We present PolyTracker, a whole-program, whole-input dynamic information flow tracing (DIFT) framework. Given an LLVM compatible codebase or a binary that has been lifted to LLVM intermediate representation (IR), PolyTracker compiles it, adding static instrumentation. The instrumented program will run normally with modest performance overhead, but will additionally output a runtime trace artifact in the co-designed TDAG (Tainted Directed Acyclic Graph) format. TDAGs can be post-processed for a variety of analyses, including tracing every input byte through program execution. TDAGs can be generated either by running the program over a corpus of inputs or by employing a randomized input generator such as a fuzzer. PolyTracker traces (TDAGs) are useful not only for very localized, targeted dynamic program analysis as with smaller-scale DIFT: TDAGs are primarily intended for whole-program runtime exploration and bug finding, granular information-flow diffing between program runs, and comparisons of implementations of the same input specification without any need to emulate and instrument the entire running environment. For user-friendliness and reproducibility, the software repository provides a number of examples of PolyTracker-instrumented builds of popular open-source software projects. We also provide an analysis library and REPL written in Python that are designed to assist users with operating over TDAGs.

The recent growth of agent-based software systems was achieved without the development of a reference architecture. From a software engineering standpoint, a reference architecture is necessary to compare, evaluate, and integrate past, current, and future agent-based software systems. The agent systems reference architecture (ASRA) advances the agent-based system development process by providing a set of key interaction patterns for functional areas that exist between the layers and protocols of agent-based systems. Furthermore, the ASRA identifies the points for interoperability between agent-based systems and increases the level of discussion when referring to agent-based systems. This paper presents methodology, grounded in software forensics, to develop the ASRA and provides an overview of the resulting architectural representation. The methodology uses an approach based on software engineering techniques adapted to study agent frameworks-the libraries and tools for building agent systems. The resulting ASRA can serve as an abstract representation of the components necessary for facilitating comparison, integration, and interoperation of software systems composed of agents.

Agent-based systems have been the object of intense research over the past decade. While great theoretical progress has been made, the software frameworks for creating agent-based systems offer considerable variability in their capabilities and functionality. This paper introduces a reference model for agent-based systems. The purpose of a reference model is to provide a common conceptual basis for comparing systems and driving the development of software architectures and other standards. The Foundation for Intelligent Physical Agents and other groups have advanced a number of agent standards, yet, to date, no comprehensive reference model has been presented for software systems composed of agents. This paper provides an overview of a reference model for agent-based systems. The agent systems reference model is the result of a multiyear effort studying software systems built with agents and software frameworks for implementing these systems. As part of this study, the team applied software reverse engineering techniques to perform static and dynamic analysis of operational agent-based systems. This analysis enabled identification of key common concepts across over one dozen different agent frameworks. To demonstrate its applicability, the reference model is then used to analyze a number of complete agent-based software systems. It is the belief of the authors that the reference model will be an essential prerequisite for future transition, deployment, and integration of agent-based systems.