Empirical Software Engineering

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  • 2023 | Journal Article | 
    ​ ​Trautsch, Alexander, Johannes Erbel, Steffen Herbold, and Jens Grabowski. "What really changes when developers intend to improve their source code: a commit-level study of static metric value and static analysis warning changes​." ​Empirical Software Engineering ​28, no. 2 (2023): . ​https://doi.org/10.1007/s10664-022-10257-9.
    ​Details  DOI 
  • 2023 | Journal Article | 
    ​ ​Trautsch, Alexander, Steffen Herbold, and Jens Grabowski. "Are automated static analysis tools worth it? An investigation into relative warning density and external software quality on the example of Apache open source projects​." ​Empirical Software Engineering ​28, no. 3 (2023): . ​https://doi.org/10.1007/s10664-023-10301-2.
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  • 2023 | Journal Article | 
    ​ ​Herbold, Steffen, and Steffen Tunkel. "Differential testing for machine learning: an analysis for classification algorithms beyond deep learning​." ​Empirical Software Engineering ​28, no. 2 (2023): . ​https://doi.org/10.1007/s10664-022-10273-9.
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  • 2022 | Journal Article | 
    ​ ​Herbold, Steffen, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher A. Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier et al. "A fine-grained data set and analysis of tangling in bug fixing commits​." ​Empirical Software Engineering ​27, no. 6 (2022): . ​https://doi.org/10.1007/s10664-021-10083-5.
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  • 2022 | Journal Article | 
    ​ ​Herbold, Steffen, and Tobias Haar. "Smoke testing for machine learning: simple tests to discover severe bugs​." ​Empirical Software Engineering ​27, no. 2 (2022): ​45​. ​https://doi.org/10.1007/s10664-021-10073-7.
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  • 2022 | Journal Article | 
    ​ ​Herbold, Steffen, Alexander Trautsch, Fabian Trautsch, and Benjamin Ledel. "Problems with SZZ and features: An empirical study of the state of practice of defect prediction data collection​." ​Empirical Software Engineering ​27, no. 2 (2022): ​42​. ​https://doi.org/10.1007/s10664-021-10092-4.
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  • 2022 | Journal Article | 
    ​ ​Tunkel, Steffen, and Steffen Herbold. "Exploring the relationship between performance metrics and cost saving potential of defect prediction models​." ​Empirical Software Engineering ​27, no. 7 (2022): . ​https://doi.org/10.1007/s10664-022-10224-4.
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  • 2020 | Journal Article | Research Paper | 
    ​ ​Trautsch, Alexander, Steffen Herbold, and Jens Grabowski. "A longitudinal study of static analysis warning evolution and the effects of PMD on software quality in Apache open source projects​." ​Empirical Software Engineering ​25, no. 6 (2020): ​5137​-5192​. ​https://doi.org/10.1007/s10664-020-09880-1.
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  • 2020 | Journal Article | Erratum | 
    ​ ​Herbold, Steffen, Alexander Trautsch, and Fabian Trautsch. "Correction to: On the feasibility of automated prediction of bug and non-bug issues​." ​Empirical Software Engineering ​25, no. 6 (2020): ​5370​-5373​. ​https://doi.org/10.1007/s10664-020-09888-7.
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  • 2020 | Journal Article | 
    ​ ​Herbold, Steffen, Alexander Trautsch, and Fabian Trautsch. "On the feasibility of automated prediction of bug and non-bug issues​." ​Empirical Software Engineering ​25, no. 6 (2020): ​5333​-5369​. ​https://doi.org/10.1007/s10664-020-09885-w.
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  • 2017 | Journal Article
    ​ ​Trautsch, Fabian, Steffen Herbold, Philip Makedonski, and Jens Grabowski. "Addressing problems with replicability and validity of repository mining studies through a smart data platform​." ​Empirical Software Engineering ​23, no. 2 (2017): ​1036​-1083​. ​https://doi.org/10.1007/s10664-017-9537-x.
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  • 2016 | Journal Article
    ​ ​Herbold, Steffen, Alexander Trautsch, and Jens Grabowski. "Global vs. local models for cross-project defect prediction​." ​Empirical Software Engineering ​22, no. 4 (2016): ​1866​-1902​. ​https://doi.org/10.1007/s10664-016-9468-y.
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  • 2011 | Journal Article | 
    ​ ​Herbold, Steffen, Jens Grabowski, and Stephan Waack. "Calculation and optimization of thresholds for sets of software metrics​." ​Empirical Software Engineering ​16, no. 6 (2011): ​812​-841​. ​https://doi.org/10.1007/s10664-011-9162-z.
    ​Details  DOI  WoS 

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