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Proving software correct

May 2nd, 2011 2 comments

Users want confidence that software is ‘correct'; what constitutes correct depends on who you talk to and can vary between doing what the user expects and behaving according to a specification (which may include behavior that users did not expect or want).

The gold standard for software correctness is that achieved by mathematical proofs, or at least what most people believe is achieved by such proofs, i.e., a statement that is shown through a sequence of steps to be derived from a set of axioms. The sequence of steps used in most real proofs operate at a much higher level than axioms and rely on the reader to fill in the gaps left between each step. Ever since theorems were first stated they sometimes contained faults, i.e., were not correct theorems, and as mathematicians have continued to increase the size and complexity of theorems being ‘proved’ the technical and social issues involved in believing a published proof have grown in complexity.

Software proofs usually operate by translating the source in to some mathematical formalism and using a theorem prover to show that one or more properties are met. Perhaps the most famous use of such a proof that had an outcome different than that predicted is the 1996 Ariane 5 rocket crash; various proofs had been obtained for the Ariane 4 software showing that the value of some variables would never exceed given limits, these proofs involved input values that depended on the performance of the rocket and because Ariane 5 was more powerful than Ariane 4 the proofs were no longer valid (management would have found this out had they recheck the proofs using the larger values). Update: My only knowledge of this work comes from a conversation I recall with somebody working in the formal verification area, I no longer have contact with them and the company they worked for no longer exists; Pascal Cuoq’s comment below suggests they may have overstated the formal nature of the work, I have no means of double checking.

Purveyors of ‘software proof’ systems will tell you about the importance of feeding in the correct input values and will tell you about the known proofs they have managed to verify using their system. The elephant in the room that rarely gets mentioned is the correctness of the program that translates source code into the mathematical formalism used. These translators often handle that subset of the language which is relatively easy to map to the target formalism, the MALPAS C to IL translator is one exception to this (ok, yes my company wrote this translator so the opinion might be a little biased).

The method commonly associated with claims of correctness proof for a translator or compiler is slightly different from that described above for applications. This method involves manually writing some mathematics, using the chosen formalism, that ‘implements’ the translator/compiler. Strangely there are people who think that doing this is sufficient to claim the compiler is ‘verified’ or ‘proved correct’. As any schoolboy knows it is possible to write mathematics that contains mistakes and the writing of a mathematical implementation is just the first step in a process intended to increase confidence in a claim of correctness.

One of the questions that might be asked of a ‘mathematics implementation’ of a compiler is: does it faithfully interpret source code syntax/semantics according to the syntax/semantics specified in the appropriate language document?

Answering this question requires that the language syntax/semantics be specified in some mathematical notation that is amenable to formal analysis. Various researchers have created mathematical models for languages such as Ada, CHILL and C. However, these models are not recognized as being definitive, that status belongs to the corresponding ISO Standard written in English prose. The Modula-2 standard is specified using both English prose and equivalent mathematical notation with both having equal status as the definition of the language (any inconsistency between the two is decided why analyzing what behavior was intended); there were lots of plans to do stuff with this mathematics but the ISO language committee struggled just to produce a tool capable of printing the mathematics.

The developers of the Compcert system refer to it as a formally verified C compiler front-end when the language actually verified is called Clight, which they describe as a subset of the C language. This is very interesting work and I hope they continue to refine it and add support for more C-like constructs. But let’s be clear, the one thing missing from this project is any proof of a connection to the requirements contained in the C Standard.

I don’t know what it is about formal verification but those involved can at the same time be both very particular about the language they use in their mathematics and completely over the top in the claims they make about what their tools do. A speaker from Polyspace at one MISRA C conference claimed his tool could detect 100% of the coding guidelines specified in MISRA C, a surprising achievement for a runtime tool (as it was then) enforcing requirements mainly aimed at source code; I eventually got him to agree that the tool detected 100% of the constructs specified by the small subset of guidelines they had implemented.

I doubt that the Advertising Standard Authority would allow adverts containing the claims made by some formal verification advocates to appear in print or on TV; if soap manufacturers have to follow ASA rules then so should formal verification researchers.

Without a language specification written in a form amenable to mathematical analysis any claims of correctness have to be based on the traditional means of reading English prose very carefully and writing lots of tests to probe every obscure corner of the language specification. This was the approach used for the production of the Model Implementation of C, a system designed to detect all unspecified, implementation defined and undefined uses in C programs (it used a compiler, linker and interpreter). One measure of how well an implementor has studied the standard is how many faults they have discovered in it (some people claim this is a quality of standard issue, but the similar number of defects reported against the Ada and C Standards show that at least for Ada this is not true); here are some from the Model Implementation project.

Performance on independently written tests can be a good indicator of implementation correctness, depending on the quality of the tests. Both the Perennial and PlumHall C validation suites are of high quality, while suites such as the gcc testsuite are rather ad-hoc, have poor coverage and tend to be runtime oriented. The problem with high quality validation suites is that they cost enough money to put them out of reach of many research groups (I suspect another problem is that such groups don’t understand the benefits of using such suites or think they can do just as good a job in a few weeks).

Recently a new formal verification tool for C has appeared that performs all its verification checking at program runtime, i.e., after the user source has been translated to executable form. It is still very early days for kcc (they have yet to chose a name and the command used to invoke the translator is currently being used), they have an initial system up and running and are keen to continue improving it.

I am interested in the system because of what it might evolve into, including:

  • a means of quickly checking the behavior of obscure bits of code (I get asked all sorts of weird questions and my brain is not always willing to switch to C language lawyer mode),
  • a means of checking the consistency of the requirements in the C Standard, which will require another tool making use of the formalism built up by kcc,
  • a tool which would help developers understand which parts of the C Standard they need to look at to understand some construct (the tool currently has a trace mode that needs lots of work).