Design, Application and Evaluation of PROV-SwProcess: A PROV extension Data Model for Software Development Processes

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Web Semantics Pub Date : 2021-11-01 DOI:10.1016/j.websem.2021.100676

Gabriella Castro Barbosa Costa , Claudia Werner , Regina Braga , Eldânae Nogueira Teixeira , Victor Ströele , Marco Antônio Pereira Araújo , Marcos Alexandre Miguel

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引用次数: 1

Abstract

The literature defines data provenance as the description of the origins of a piece of data and the process by which it arrived in a database. It helps to audit and understand data history and bring transparency to the process. Provenance has been successfully used in scientific computing, chemical industries, and health sciences, considering that these areas require a comprehensive traceability mechanism. Meanwhile, companies have been collecting and storing more data from their systems and processes. This work investigates if the use of provenance models and techniques can support software processes execution analysis and data-driven decision-making, considering the increasing availability of process data provided by companies. PROV-SwProcess, a software development process provenance modeling proposal, was developed and evaluated by process and provenance experts. Our proposal is an extension of the W3C recommended standard model PROV, aiming to capture and store the most relevant information about software development process provenance data. The results suggest that the model’s suitability improves and assists process managers in the software process analysis and supports data-driven decision-making.

查看原文本刊更多论文

PROV- swprocess的设计、应用与评价:软件开发过程的PROV扩展数据模型

文献将数据来源定义为对一段数据的起源及其到达数据库的过程的描述。它有助于审计和理解数据历史，并为流程带来透明度。考虑到这些领域需要全面的可追溯性机制，来源已经成功地应用于科学计算、化学工业和健康科学。与此同时，公司一直在从他们的系统和流程中收集和存储更多的数据。这项工作调查了来源模型和技术的使用是否能够支持软件过程执行分析和数据驱动的决策，考虑到公司提供的过程数据的日益增加的可用性。PROV-SwProcess是一个软件开发过程来源建模建议，由过程和来源专家开发和评估。我们的建议是W3C推荐的标准模型PROV的扩展，旨在捕获和存储关于软件开发过程起源数据的最相关信息。结果表明，该模型的适用性提高并有助于过程管理者进行软件过程分析，支持数据驱动的决策。

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来源期刊

Journal of Web Semantics 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.00%

发文量

审稿时长

14.6 weeks

期刊介绍： The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include: knowledge technologies, ontology, agents, databases and the semantic grid, obviously disciplines like information retrieval, language technology, human-computer interaction and knowledge discovery are of major relevance as well. All aspects of the Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents and services. The journal emphasizes the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications.