Disassembly is the first step of a variety of binary analysis and transformation techniques, such as reverse engineering, or binary rewriting. Recent disassembly approaches consist of three phases: an exploration phase, that overapproximates the binary's code; an analysis phase, that assigns weights to candidate instructions or basic blocks; and a conflict resolution phase, that downselects the final set of instructions. We present a disassembly algorithm that generalizes this pattern for a wide range of architectures, namely x86, x64, arm32, and aarch64. Our algorithm presents a novel conflict resolution method that reduces disassembly to weighted interval scheduling. Additionally, we present a weight assignment algorithm that allows us to learn optimal weights for the various disassembly heuristics in the analysis phase. Learned weights outperform manually tuned weights in most cases while reducing the number of necessary heuristics by 40% (by setting their weights to zero). Our implementation, built on top of Ddisasm, outperforms state-of-the-art disassemblers in several metrics and achieves the largest proportion of perfectly disassembled binaries by a wide margin in all evaluated datasets.

This article presents a may-happen-in-parallel (MHP) analysis for languages with actor-based concurrency . In this concurrency model, actors are the concurrency units such that, when a method is invoked on an actor a 2 from a task executing on actor a 1 , statements of the current task in a 1 may run in parallel with those of the (asynchronous) call on a 2 , and with those of transitively invoked methods. The goal of the MHP analysis is to identify pairs of statements in the program that may run in parallel in any execution. Our MHP analysis is formalized as a method-level ( local ) analysis whose information can be modularly composed to obtain application-level ( global ) information. The information yielded by the MHP analysis is essential to infer more complex properties of actor-based concurrent programs, for example, data race detection, deadlock freeness, termination, and resource consumption analyses can greatly benefit from the MHP relations to increase their accuracy. We report on MayPar, a prototypical implementation of an MHP static analyzer for a distributed asynchronous language.

We present the concepts, usage and prototypical implementation of MayPar, a may-happen-in-parallel (MHP) static analyzer for a distributed asynchronous language based on concurrent objects. Our tool allows analyzing an application and finding out the pairs of statements that can execute in parallel. The information can be displayed by means of a graphical representation of the MHP analysis graph or, in a textual way, as a set of pairs which identify the program points that may run in parallel. The information yield by MayPar can be relevant (1) to spot bugs in the program related to fragments of code which should not run in parallel and also (2) to improve the precision of other analyses which infer more complex properties (e.g., termination and cost).