Building checkers with the Infer.AI framework

Infer.AI is a framework for quickly developing abstract interpretation-based checkers (intraprocedural or interprocedural). You define only:

1. An abstract domain (type of abstract state plus <=, join, and widen operations)
2. Transfer functions (a transformer that takes an abstract state as input and produces an abstract state as output)

What you get in exchange is an analysis that can run on all of the languages Infer supports (C, Objective-C, C++, and Java)!

This guide covers how to use the framework. For background on why we built the framework and how it works, check out these slides from a PLDI 2017 tutorial and this talk from @Scale2016.

If you feel like coding instead of reading, a great way to get started with Infer.AI is to go through the lab exercise here.

By example: intraprocedural analysis

This section helps you get started ASAP if you already understand abstract interpretation (or don't, but are feeling bold).

Take a look at liveness.ml. This code is performing a compilers-101 style liveness analysis over SIL, Infer's intermediate language. Since this code is fairly small and you should already understand what it's trying to do, it's a fairly good place to look in order to understand both how to use the abstract interpretation framework and what SIL is.

There are basically three important bits here: defining the domain, defining the transfer functions, and then passing the pieces to the framework to create an analysis. Let's break down the third bit:

module CFG = ProcCfg.OneInstrPerNode (ProcCfg.Backward (ProcCfg.Exceptional))
module CheckerAnalyzer =
  AbstractInterpreter.MakeRPO (TransferFunctions (CheckerMode) (CFG))

The ProcCfg.Backward (ProcCfg.Exceptional) part says: "I want the direction of iteration to be backward" (since liveness is a backward analysis), and "I want the analysis to follow exceptional edges". For a forward analysis that ignores exceptional edges, you would do ProcCfg.Normal instead (and many other combinations are possible; take a look at ProcCfg.mli for more). And finally, the TransferFunctions part says "Use the transfer functions I defined above".

Now you have a CheckerAnalyzer module that exposes useful functions like compute_post (take a procedure as input and compute a postcondition) and exec_pdesc (take a procedure and compute an invariant map from node id's to the pre/post at each node). The next step is to hook your checker up to the Infer command-line interface (CLI). For the liveness analysis, you would do this by exposing a function for running the checker on a single procedure:

let checker ({IntraproceduralAnalysis.proc_desc; err_log} as analysis_data) =
  match Analyzer.compute_post analysis_data ~initial:Domain.empty with
  | Some post ->
      Logging.progress "Computed post %a for %a"
        Domain.pp post Procname.pp (Procdesc.get_proc_name proc_desc);
  | None -> ()

and then adding Liveness.checker to the list of registered checkers in registerCheckers.ml (search for "Liveness").

you can then run infer run --liveness-only -- <your_build_command> to run your checker on real code. See here for more details on the build systems supported by Infer.

Other examples of simple intraprocedural checkers are addressTaken.ml and Siof.ml.

Error reporting

Useful analyses have output. Basic printing to stderr or stderr is good for debugging, but to report a programmer-readable error that is tied to a source code location, you'll want to use Reporting.log_issue.

By example: interprocedural analysis

Let's assume you have already read and understood the "intraprocedural analysis" section and have an intraprocedural checker. The abstract interpretation framework makes it easy to convert your intraprocedural analysis into a modular interprocedural analysis. Let me emphasize the modular point once more; global analyses cannot be expressed in this framework.

To make your checker interprocedural, you need to:

1. Define the type of procedure summaries for your analysis and let registerCheckers.ml know that your checker is interprocedural

2. Add logic for (a) using summaries in your transfer functions and (b) converting your intraprocedural abstract state to a summary.

A good example to look at here is Siof.ml. Step (1) is just:

(* in src/checkers/SiofDomain.ml *)
(* note that as a result the type of summaries is the same as the type of domain
   elements *)
module Summary = ...
include Summary


(* in src/backend/Payloads.ml: register the payload of the analyzer *)
type t =
  { ...
  ; siof: SiofDomain.Summary.t option
  ... }


(* in src/backend/registerCheckers.ml *)
let all_checkers = [ ...
  ; {checker= SIOF; callbacks= [(interprocedural Payloads.Fields.siof Siof.checker, Clang)]}
  ... ]

Here, the type of the abstract state and the type of the summary are the same, which makes things easier for us (no logic to convert an abstract state to a summary).

Part (2a) is here and uses the analyze_dependency callback provided by the framework:

match analyze_dependency callee_pname with

This says: "read the summary for callee_pname, possibly computing it first". You must then add logic for applying the summary to the current abstract state (often, this is as simple as doing a join).

Because our summary type is the same as the abstract state, part (2b) here simply consists in return the post computed by the analysis as the procedure's summary, using Analyzer.compute_post.

That's it! We now have an interprocedural analysis.

To go deeper, jump to the lab exercise and to the API documentation, e.g. for the Absint and IR modules.