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Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection.

, , , , , , and . SAC, page 318-327. ACM, (2019)

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Using developers' feedback to improve code smell detection., , , , and . SAC, page 1661-1663. ACM, (2015)Search-based many-criteria identification of microservices from legacy systems., , , , , , , and . GECCO Companion, page 305-306. ACM, (2020)A Qualitative Analysis of Variability Weaknesses in Configurable Systems with #ifdefs., , , , , and . VaMoS, page 51-58. ACM, (2018)Beyond the Code: Investigating the Effects of Pull Request Conversations on Design Decay., , , , , , , , , and 1 other author(s). ESEM, page 1-12. IEEE, (2023)Investigating the social representations of code smell identification: a preliminary study., , , , , , and . CHASE@ICSE, page 53-60. IEEE / ACM, (2019)Manual Tests Do Smell! Cataloging and Identifying Natural Language Test Smells., , , , , , , , , and . ESEM, page 1-11. IEEE, (2023)Investigating the Social Representations of the Identification of Code Smells by Practitioners and Students from Brazil., , , , , , , and . SBES, page 457-466. ACM, (2019)Lint-Based Warnings in Python Code: Frequency, Awareness and Refactoring., , , , , and . SCAM, page 208-218. IEEE, (2022)Code Change History and Software Vulnerabilities., , and . DSN Workshops, page 6-9. IEEE Computer Society, (2016)On the Performance and Adoption of Search-Based Microservice Identification with toMicroservices., , , , , , , , and . ICSME, page 569-580. IEEE, (2020)