A survey of self-admitted technical debt

IF 3.7 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Journal of Systems and Software Pub Date : 2019-06-01 DOI:10.1016/j.jss.2019.02.056
Giancarlo Sierra , Emad Shihab , Yasutaka Kamei
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引用次数: 45

Abstract

Technical Debt is a metaphor used to express sub-optimal source code implementations that are introduced for short-term benefits that often need to be paid back later, at an increased cost. In recent years, various empirical studies have focused on investigating source code comments that indicate Technical Debt often referred to as Self-Admitted Technical Debt (SATD). Since the introduction of SATD as a concept, an increasing number of studies have examined various aspects pertaining to SATD. Therefore, in this paper we survey research work on SATD, analyzing the characteristics of current approaches and techniques for SATD detection, comprehension, and repayment. To motivate the submission of novel and improved work, we compile tools, resources, and data sets made available to replicate or extend current SATD research. To set the stage for future work, we identify open challenges in the study of SATD, areas that are missing investigation, and discuss potential future research avenues.

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一项自我承认的技术债务调查
技术债务是一个比喻,用于表示为了短期利益而引入的次优源代码实现,这些实现通常需要在以后以增加的成本偿还。近年来,各种实证研究都集中在调查表明技术债务的源代码注释上,这些注释通常被称为自我承认的技术债务(SATD)。自从将可持续发展作为一个概念引入以来,越来越多的研究审查了与可持续发展有关的各个方面。因此,本文综述了SATD的研究工作,分析了目前SATD检测、理解和偿还的方法和技术的特点。为了鼓励提交新颖和改进的工作,我们汇编了可用的工具、资源和数据集,以复制或扩展当前的SATD研究。为了为未来的工作奠定基础,我们确定了SATD研究中存在的挑战,以及缺少调查的领域,并讨论了潜在的未来研究途径。
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来源期刊
Journal of Systems and Software
Journal of Systems and Software 工程技术-计算机:理论方法
CiteScore
8.60
自引率
5.70%
发文量
193
审稿时长
16 weeks
期刊介绍: The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: •Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution •Agile, model-driven, service-oriented, open source and global software development •Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems •Human factors and management concerns of software development •Data management and big data issues of software systems •Metrics and evaluation, data mining of software development resources •Business and economic aspects of software development processes The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.
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