IRE: A Framework For Inductive Reverse Engineering

abstract: Reverse engineering is critical to reasoning about how a system behaves. While complete access to a system inherently allows for perfect analysis, partial access is inherently uncertain. This is the case foran individual agent in a distributed system. Inductive Reverse Engineering (IRE) enables analysis under such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space. Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system. While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant. IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques.Dissertation/ThesisMasters Thesis Computer Science 201

Theoretical Underpinnings of Version Spaces

Mitchell's version-space approach to inductive concept learning has been highly influential in machine learning, as it formalizes inductive concept learning as a search problem—to identify some concept definition out of a space of possible definitions. This paper lays out some theoretical underpinnings of version spaces. It presents the conditions under which an arbitrary set of concept definitions in a concept description language can be represented by boundary sets, which is a necessary condition for a set of concept definitions to be a version space. Furthermore, although version spaces can be intersected and unioned (version spaces are simply sets, albeit with special structure), the result need not be a version space; this paper also presents the conditions under which such intersection and union of two version spaces yields a version space (i.e., representable by boundary sets). Finally, the paper shows how the resulting boundary sets after intersections and unions can be computed from the initial boundary sets, and proves the algorithms correct.