AdaCore Blog

An Insight Into the AdaCore Ecosystem

by Paul Butcher
Fuzz Testing in International Aerospace Guidelines

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 Emma Adby
Welcoming New Members to the GNAT Pro Family

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   

Physical Units Pass the Generic Test

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

Research Corner - Floating-Point Computations in SPARK

It is notoriously hard to prove properties of floating-point computations, including the simpler bounding properties that state safe bounds on the values taken by entities in the program. Thanks to the recent changes in SPARK 17, users can now benefit from much better provability for these programs, by combining the capabilities of different provers. For the harder cases, this requires using ghost code to state intermediate assertions proved by one of the provers, to be used by others. This work is described in an article which was accepted at VSTTE 2017 conference.

#Formal Verification    #SPARK   

by Yannick Moy
New Guidance for Adoption of SPARK

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 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