Publications
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ZORA Publication List
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Publications
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2022
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An Exploratory Study on Regression Vulnerabilities (F. Madeiral, C. Lassenius, T. Conte, & T. Männistö, Eds.; pp. 12–22). ACM. https://doi.org/10.1145/3544902.3546250
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The evolution of the code during review: an investigation on review changes Empirical Software Engineering, 27, 177:1-177:49. https://doi.org/10.1007/s10664-022-10205-7
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Less is more: Supporting developers in vulnerability detection during code review 1317–1329. https://doi.org/10.1145/3510003.3511560
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Do explicit review strategies improve code review performance? Towards understanding the role of cognitive load Empirical Software Engineering, 27, 99:1-99:46. https://doi.org/10.1007/s10664-022-10123-8
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What happens in my code reviews? An investigation on automatically classifying review changes Empirical Software Engineering, 27, 89:1-89:43. https://doi.org/10.1007/s10664-021-10075-5
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Interpersonal Conflicts During Code Review 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing, Taipei. https://doi.org/10.5281/zenodo.5848794
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Workflow analysis of data science code in public GitHub repositories Empirical Software Engineering, 28, 7. https://doi.org/10.1007/s10664-022-10229-z
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2021
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ChangeViz: Enhancing the GitHub Pull Request Interface with Method Call Information 115–119. https://doi.org/10.1109/VISSOFT52517.2021.00022
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The indolent lambdification of Java: Understanding the support for lambda expressions in the Java ecosystem Empirical Software Engineering, 26, 134:1-134:36. https://doi.org/10.1007/s10664-021-10039-9
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Authorship attribution of source code: a language-agnostic approach and applicability in software engineering 932–944. https://doi.org/10.1145/3468264.3468606
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Why Don’t Developers Detect Improper Input Validation? 499–511. https://doi.org/10.1109/ICSE43902.2021.00054
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2020
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Primers or reminders? The effects of existing review comments on code review 1171–1182. https://doi.org/10.1145/3377811.3380385
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Investigating Severity Thresholds for Test Smells 311–321. https://doi.org/10.1145/3379597.3387453
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Do Explicit Review Strategies Improve Code Review Performance? 606–610. https://doi.org/10.1145/3379597.3387509
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UI Dark Patterns and Where to Find Them A Study on Mobile Applications and User Perception 1–14. https://doi.org/10.1145/3313831.3376600
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The effects of change decomposition on code review—a controlled experiment PeerJ, 5, 193–193. https://doi.org/10.7717/peerj-cs.193
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Does Reviewer Recommendation Help Developers? IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 710–731. https://doi.org/10.1109/TSE.2018.2868367
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On the performance of method-level bug prediction: A negative result Journal of Systems and Software, 161, 110493. https://doi.org/10.1016/j.jss.2019.110493
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To react, or not to react: Patterns of reaction to API deprecation Empirical Software Engineering, 24, 3824–3870. https://doi.org/10.1007/s10664-019-09713-w
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2019
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Understanding flaky tests: the developer’s perspective 830–840. https://doi.org/10.1145/3338906.3338945
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