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On the identification of design problems in stinky code: experiences and tool support.

, , , , , , and . J. Braz. Comput. Soc., 24 (1): 13:1-13:30 (2018)

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On the relationship of code-anomaly agglomerations and architectural problems., , , , and . J. Softw. Eng. Res. Dev., (2015)On the Prioritization of Design-Relevant Smelly Elements: A Mixed-Method, Multi-Project Study., , , and . SBCARS, page 83-92. ACM, (2019)Applying Machine Learning to Customized Smell Detection: A Multi-Project Study., , , , , and . SBES, page 233-242. ACM, (2020)Recommendations for Developers Identifying Code Smells., , , , , , and . IEEE Softw., 40 (2): 90-98 (March 2023)Don't Forget the Exception! : Considering Robustness Changes to Identify Design Problems., , , , , , , , , and . MSR, page 417-429. IEEE, (2023)On the Proactive Design of Product-Line Architectures with Aspects: An Exploratory Study., , , , , , , , and . COMPSAC, page 273-278. IEEE Computer Society, (2012)On the criteria for prioritizing code anomalies to identify architectural problems., , , , , and . SAC, page 1812-1814. ACM, (2016)On the Density and Diversity of Degradation Symptoms in Refactored Classes: A Multi-case Study., , , , , , and . ISSRE, page 346-357. IEEE, (2019)Recommendation of Refactorings for Improving Dependability Attributes.. ISSRE Workshops, page 89-92. IEEE, (2019)When Are Smells Indicators of Architectural Refactoring Opportunities: A Study of 50 Software Projects., , , , , and . ICPC, page 354-365. ACM, (2020)