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Scented since the beginning: On the diffuseness of test smells in automatically generated test code.

, , , , and . Journal of Systems and Software, (2019)

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A graph-based dataset of commit history of real-world Android apps., , , , , and . MSR, page 30-33. ACM, (2018)Methods and Tools for Focusing and Prioritizing the Testing Effort.. ICSME, page 722-726. IEEE Computer Society, (2018)Scented since the beginning: On the diffuseness of test smells in automatically generated test code., , , , and . Journal of Systems and Software, (2019)SoCRATES: Scala radar for test smells., , and . SCALA@ECOOP, page 22-26. ACM, (2019)On the role of data balancing for machine learning-based code smell detection., , , and . MaLTeSQuE@ESEC/SIGSOFT FSE, page 19-24. ACM, (2019)Lightweight detection of Android-specific code smells: The aDoctor project., , , , and . SANER, page 487-491. IEEE Computer Society, (2017)Self-reported activities of Android developers., , , , , and . MOBILESoft@ICSE, page 144-155. ACM, (2018)The role of meta-learners in the adaptive selection of classifiers., and . MaLTeSQuE@SANER, page 7-12. IEEE Computer Society, (2018)Landfill: An Open Dataset of Code Smells with Public Evaluation., , , , , , and . MSR, page 482-485. IEEE Computer Society, (2015)Comparing heuristic and machine learning approaches for metric-based code smell detection., , , and . ICPC, page 93-104. IEEE / ACM, (2019)