masud-technope / ICSME2020-Replication-Package

The replication package for the article: Works for Me! Cannot Reproduce -- A Large Scale Empirical Study of Non-reproducible Bugs

Works for Me! Cannot Reproduce -- A Large Scale Empirical Study of Non-reproducible Bugs

Mohammad Masudur Rahman, Foutse Khomh, and Marco Castelluccio

Abstract Software developers attempt to reproduce software bugs to understand their erroneous behaviours and to fix them. Unfortunately, they often fail to reproduce (or fix) them, which leads to faulty, unreliable software systems. However, to date, only a little research has been done to better understand what makes the software bugs non-reproducible. In this article, we conduct a multimodal study to better understand the non-reproducibility of software bugs. First, we perform an empirical study using 576 non-reproducible bug reports from two popular software systems (Firefox, Eclipse) and identify 11 key factors that might lead a reported bug to non-reproducibility. Second, we conduct a user study involving 13 professional developers where we investigate how the developers cope with non-reproducible bugs. We found that they either close these bugs or solicit for further information, which involves long deliberations and counter-productive manual searches. Third, we offer several actionable insights on how to avoid non-reproducibility (e.g., false-positive bug report detector) and improve reproducibility of the reported bugs (e.g., sandbox for bug reproduction) by combining our analyses from multiple studies (e.g., empirical study, developer study). Fourth, we explain the differences between reproducible and non-reproducible bug reports by systematically interpreting multiple machine learning models that classify these reports with high accuracy. We found that links to existing bug reports might help improve the reproducibility of a reported bug. Fifth, we further demonstrate how 93 prior bugs connected to 71 non-reproducible bugs from our dataset can offer complementary information (e.g., attachments, screenshots, program flows).

Subject Systems

WORKSFORME

• Mozilla Firefox Core (250)
• Eclipse JDT (326)

FIXED

• Mozilla Firefox Core (250)
• Eclipse JDT (283)

Total: 1,109

Materials Included

• Grounded Theory contains all the artifacts generated during our empiricial study
  • Firefox Core: Open Coding, Axial Coding
  • Eclipse JDT: Open Coding, Axial Coding
  • Combined: Selective Coding
  • Combined: Key Factors
• Developer Study contains all the artifacts generated during our developer study
  • Raw responses from the developers
  • Agreed + Disagreed responses
• Machine Learning Model contains all the artifacts related to machine learning-based classification & model interpretation
  • Dataset: Contains the 17 features (12 structural + 2 textual + 3 sentiment) for 1,109 bug reports
  • SelectedBugs: Bug IDs of 1,109 bugs from Firefox Core and Eclipse JDT
  • XGBoost: Implemented XGBoost classifier & model configurations
  • Model Interpretation: Jupyter notebook containing the code to generate all the interpretation diagrams using SHAP framework
  • Model Configurations: Naive Bayes, Logistic Regression, J48, and RandomForest used the default configurations from Weka 3.9.4
• Manual Analysis contains all the artifacts generated during our manual analysis
  • Eclipse-Firefox-Manual-Analysis: Shows whether the connected bug reports contain stack traces, fixes, attachments, and other structured items
  • Manual-Analysis-Workbook : Raw data from our manual analysis
• README
• LICENSE

Accepted Paper at ICSME 2020

TCSE Distinguished Paper Award 2020

Why are Some Bugs Non-Reproducible? An Empirical Investigation using Data Fusion

Mohammad Masudur Rahman, Foutse Khomh, and Marco Castelluccio

Download this paper:

Please cite our work as

@INPROCEEDINGS{icsme2020masud,
	author={Rahman, M. M. and Khomh, F. and Castelluccio, M.},
	booktitle={Proc. ICSME},
	title={Why are Some Bugs Non-Reproducible? An Empirical Investigation using Data Fusion},
	year={2020},
	pages={605-616}
}

Please contact Masud Rahman ([e-mail]) for details.