Open Tracing Tools: Overview and Critical Comparison

• URL: http://arxiv.org/abs/2207.06875v2
• Date: Fri, 23 Jun 2023 15:51:36 GMT
• Title: Open Tracing Tools: Overview and Critical Comparison
• Authors: Andrea Janes, Xiaozhou Li, Valentina Lenarduzzi
• Abstract summary: This paper aims to provide an overview of popular Open tracing tools via comparison. We first identified ra30 tools in an objective, systematic, and reproducible manner. We then characterized each tool looking at the 1) measured features, 2) popularity both in peer-reviewed literature and online media, and 3) benefits and issues.
• Score: 10.196089289625599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval [47.81307125613145]
  Re-Invoke is an unsupervised tool retrieval method designed to scale effectively to large toolsets without training. We employ a novel multi-view similarity ranking strategy based on intents to pinpoint the most relevant tools for each query. Our evaluation demonstrates that Re-Invoke significantly outperforms state-of-the-art alternatives in both single-tool and multi-tool scenarios.
  arXiv  Detail & Related papers  (2024-08-03T22:49:27Z)
• SurgiTrack: Fine-Grained Multi-Class Multi-Tool Tracking in Surgical Videos [2.3810490830445183]
  This work sets a new standard in surgical tool tracking, providing dynamic trajectories for more adaptable and precise assistance in minimally invasive surgeries. We propose SurgiTrack, a novel deep learning method that leverages YOLOv7 for precise tool detection and employs an attention mechanism to model the originating direction of the tools, as a proxy to their operators, for tool re-identification.
  arXiv  Detail & Related papers  (2024-05-30T17:59:10Z)
• Tool Learning with Large Language Models: A Survey [60.733557487886635]
  Tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization.
  arXiv  Detail & Related papers  (2024-05-28T08:01:26Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• What Are Tools Anyway? A Survey from the Language Model Perspective [67.18843218893416]
  Language models (LMs) are powerful yet mostly for text generation tasks. We provide a unified definition of tools as external programs used by LMs. We empirically study the efficiency of various tooling methods.
  arXiv  Detail & Related papers  (2024-03-18T17:20:07Z)
• Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications [43.358953895199264]
  We will identify static analysis architecture recovery tools for microservice applications via a multi-vocal literature review. We will then execute them on a common dataset and compare the measured effectiveness in architecture recovery.
  arXiv  Detail & Related papers  (2024-03-11T17:26:51Z)
• ControlLLM: Augment Language Models with Tools by Searching on Graphs [97.62758830255002]
  We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving real-world tasks. Our framework comprises three key components: (1) a textittask decomposer that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a textitThoughts-on-Graph (ToG) paradigm that searches the optimal solution path on a pre-built tool graph; and (3) an textitexecution engine with a rich toolbox that interprets the solution path and runs the
  arXiv  Detail & Related papers  (2023-10-26T21:57:21Z)
• Automated Grading and Feedback Tools for Programming Education: A Systematic Review [7.776434991976473]
  Most papers assess the correctness of assignments in object-oriented languages. Few tools assess the maintainability, readability or documentation of the source code. Most tools offered fully automated assessment to allow for near-instantaneous feedback.
  arXiv  Detail & Related papers  (2023-06-20T17:54:50Z)
• SOCIOFILLMORE: A Tool for Discovering Perspectives [10.189255026322996]
  SOCIOFILLMORE is a tool which helps to bring to the fore the perspective that a text expresses in depicting an event. Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics.
  arXiv  Detail & Related papers  (2022-03-07T14:42:22Z)
• AI Explainability 360: Impact and Design [120.95633114160688]
  In 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
  arXiv  Detail & Related papers  (2021-09-24T19:17:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.