Empirical Software Engineering

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Springer
 
 
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  • 2022 Journal Article
    ​ Problems with SZZ and features: An empirical study of the state of practice of defect prediction data collection
    Herbold, S.; Trautsch, A.; Trautsch, F. & Ledel, B. (2022) 
    Empirical Software Engineering27(2) art. 42.​
    Details  DOI 
  • 2022 Journal Article
    ​ Smoke testing for machine learning: simple tests to discover severe bugs
    Herbold, S. & Haar, T. (2022) 
    Empirical Software Engineering27(2) art. 45.​
    Details  DOI 
  • 2020 Journal Article
    ​ On the feasibility of automated prediction of bug and non-bug issues
    Herbold, S. ; Trautsch, A.   & Trautsch, F.  (2020) 
    Empirical Software Engineering25(6) pp. 5333-5369.​
    Details  DOI 
  • 2020 Journal Article
    ​ Correction to: On the feasibility of automated prediction of bug and non-bug issues
    Herbold, S. ; Trautsch, A.   & Trautsch, F.  (2020) 
    Empirical Software Engineering25(6) pp. 5370-5373.​
    Details  DOI 
  • 2020 Journal Article | Original Work
    ​ A longitudinal study of static analysis warning evolution and the effects of PMD on software quality in Apache open source projects
    Trautsch, A. ; Herbold, S.   & Grabowski, J.  (2020) 
    Empirical Software Engineering25(6) pp. 5137-5192.​
    Details  DOI 
  • 2017 Journal Article
    ​ Addressing problems with replicability and validity of repository mining studies through a smart data platform
    Trautsch, F. ; Herbold, S. ; Makedonski, P.   & Grabowski, J.  (2017) 
    Empirical Software Engineering23(2) pp. 1036-1083.​
    Details  DOI 
  • 2016 Journal Article
    ​ Global vs. local models for cross-project defect prediction
    Herbold, S. ; Trautsch, A.   & Grabowski, J.  (2016) 
    Empirical Software Engineering22(4) pp. 1866-1902.​
    Details  DOI 
  • 2011 Journal Article | 
    ​ Calculation and optimization of thresholds for sets of software metrics
    Herbold, S. ; Grabowski, J.   & Waack, S.  (2011) 
    Empirical Software Engineering16(6) pp. 812-841.​
    Details  DOI  WoS 

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