Code smells are anomalous code structures which often indicate maintenance problems in software systems. The identification of code smells is required to reveal code elements, such as classes and methods, that are poorly structured. Some examples of code smell types perceived as critical by developers include God Classes and Feature Envy. However, the individual smell identification, which is performed by a single developer, may be ineffective. Several studies have reported limitations of individual smell identification. For instance, the smell identification usually requires an indepth understanding of multiple elements scattered in a program, and each of these elements is better understood by a different developer. As a consequence, a single developer often struggles and to find to confirm or refute a code smell suspect. Collaborative smell identification, which is performed together by two or more collaborators, has the potential to address this problem. However, there is little empirical evidence on the effectiveness of collaborative smell identification. In this thesis, we addressed the aforementioned limitations as follows. First, we conducted empirical studies aimed at understanding the effectiveness of both collaborative and individual smell identification. We computed and compared the effectiveness of collaborators and single developers based on the number of correctly identified code smells. We conducted these studies in both industry’s companies and research laboratories with 67 developers, including novice and professional developers. Second, we defined some influential factors on the effectiveness of collaborative smell identification, such as the smell granularity. Third, we revealed and characterized some collaborative activities which improve the developers effectiveness for identifying code smells. Fourth, we also characterized opportunities for further improving the effectiveness of certain collaborative activities. Our results suggest that collaborators are more effective than single developers in: (i) both professional and academic settings, and (ii) identifying a wide range of code smell types.