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A fine-grained data set and analysis of tangling in bug fixing commits

Herbold, S ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1007/s10664-021-10083-5
  3. Publisher: Springer , 2022
  4. Abstract:
  5. Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. © 2022, The Author(s)
  6. Keywords:
  7. Bug fix ; Manual validation ; Registered report ; Tangled changes ; Tangled commits ; Codes (symbols) ; Bug fixes ; Bug-fixing ; Crowd sourcing ; Data set ; Fine grained ; Research turk ; Tangled change ; Tangled commit ; Uncertainty analysis
  8. Source: Empirical Software Engineering ; Volume 27, Issue 6 , 2022 ; 13823256 (ISSN)
  9. URL: https://link-springer-com.lib.ezoclc.ml/article/10.1007/s10664-021-10083-5