AdaCore Blog

84 entries tagged with #GUI

by Yannick Moy , Claire Dross

Proving the Correctness of GNAT Light Runtime Library

The GNAT light runtime library is a version of the runtime library targeted at embedded platforms and certification, which has been certified for use at the highest levels of criticality in several industrial domains. It contains around 180 units focused mostly on I/O, numerics, text manipulation, memory operations. We have used SPARK to prove the correctness of 40 of them: that the code is free of runtime errors, and that it satisfies its functional specifications.

#SPARK    #Runtime    #Proof   

by Paul Butcher

Fuzz Testing in International Aerospace Guidelines

Through the HICLASS UK research group, AdaCore has been developing security-focused software development tools that are aligned with the objectives stated within the avionics security standards. In addition, they have been developing further guidelines that describe how vulnerability identification and security assurance activities can be described within a Plan for Security Aspects of Certification.

#Fuzzing    #Cyber Security    #Civil Avionics    #DO-356A    #ED-203A   

by Kyriakos Georgiou

Security-Hardening Software Libraries with Ada and SPARK

Part of AdaCore's ongoing efforts under the HICLASS project is to demonstrate how the SPARK technology can play an integral part in the security-hardening of existing software libraries written in other non-security-oriented programming languages such as C. This blog post presents the first white paper under this work-stream, “Security-Hardening Software Libraries with Ada and SPARK”.

#SPARK    #STM32    #Embedded   

by Jessie Glockner

Celebrating Women Engineering Heroes - International Women in Engineering Day 2021

Women make up roughly 38% of the global workforce, yet they constitute only 10–20% of the engineering workforce. In the U.S., numbers suggest that 40% of women who graduate with engineering degrees never enter the profession or eventually leave it. Why? The reasons vary but primarily involve socio-economic constraints on women in general, workplace inequities, and lack of support for work-life balance. Sadly, history itself has often failed to properly acknowledge the instrumental contributions of women inventors, scientists, and mathematicians who have helped solve some of our world's toughest challenges. How can young women emulate their successes if they don't even know about them?

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 Claire Dross

Relaxing the Data Initialization Policy of SPARK

SPARK always being under development, new language features make it in every release of the tool, be they previously unsupported Ada features (like access types) or SPARK specific developments. However, new features generally take a while to make it into actual user code. The feature I am going to present here is in my experience an exception, as it was used both internally and by external users before it made it into any actual release. It was designed to enhance the verification of data initialization, whose limitations have been a long standing issue in SPARK.

#Formal Verification    #SPARK   

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 Joffrey Huguet

Using SPARK to prove 255-bit Integer Arithmetic from Curve25519

In 2014, Adam Langley, a well-known cryptographer from Google, wrote a post on his personal blog, in which he tried to prove functions from curve25519-donna, one of his projects, using various verification tools: SPARK, Frama-C, Isabelle... He describes this attempt as "disappointing", because he could not manage to prove "simple" things, like absence of runtime errors. I will show in this blogpost that today, it is possible to prove what he wanted to prove, and even more.

#SPARK    #Formal Verification    #Cryptography   

by Yannick Moy , Nicolas Setton , Ben Brosgol

A Readable Introduction to Both MISRA C and SPARK Ada

MISRA C is the most widely known coding standard restricting the use of the C programming language for critical software. For good reasons. For one, its focus is entirely on avoiding error-prone programming features of the C programming language rather than on enforcing a particular programming style. In addition, a large majority of rules it defines are checkable automatically (116 rules out of the total 159 guidelines), and many tools are available to enforce those. As a coding standard, MISRA C even goes out of its way to define a consistent sub-language of C, with its own typing rules (called the "essential type model" in MISRA C) to make up for the lack of strong typing in C.

#MISRA-C    #SPARK    #Safety    #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 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 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

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 Yannick Moy

New Guidance for Adoption of SPARK

While SPARK has been used for years in companies like Altran UK, companies without the same know-how may find it intimidating to get started on formal program verification. To help with that process, AdaCore has collaborated with Thales throughout the year 2016 to produce a 70-pages detailed guidance document for the adoption of SPARK. These guidelines are based on five levels of assurance that can be achieved on software, in increasing order of costs and benefits: Stone level (valid SPARK), Bronze level (initialization and correct data flow), Silver level (absence of run-time errors), Gold level (proof of key properties) and Platinum level (full functional correctness). These levels, and their mapping to the Development Assurance Levels (DAL) and Safety Integrity Levels (SIL) used in certification standards, were presented at the recent High Confidence Software and Systems conference.

#Formal Verification    #SPARK   

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 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 Yannick Moy

New Year's Resolution for 2017: Use SPARK, Say Goodbye to Bugs

​NIST has recently published a report called "Dramatically Reducing Software Vulnerabilities"​ in which they single out five approaches which have the potential for creating software with 100 times fewer vulnerabilities than we do today. One of these approaches is formal methods. Among formal methods, the report highlights strong suits of SPARK, and cites SPARK projects as example of mature uses of formal methods. NIST is not the only ones to support the use of SPARK. Editor Bill Wong from Electronic Design has included SPARK in his "2016 Gifts for the Techie". So if your new year's resolutions include software without bugs, have a look at SPARK in 2017.

#VerificationTools    #Formal Methods    #SPARK   

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 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

Did SPARK 2014 Rethink Formal Methods?

David Parnas is a well-known researcher in formal methods, who famously contributed to the analysis of the shut-down software for the Darlington nuclear power plant and designed the specification method known as Parnas tables and the development method called Software Cost Reduction. In 2010, the magazine CACM asked him to identify what was preventing more widespread adoption of formal methods in industry, and in this article on Really Rethinking Formal Methods he listed 17 areas that needed rethinking. The same year, we started a project to recreate SPARK with new ideas and new technology, which lead to SPARK 2014 as it is today. Parnas's article influenced some critical design decisions. Six years later, it's interesting to see how the choices we made in SPARK 2014 address (or not) Parnas's concerns.

#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

The Eight Reasons For Using SPARK

Based on our many years of experience with our customers using SPARK in their projects, we have come up with a list of eight objectives that are most commonly targeted when using SPARK. Most projects only target a few of them, but in theory one could try to achieve all of them with SPARK on a project. This list may be useful for those who want to assess if the SPARK technology can be of benefit in their context, and to existing SPARK users to compare their existing practice with what others do.

#Formal Verification    #Design Method    #Certification    #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 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 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 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 Yannick Moy

Use of SPARK in a Certification Context

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.

#Formal Verification    #Certification   

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

Case Study for System to Software Integrity Includes SPARK 2014

My colleague Matteo Bordin will present at the upcoming Embedded Real Time Software and Systems conference in Toulouse in February a case study showing how formal verification with SPARK can be included in a larger process to show preservation of properties from the system level down to the software level. The case study is based on the Nose Gear challenge from the Workshop on Theorem Proving in Certification.

#Formal Verification    #Certification    #SPARK