Background. Coping with the rapid growing complexity in contemporary software architecture, tracing has become an increasingly critical practice and been adopted widely by software engineers. By adopting tracing tools, practitioners are able to monitor, debug, and optimize distributed software architectures easily. However, with excessive number of valid candidates, researchers and practitioners have a hard time finding and selecting the suitable tracing tools by systematically considering their features and advantages.Objective. To such a purpose, this paper aims to provide an overview of popular Open tracing tools via comparison. Method. Herein, we first identified \ra{30} tools in an objective, systematic, and reproducible manner adopting the Systematic Multivocal Literature Review protocol. Then, we characterized each tool looking at the 1) measured features, 2) popularity both in peer-reviewed literature and online media, and 3) benefits and issues. We used topic modeling and sentiment analysis to extract and summarize the benefits and issues. Specially, we adopted ChatGPT to support the topic interpretation. Results. As a result, this paper presents a systematic comparison amongst the selected tracing tools in terms of their features, popularity, benefits and issues. Conclusion. The result mainly shows that each tracing tool provides a unique combination of features with also different pros and cons. The contribution of this paper is to provide the practitioners better understanding of the tracing tools facilitating their adoption.

翻译：背景：应对当代软件架构日益增长的复杂性，追踪已成为一项日益关键的实践并被软件工程师广泛采用。通过采用追踪工具，从业者能够轻松监控、调试和优化分布式软件架构。然而，由于存在数量过多的有效备选方案，研究人员和从业者难以通过系统考量其特性与优势来发现和选择适用的追踪工具。目标：为此，本文旨在通过比较概述流行的开放追踪工具。方法：本文首先采用系统性多声文献综述协议，以客观、系统且可复现的方式遴选出30种工具。随后，我们从以下三个维度对每种工具进行特征描述：1) 可测量特性，2) 在同行评审文献和在线媒体中的流行度，3) 优势与问题。我们采用主题建模和情感分析来提取并总结优势与问题，特别借助ChatGPT辅助主题解读。结果：本文对所选追踪工具在特性、流行度、优势与问题方面进行了系统比较。结论：主要结果表明，每种追踪工具都提供了独特的特性组合，同时具有不同的优缺点。本文的贡献在于帮助从业者更深入地理解追踪工具，从而促进其应用。