关于敏捷中精确估算努力的原因和方法的系统文献综述

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-05-01 DOI:10.1145/3663365
Jirat Pasuksmit, Patanamon Thongtanunam, Shanika Karunasekera
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引用次数: 0

摘要

背景:准确的工作量估算对于敏捷迭代开发中的规划至关重要。敏捷估算通常依赖于基于共识的方法,如规划扑克,与其他正式方法(如 COSMIC)相比,这种方法需要的时间和信息更少,但容易出现不准确的情况。了解估算不准确的常见原因以及建议的方法如何帮助实践者至关重要。然而,之前的系统性文献综述(SLR)只关注估算实践(如 [26, 127])和工作量估算方法(如 [6])。目的:我们旨在找出估算不准确的原因,并对改进努力估算的方法进行分类。方法:我们进行了 SLR,确定了关键主题和分类标准。结果:估算不准确的原因与信息质量、团队、估算实践、项目管理和业务影响因素有关。文献中对工作量估算方法的研究最多,而旨在支持工作量估算流程的方法却寥寥无几。然而,很少有自动化方法存在数据泄露和间接验证的风险。建议:从业人员应提高努力估算信息的质量,可采用自动化方法。未来的研究应以提高信息质量为目标,同时避免数据泄露和间接验证情况。
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A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile

Background: Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, which require less time and information than other formal methods (e.g., COSMIC) but are prone to inaccuracies. Understanding the common reasons for inaccurate estimations and how proposed approaches can assist practitioners is essential. However, prior systematic literature reviews (SLR) only focus on the estimation practices (e.g., [26, 127]) and the effort estimation approaches (e.g., [6]). Aim: We aim to identify themes of reasons for inaccurate estimations and classify approaches to improve effort estimation. Method: We conducted an SLR and identified the key themes and a taxonomy. Results: The reasons for inaccurate estimation are related to information quality, team, estimation practice, project management, and business influences. The effort estimation approaches were the most investigated in the literature, while only a few aim to support the effort estimation process. Yet, few automated approaches are at risk of data leakage and indirect validation scenarios. Recommendations: Practitioners should enhance the quality of information for effort estimation, potentially by adopting an automated approach. Future research should aim to improve the information quality, while avoiding data leakage and indirect validation scenarios.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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