Abstract
Code smells are symptoms that something is wrong in the source code. They have been catalogued and investigated in several programming techniques. These techniques can be used to develop Software Product Lines (SPL). However, feature-oriented programming (FOP) is a specific technique to deal with the modularization of features in SPL. One of the most popular FOP languages is AHEAD and, as far as we are concerned, we still lack systematic studies on the categorization and detection of code smells in AHEAD-based SPL. To fill this gap, this paper extends the definitions of three traditional code smells, namely God Method, God Class, and Shotgun Surgery, to take into account FOP abstractions. We then proposed 8 new FOP measures to quantify specific characteristics of compositional approaches like AHEAD. Finally, we combine the proposed and existing measures to define 3 detection strategies for identifying the investigated code smells. To evaluate the detection strategies, we performed an exploratory study involving 26 participants. The study participants rely on metrics to identify code smells in 8 AHEAD systems. Our results show that the proposed detection strategies can be used as code smell predictor since statistical tests indicate agreement among them and the study participants.
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