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Identifying performance problems is critical in the software design, mostly because the results of performance analysis (i.e., mean values, variances, and probability distributions) are difficult to be interpreted for providing feedback to software designers. Performance antipatterns support the interpretation of performance analysis results and help to fill the gap between numbers and design alternatives. In this paper, we present a model-driven framework that enables an early detection ofdoi:10.1145/2693561.2693565 dblp:conf/wosp/ArcelliBT15 fatcat:2mtrpasusngp7lwooofy6ynncm