AdaCore Blog

50 entries tagged with #Static Analysis

by Quentin Ochem , Florian Gilcher

AdaCore and Ferrous Systems Joining Forces to Support Rust

For over 25 years, AdaCore has been committed to supporting the needs of safety- and mission-critical industries. This started with an emphasis on the Ada programming language and its toolchain, and over the years has been extended to many other technologies. AdaCore’s product offerings today include support for the Ada language and its formally verifiable SPARK subset, C and C++, and Simulink and Stateflow models. We have accomplished this while addressing the requirements of various safety standards such as DO-178B/C, EN 50128, ECSS-E-ST-40C / ECSS-Q-ST-80C, IEC 61508 and ISO 26262.

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 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.


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 Abe Cohen

An Introduction to Contract-Based Programming in Ada

One of the most powerful features of Ada 2012* is the ability to specify contracts on your code. Contracts describe conditions that must be satisfied upon entry (preconditions) and upon exit (postconditions) of your subprogram. Preconditions describe the context in which the subprogram must be called, and postconditions describe conditions that will be adhered to by the subprogram’s implementation. If you think about it, contracts are a natural evolution of Ada’s core design principle. To encourage developers to be as explicit as possible with their expressions, putting both the compiler/toolchain and other developers in the best position to help them develop better code.

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 Yannick Moy , Roderick Chapman

How Ada and SPARK Can Increase the Security of Your Software

There is a long-standing debate about which phase in the Software Development Life Cycle causes the most bugs: is it the specification phase or the coding phase? A recent study by NIST shows that, in the software industry at large, coding bugs are causing the majority of security issues. Choosing a safer language like Ada or SPARK is a critical component for reducing these vulnerabilities that result from simple mistakes. In a new freely available booklet, we explain how these languages and the associated toolsets can be used to increase the security of software.

#Ada    #SPARK    #Security   

by Yannick Moy

Two Days Dedicated to Sound Static Analysis for Security

​AdaCore has been working with CEA, Inria and NIST to organize a two-days event dedicated to sound static analysis techniques and tools, and how they are used to increase the security of software-based systems. The program gathers top-notch experts in the field, from industry, government agencies and research institutes, around the three themes of analysis of legacy code, use in new developments and accountable software quality. Here is why it is worth attending.

#SPARK    #Frama-C    #Security    #Formal Methods    #Static Analysis   

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

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.


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 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 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 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

The Most Obscure Arithmetic Run-Time Error Contest

Something that many developers do not realize is the number of run-time checks that occur in innocent looking arithmetic expressions. Of course, everyone knows about overflow checks and range checks (although many people confuse them) and division by zero. After all, these are typical errors that do show up in programs, so programmers are aware that they should keep an eye on these. Or do they?

#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

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 Jamie Ayre

Verification on Ada code with Static and Dynamic Code Analysis - Webinar

One of the main challenges to get certification in Ada projects is the achievement of 100% code coverage but in most projects an amount of more than 95% structural coverage is hard to achieve. What can you do with the last 5% of code that can't be covered? DO-178C for example, provides a framework for the integration of various techniques in the development process to solve the problem. In this webinar you learn how static analysis and dynamic testing can help complete analysis for pieces of code that are not covered.

#CodePeer    #Code Coverage    #Dynamic Analysis    #Static Analysis    #DO-178    #DO-178C   

by Yannick Moy

A Building Code for Building Code

In a recent article in Communications of the ACM, Carl Landwehr, a renowned scientific expert on security, defends the view that the software engineering community is doing overall a poor job at securing our global information system and that this is mostly avoidable by putting what we know works to work, to the point that most vulnerabilities could be completely avoided by design if we cared enough. Shocking! Or so it should appear.

#Ada    #SPARK    #Static Analysis    #Security   

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

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 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