugs

We reported in a previous post our initial experiments to create lightweight checkers for Ada source code, based on the new Libadalang technology. The two checkers we described discovered 12 issues in the codebase of the tools we develop at AdaCore. In this post, we are reporting on 6 more lightweight checkers, which have discovered 114 new issues in our codebase. This is definitely showing that these kind of checkers are worth integrating in static analysis tools, and we look forward to integrating these and more in our static analyzer CodePeer for Ada programs.

#Static Analysis    #Libadalang   

by Yannick Moy , Nicolas Roche

A Usable Copy-Paste Detector in A Few Lines of Python

After we created lightweight checkers based on the recent Libadalang technology developed at AdaCore, a colleague gave us the challenge of creating a copy-paste detector based on Libadalang. It turned out to be both easier than anticipated, and much more efficient and effective than we could have hoped for. In the end, we hope to use this new detector to refactor the codebase of some of our tools, and we expect to integrate it in our IDEs.

#Libadalang    #Static Analysis    #refactoring   

by Yannick Moy

VerifyThis Challenge in SPARK

This year again, the VerifyThis competition took place as part of ETAPS conferences. This is the occasion for builders and users of formal program verification platforms to use their favorite tools on common challenges. The first challenge this year was a good fit for SPARK, as it revolves around proving properties of an imperative sorting procedure. In this post, I am using this challenge to show how one can reach different levels of software assurance with SPARK.

#Formal Verification    #SPARK   

by Yannick Moy

GNATprove Tips and Tricks: Proving the Ghost Common Divisor (GCD)

Euclid's algorithm for computing the greatest common divisor of two numbers is one of the first ones we learn in school, and also one of the first algorithms that humans devised. So it's quite appealing to try to prove it with an automatic proving toolset like SPARK. It turns out that proving it automatically is not so easy, just like understanding why it works is not so easy. In this post, I am using ghost code to prove correct implementations of the GCD, starting from a naive linear search algorithm and ending with Euclid's algorithm.

#Formal Verification    #SPARK   

by Jorge Real

Writing on Air

While searching for motivating projects for students of the Real-Time Systems course here at Universitat Politècnica de València, we found a curious device that produces a fascinating effect. It holds a 12 cm bar from its bottom and makes it swing, like an upside-down pendulum, at a frequency of nearly 9 Hz. The free end of the bar holds a row of eight LEDs. With careful and timely switching of those LEDs, and due to visual persistence, it creates the illusion of text... floating in the air!

#STM32    #Ravenscar    #Ada    #Makers    #Embedded Development   

by Fabien Chouteau , Arnaud Charlet , Yannick Moy

SPARK Tetris on the Arduboy

One of us got hooked on the promise of a credit-card-size programmable pocket game under the name of Arduboy and participated in its kickstarter in 2015. The kickstarter was successful (but late) and delivered  the expected working board in mid 2016. Of course, the idea from the start was to program it in Ada , but this is an 8-bits AVR microcontroller (the ATmega32u4 by Atmel) not supported anymore by GNAT Pro. One solution would have been to rebuild our own GNAT compiler for 8-bit AVR from the GNAT FSF repository and use the AVR-Ada project. Another solution, which we explore in this blog post, is to use the SPARK-to-C compiler that we developed at AdaCore to turn our Ada code into C and then use the Arduino toolchain to compile for the Arduboy board.

by Claire Dross

Research Corner - Auto-active Verification in SPARK

GNATprove performs auto-active verification, that is, verification is done automatically, but usually requires annotations by the user to succeed. In SPARK, annotations are most often given in the form of contracts (pre and postconditions). But some language features, in particular ghost code, allow proof guidance to be much more involved. In a paper we are presenting at NASA Formal Methods symposium 2017, we describe how an imperative red black tree implementation in SPARK was verified using intensive auto-active verification.

#Formal Verification    #SPARK   

by Raphaël Amiard , Yannick Moy , Pierre-Marie de Rodat

Going After the Low Hanging Bug

At AdaCore, we have a strong expertise in deep static analysis tools (CodePeer and SPARK), and we have been relying on the compiler GNAT and our coding standard checker GNATcheck to deal with more syntactic or weakly-semantic checks. The recent Libadalang technology, developed at AdaCore, provided us with an ideal basis to develop specialized light-weight static analyzers. As an experiment, we implemented two simple checkers using the Python binding of Libadalang. The results on our own codebase were eye-opening: we found a dozen bugs in the codebases of the tools we develop at AdaCore (including the compiler and static analyzers).

#Static Analysis   

by Raphaël Amiard , Pierre-Marie de Rodat

Introducing Libadalang

AdaCore is working on a host of tools that works on Ada code. The compiler, GNAT, is the most famous and prominent one, but it is far from being the only one. At AdaCore, we already have several other tools to process Ada code: the ASIS library, GNAT2XML, the GPS IDE. A realization of the past years, however, has been that we were lacking a unified solution to process code that is potentially evolving, potentially incorrect Ada code. Hence Libadalang.

#Ada    #tooling   

by Claire Dross

Automatic Generation of Frame Conditions for Array Components

One of the most important challenges for SPARK users is to come up with adequate contracts and annotations, allowing GNATprove to verify the expected properties in a modular way. Among the annotations mandated by the SPARK toolset, the hardest to come up with are probably loop invariants. A previous post explains how GNATprove can automatically infer loop invariants for preservation of unmodified record components, and so, even if the record is itself nested inside a record or an array. Recently, this generation was improved to also support the simplest cases of partial array updates. We describe in this post in which cases GNATprove can, or cannot, infer loop invariants for preservation of unmodified array components.

#Formal Verification    #SPARK   

by Yannick Moy

GNATprove Tips and Tricks: What’s Provable for Real Now?

One year ago, we presented on this blog what was provable about fixed-point and floating-point computations (the two forms of real types in SPARK). Since then, we have integrated static analysis in SPARK, and modified completely the way floating-point numbers are seen by SMT provers. Both of these features lead to dramatic changes in provability for code doing fixed-point and floating-point computations.

#Formal Verification    #SPARK   

by Yannick Moy

Verified, Trustworthy Code with SPARK and Frama-C

Last week, a few of us at AdaCore have attended a one-day workshop organized at Thales Research and Technologies, around the topic of "Verified, trustworthy code - formal verification of software". Attendees from many different branches of Thales (avionics, railway, security, networks) were given an overview of the state-of-practice in formal verification of software, focused on two technologies: the SPARK technology that we develop at AdaCore for programs in Ada, and the Frama-C technology developed at CEA research labs for programs in C. The most interesting part of the day was the feedback given by three operational teams who have experimented during a few months with either SPARK (two teams) or Frama-C (one team). The lessons learned by first-time adopters of such technologies are quite valuable.

#SPARK    #Formal Methods   

by Claire Dross

Automatic Generation of Frame Conditions for Record Components

Formal verification tools like GNATprove rely on the user to provide loop invariants to describe the actions performed inside loops. Though the preservation of variables which are not modified in the loop need not be mentioned in the invariant, it is in general necessary to state explicitly the preservation of unmodified object parts, such as record fields or array elements. These preservation properties form the loop’s frame condition. As it may seem obvious to the user, the frame condition is unfortunately often forgotten when writing a loop invariant, leading to unprovable checks. To alleviate this problem, the GNATprove tool now generates automatically frame conditions for preserved record components. In this post, we describe this new feature on an example.

#Formal Verification    #SPARK   

by Pierre-Marie de Rodat

C library bindings: GCC plugins to the rescue

I recently started working on an Ada binding for the excellent libuv C library. This library provides a convenient API to perform asynchronous I/O under an event loop, which is a popular way to develop server stacks. A central part of this API is its enumeration type for error codes: most functions use it. Hence, one of the first things I had to do was to bind the enumeration type for error codes. Believe it or not: this is harder than it first seems!

#Code generation    #Ada   

by Yannick Moy

GNATprove Tips and Tricks: Using the Lemma Library

A well-know result of computing theory is that the theory of arithmetic is undecidable. This has practical consequences in automatic proof of programs which manipulate numbers. The provers that we use in SPARK have a good support for addition and subtraction, but much weaker support for multiplication and division. This means that as soon as the program has multiplications and divisions, it is likely that some checks won't be proved automatically. Until recently, the only way forward was either to complete the proof using an interactive prover (like Coq or Isabelle/HOL) or to justify manually the message about an unproved check. There is now a better way to prove automatically such checks, using the recent SPARK lemma library.

#Formal Verification    #SPARK   

by Yannick Moy

Formal Verification of Legacy Code

Just a few weeks ago, one of our partners reported a strange behavior of the well-known function Ada.Text_IO.Get_Line, which reads a line of text from an input file. When the last line of the file was of a specific length like 499 or 500 or 1000, and not terminated with a newline character, then Get_Line raised an exception End_Error instead of returning the expected string. That was puzzling for a central piece of code known to have worked for the past 10 years! But fair enough, there was indeed a bug in the interaction between subprograms in this code, in boundary cases having to do with the size of an intermediate buffer. My colleague Ed Schonberg who fixed the code of Get_Line had nonetheless the intuition that this particular event, finding such a bug in an otherwise trusted legacy piece of code, deserved a more in depth investigation to ensure no other bugs were hiding. So he challenged the SPARK team at AdaCore in checking the correctness of the patched version. He did well, as in the process we uncovered 3 more bugs.

#SPARK    #Legacy    #Formal Methods   

by Yannick Moy

GNATprove Tips and Tricks: What’s Provable for Real?

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.

#Formal Verification    #SPARK   

by Yannick Moy

SPARK 2014 Rationale: Support for Ravenscar

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.

#Language    #Formal Verification    #SPARK   

by David Hauzar

SPARK 16: Generating Counterexamples for Failed Proofs

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.

#Formal Verification    #SPARK   

by Yannick Moy

GNATprove Tips and Tricks: User Profiles

One of the most difficult tasks when using proof techniques is to interact with provers, in particular to progressively increase proof power until everything that should be proved is proved. Until the last release, increasing the proof power meant operating on three separate switches. There is now a simpler solution based on a new switch --level, together with a simpler proof panel in GPS for new users.

#Formal Verification    #SPARK   

by Yannick Moy

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.

#Dev Projects    #Formal Verification    #SPARK   

by Yannick Moy

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.

#Compilation    #Formal Verification    #SPARK   

by Claire Dross

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.

#Formal Verification    #SPARK   

by Clément Fumex

GNATprove Tips and Tricks: Bitwise Operations

The ProofInUse joint laboratory is currently improving the way SPARK deals with modular types and bitwise operators. Until now the SPARK tool was trying its best to translate those into equivalent operations on integers. It is now using native theory of smt-solvers when available resulting in much better support, and guaranteeing state of the art handling of bitwise operations. We present some examples in this post.

#Formal Verification    #SPARK   

by Olivier Ramonat

AdaCore Releases GNAT Pro 7.3, QGen 1.0 and GNATdashboard 1.0

February saw the annual customer release of a number of important products. This is no mean task when you consider the fact that GNAT Pro is available on over 50 platforms and supports over 150 runtime profiles (ranging from Full Ada Support to the very restricted Zero Footprint Profile suitable for safety-critical development). All in all, from the branching of the preview version to the customer release it takes us nearly 4 months to package everything up! Quality is assured through the internally developed AdaCore Factory.

#GNAT Pro    #SPARK Pro    #GPS    #GNATbench    #GNATdashboard    #Ada    #AdaCore Factory    #CodePeer    #QGen   

by Yannick Moy

GNATprove Tips and Tricks: Catching Mistakes in Contracts

Contracts may be quite complex, as complex as code in fact, so it is not surprising that they contain errors sometimes. GNATprove can help by pinpointing suspicious constructs that, although legal, do not make much sense. These constructs are likely to be caused by mistakes made by the programmer when writing the contract. In this post, I show examples of incorrect constructs that are signaled by GNATprove.

#Formal Verification    #Compilation    #SPARK   

by Tristan Gingold

AdaCore at FOSDEM'15

I was at Bruxelles on January 31st to present the components of GNAT GPL 2015 : SPARK 2014 and GNAT GPL for ARM bare-board. This is not unrelated to a previous blog entry on Tetris in SPARK on ARM Cortex M4, in particular I presented that Tetris demo (I brought some boards with me and despite the simple package, none were broken!). The slides contain technical details on the ravenscar profile (main principles), how to build a program for the stm32f4-discovery board and how to port the runtime. There are also less technical slides such as why we choose the stm32f4 board and photos of some graphical demos. As that could be useful to anyone interested in Ravenscar or in porting the runtime to other boards or other platforms, we've made the slides available here.

#ARM    #Ravenscar    #FOSDEM    #GNATGPL   

by Yannick Moy

SPARK 2014 Rationale: Object Oriented Programming

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.

#Language    #Formal Verification    #SPARK   

by Yannick Moy

SPARK 2014 Rationale: Ghost Code

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.

#Formal Verification    #SPARK   

by Yannick Moy

Using Coq to Verify SPARK 2014 Code

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.

#Formal Verification    #SPARK   

by Yannick Moy

Using SPARK to Prove AoRTE in Robot Navigation Software

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.

#Formal Verification    #SPARK    #Robotics   

by Claire Dross

External Axiomatizations: a Trip Into SPARK’s Internals

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.

#Formal Verification    #SPARK   

by Claire Dross

Manual Proof with Ghost Code in SPARK 2014

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.

#Formal Verification    #SPARK   

by Florian Schanda

SPARK 2014 Rationale: Information Flow

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.

#Formal Verification    #SPARK   

by Yannick Moy

GNATprove Tips and Tricks: How to Write Loop Invariants

Having already presented in previous posts why loop invariants are necessary for formal verification of programs with loops, and what loop invariants are necessary for various loops, we detail here a methodology for how users can come up with the right loop invariants for their loops. This methodology in four steps allows users to progressively add the necessary information in their loop invariants, with the tool GNATprove providing the required feedback at each step on whether the information provided is sufficient or not.

#Formal Verification    #SPARK   

by Yannick Moy

GNATprove Tips and Tricks: Referring to Input in Contracts

In a previous post about pre-call values, I described how the Ada language rules implemented in the compiler prevent surprises when referring to input values in the postcondition, using the Old attribute. Unfortunately, these rules also make it difficult to express some complex postconditions that may be useful when doing formal verification. In this post, I describe how contract cases allow the expression of these complex contracts, while still detecting potential problems with uses of the Old attribute.

#Language    #Formal Verification    #SPARK   

by Yannick Moy

SPARK 2014 Rationale: Global State

Global variables are a common source of programming errors: they may fail to be initialized properly, they can be modified in unexpected ways, sequences of modifications may be illegal, etc. SPARK 2014 provides a way to define abstractly the global state of a unit, so that it can be referred to in subprogram specifications. The associated toolset checks correct access to global variables in the implementation.

#Language    #Formal Verification    #SPARK   

by Yannick Moy

SPARK 2014 Rationale: Loop Invariants

Formal verification tools like GNATprove rely on two main inputs from programmers: subprogram contracts (preconditions and postconditions) and loop invariants. While the first ones are easy to understand (based on the "contract" analogy, in which a subprogram and its caller have mutual obligations), the second ones are not so simple to grasp. This post presents loop invariants and the choices we made in SPARK 2014.

#Language    #Formal Verification    #SPARK   

by Yannick Moy

SPARK 2014 Rationale: Pre-call and Pre-loop Values

Subprogram contracts are commonly presented as special assertions: the precondition is an assertion checked at subprogram entry, while the postcondition is an assertion checked at subprogram exit. A subtlety not covered by this simplified presentation is that postconditions are really two-state assertions: they assert properties over values at subprogram exit and values at subprogram entry. A special attribute Old is defined in Ada 2012 to support these special assertions. A special attribute Loop_Entry is defined in SPARK 2014 to support similar special assertions for loops.

#Formal Verification    #SPARK