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iGen: dynamic interaction inference for configurable software., , , , and . SIGSOFT FSE, page 655-665. ACM, (2016)Learning a classifier for false positive error reports emitted by static code analysis tools., , , and . MAPL@PLDI, page 35-42. ACM, (2017)An Empirical Assessment of Machine Learning Approaches for Triaging Reports of a Java Static Analysis Tool., , , , and . ICST, page 288-299. IEEE, (2019)Identifying and Documenting False Positive Patterns Generated by Static Code Analysis Tools., , , , , and . SER&IP@ICSE, page 55-61. IEEE, (2017)An empirical study of real-world variability bugs detected by variability-oblivious tools., , , , and . ESEC/SIGSOFT FSE, page 50-61. ACM, (2019)SATune: A Study-Driven Auto-Tuning Approach for Configurable Software Verification Tools., , , , and . ASE, page 330-342. IEEE, (2021)Localizing configurations in highly-configurable systems., , , and . SPLC, page 269-273. ACM, (2018)Improving the Usability of Static Analysis Tools Using Machine Learning.. University of Maryland, College Park, MD, USA, (2019)base-search.net (ftunivmaryland:oai:drum.lib.umd.edu:1903/25464).An empirical assessment of machine learning approaches for triaging reports of static analysis tools., , , , , , and . Empir. Softw. Eng., 28 (2): 28 (March 2023)iGen: Dynamic Interaction Inference for Configurable Software., , , , and . CoRR, (2019)