This paper introduces reAnalyst, a framework designed to facilitate the study of reverse engineering (RE) practices through the semi-automated annotation of RE activities across various RE tools. By integrating tool-agnostic data collection of screenshots, keystrokes, active processes, and other types of data during RE experiments with semi-automated data analysis and generation of annotations, reAnalyst aims to overcome the limitations of traditional RE studies that rely heavily on manual data collection and subjective analysis. The framework enables more efficient data analysis, which will in turn allow researchers to explore the effectiveness of protection techniques and strategies used by reverse engineers more comprehensively and efficiently. Experimental evaluations validate the framework's capability to identify RE activities from a diverse range of screenshots with varied complexities. Observations on past experiments with our framework as well as a survey among reverse engineers provide further evidence of the acceptability and practicality of our approach.

翻译：本文介绍reAnalyst框架，该框架旨在通过跨多种逆向工程工具的半自动化逆向工程活动标注，促进逆向工程实践研究。reAnalyst在逆向工程实验中集成工具无关的屏幕截图、击键记录、活动进程及其他类型数据的采集，结合半自动化数据分析与标注生成，旨在克服传统逆向工程研究严重依赖人工数据收集和主观分析的局限性。该框架支持更高效的数据分析，从而使研究人员能够更全面、更有效地探究逆向工程师所用保护技术与策略的有效性。实验评估验证了该框架从复杂度各异的多样化屏幕截图中识别逆向工程活动的能力。基于本框架的过往实验观察以及对逆向工程师的调研，进一步证明了本方法的可接受性与实用性。