Does it matter who pays back Technical Debt? An empirical study of self-fixed TD

Jie Tan*, Daniel Feitosa, Paris Avgeriou

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
23 Downloads (Pure)

Abstract

Context: Technical Debt (TD) can be paid back either by those that incurred it or by others. We call the former self-fixed TD, and it can be particularly effective, as developers are experts in their own code and are well-suited to fix the corresponding TD issues. Objective: The goal of our study is to investigate self-fixed technical debt, especially the extent in which TD is self-fixed, which types of TD are more likely to be self-fixed, whether the remediation time of self-fixed TD is shorter than non-self-fixed TD and how development behaviors are related to self-fixed TD. Method: We report on an empirical study that analyzes the self-fixed issues of five types of TD (i.e., Code, Defect, Design, Documentation and Test), captured via static analysis, in more than 44,000 commits obtained from 20 Python and 16 Java projects of the Apache Software Foundation. Results: The results show that about half of the fixed issues are self-fixed and that the likelihood of contained TD issues being self-fixed is negatively correlated with project size, the number of developers and total issues. Moreover, there is no significant difference of the survival time between self-fixed and non-self-fixed issues. Furthermore, developers are more keen to pay back their own TD when it is related to lower code level issues, e.g., Defect Debt and Code Debt. Finally, developers who are more dedicated to or knowledgeable about the project contribute to a higher chance of self-fixing TD. Conclusions: These results can benefit both researchers and practitioners by aiding the prioritization of TD remediation activities and refining strategies within development teams, and by informing the development of TD management tools.

Original languageEnglish
Article number106738
Number of pages15
JournalInformation and Software Technology
Volume143
DOIs
Publication statusPublished - Mar-2022

Keywords

  • Human factors
  • Python
  • Self-fixed issues
  • Static analysis
  • Technical Debt

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