AdaCore Blog

64 entries tagged with #FACE

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 Pat Rogers

Making an RC Car with Ada and SPARK

As a demonstration for the use of Ada and SPARK in very small embedded targets, I created a remote-controlled (RC) car using Lego NXT Mindstorms motors and sensors but without using the Lego computer or Lego software. I used an ARM Cortex System-on-Chip board for the computer, and all the code -- the control program, the device drivers, everything -- is written in Ada. Over time, I’ve upgraded some of the code to be in SPARK. This blog post describes the hardware, the software, the SPARK upgrades, and the repositories that are used and created for this purpose.

#Ada    #SPARK    #Robotics   

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 Yannick Moy

​Amazon Relies on Formal Methods for the Security of AWS

Byron Cook, who founded and leads the Automated Reasoning Group at Amazon Web Services (AWS) Security, gave a powerful talk at the Federated Logic Conference in July about how Amazon uses formal methods for ensuring the security of parts of AWS infrastructure. In the past four years, this group of 20+ has progressively hired well-known formal methods experts to face the growing demand inside AWS to develop tools based on formal verification for reasoning about cloud security. What is unique so far is the level of investment at AWS in formal verification as a means to radically eliminate some security problems, both for them and for their customers. This is certainly an approach we're eager to support with our own investment in the SPARK technology​.

#Formal Verification    #Cloud    #Security   

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 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 Jonas Attertun

Make with Ada 2017: Brushless DC Motor Controller

This project involves the design of a software platform that provides a good basis when developing motor controllers for brushless DC motors (BLDC/PMSM). It consist of a basic but clean and readable implementation of a sensored field oriented control algorithm. Included is a logging feature that will simplify development and allows users to visualize what is happening. The project shows that Ada successfully can be used for a bare-metal project that requires fast execution.

#Makers    #MakewithAda    #STM32    #embedded   

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 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 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 Florian Schanda

SPARKSMT - An SMTLIB Processing Tool Written in SPARK - Part I

Today I will write the first article in a short series about the development of an SMTLIB processing tool in SPARK. Instead of focusing on features, I intend to focus on the how I have proved absence of run-time errors in the name table and lexer. I had two objectives: show absence of run-time errors, and do not write useless defensive code. Today's blog will be about the name table, a data structure found in many compilers that can map strings to a unique integer and back. The next blog post will talk about the lexical analyzer.

#Dev Projects    #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 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 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