A large-scale empirical study on self-admitted technical debt

Bavota, Gabriele; Russo, Barbara

doi:10.1145/2901739.2901742

Cited by 103 publications

(152 citation statements)

References 34 publications

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“…Maldonado and Shihab [10], instead, looking at source code comments, classified different types of TD reporting that design SATD are the most common. Bavota and Russo [5] performed a finer categorization of SATD and reported that there is no correlation between SATD and code quality metrics evaluated at class-level. Looking at the change history, they quantified that 57% of SATD are removed from software projects.…”

Section: A Self-admitted Technical Debt (Satd) and Its Removalmentioning

confidence: 99%

“…The aforementioned works motivate SARDELE, highlighting the developers' need to cope with (SA)TD removal. Moreover, while previous research has attempted at identifying the cause of the problem in the comment [5] or the source code [56], it did not try to exploit comments and source code to recommend solution strategies like SARDELE is doing.…”

Section: A Self-admitted Technical Debt (Satd) and Its Removalmentioning

confidence: 99%

“…As SATD represents an admitted manifestation of a negative phenomenon (TD), it is of particular interest to understand whether this admission leads to appropriate corrective action, i.e., SATD removal. Bavota and Russo [5] found that over half (57%) of the SATD is actually being removed. While 63% of the removals are done by the same developer who admitted the SATD, in the remaining cases the SATD is addressed by somebody else.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatically Learning Patterns for Self-Admitted Technical Debt Removal

Zampetti

Serebrenik

Penta

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: A Self-admitted Technical Debt (Satd) and Its Removalmentioning

confidence: 99%

Section: A Self-admitted Technical Debt (Satd) and Its Removalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatically Learning Patterns for Self-Admitted Technical Debt Removal

Zampetti

Serebrenik

Penta

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…However, many types of TD can occur during the software lifecycle [1]. To identify a particular type of TD, it is necessary to know the causes of that typeof TD and the indicator [8]. Before we manage the TD, the first is to identify it in the software [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, the code comments also help developers to understand the code source [11,12]. That reason leads some researchers to use code comments to identify Self-Admitted Technical Debt (SATD) [8,9,10]. In these 43 empirical studies, approximately 48 tools were used.…”

Section: Introductionmentioning

confidence: 99%

Investigate, Identify and Estimate the Technical Debt: A Systematic Mapping Study

BenIdris¹,

Ammar²,

Dzielski³

2018

IJSEA

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Context: Technical Debt (TD) is a metaphor that refers to short-term solutions in software development that may affect the cost to the software development life cycle. Objective: To explore and understand TDrelated to the software industry as well as an overview on the current state of TD research. Forty-three TD empirical studies were collected for classification and analyzation. Goals: Classify TD types, find the indicators used to detect TD, find the estimators used to quantify the TD, evaluate how researchers investigate TD. Method: By performing a systematic mapping study to identify and analyze the TD empirical studies which published between 2014 and 2017. Results: We present the most common indicators and evaluators to identify and evaluate the TD, and we gathered thirteen types of TD. We showed some ways to investigate the TD, and used tools in the selected studies. Conclusion: The outcome of our systematic mapping study can help researchers to identify interestand future in TD.

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Evolution of technical debt remediation in Python: A case study on the Apache Software Ecosystem

Tan

Feitosa

Avgeriou

et al. 2020

J Software Evolu Process

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In recent years, the evolution of software ecosystems and the detection of technical debt received significant attention by researchers from both industry and academia. While a few studies that analyze various aspects of technical debt evolution already exist, to the best of our knowledge, there is no large‐scale study that focuses on the remediation of technical debt over time in Python projects—that is, one of the most popular programming languages at the moment. In this paper, we analyze the evolution of technical debt in 44 Python open‐source software projects belonging to the Apache Software Foundation. We focus on the type and amount of technical debt that is paid back. The study required the mining of over 60K commits, detailed code analysis on 3.7K system versions, and the analysis of almost 43K fixed issues. The findings show that most of the repayment effort goes into testing, documentation, complexity, and duplication removal. Moreover, more than half of the Python technical debt is short term being repaid in less than 2 months. In particular, the observations that a minority of rules account for the majority of issues fixed and spent effort suggest that addressing those kinds of debt in the future is important for research and practice.

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A large-scale empirical study on self-admitted technical debt

Cited by 103 publications

References 34 publications

Automatically Learning Patterns for Self-Admitted Technical Debt Removal

Automatically Learning Patterns for Self-Admitted Technical Debt Removal

Investigate, Identify and Estimate the Technical Debt: A Systematic Mapping Study

Evolution of technical debt remediation in Python: A case study on the Apache Software Ecosystem

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