The first module of the free SPARK course on AdaCore U e-learning website (out of five modules) is now available in Japanese, courtesy of Mr. Masao Ito.
Next Monday, Bill Wong from Electronic Design will host a free webcast on "Building High-Assurance Software without Breaking the Bank", with SPARK experts Rod Chapman and myself as presenters. You can already register for the event.
We report on the creation of the first lemma of a new lemma library on arrays: a lemma on transitivity of the order in arrays.
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.
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.
Researchers Carl Brandon and Peter Chapin recently presented during conference HILT 2016 their ongoing work to build a micro satellite called Lunar IceCube that will map water vapor and ice on the moon. In their paper, they explain how the use of proof with SPARK is going to help them get perfect software in the time and budget available.
Tasking was one of the big features introduced in the previous release of SPARK 2014. However, GNATprove only supported tasking-related constructs allowed by the Ravenscar profile. Now it also supports the more relaxed GNAT Extended Ravenscar profile.
Type invariants are used to model properties that should always hold for users of a data type but can be broken inside the data type implementation. Type invariant are part of Ada 2012 but were not supported in SPARK until SPARK Pro 17.
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?
The repository on the open-do forge is now obsolete. SPARK2014 and its components Why3, Alt-Ergo, CVC4 and Z3 are available on github.