Having high-quality apps is therefore a vital requirement for developers to keep staying on the market and acquire new users. apps) on a daily basis to access high-speed connectivity and all kinds of services it enables, from social to emergency needs. These days, over three billion users rely on mobile applications (a.k.a. This study shows that established static analysis methods deemed to be effective for code smells detection are inadequate for behavioural mobile code smells detection. First, are the detection tools effective in detecting behavioural code smells? Second, are the behavioural code smells detected by the tools consistent with their original literal definition? We emphasise the limitations of detection using only static techniques and the lessons learned from our empirical study. This empirical study aims to answer two research questions. We analyse the effectiveness of behavioural code smells detection in practice within the tools of concern by performing an empirical study of code smells detected in apps. Both tools use representative techniques from the literature and contain behavioural code smells. In this paper, we are especially interested in two tools: Paprika and aDoctor. Many tools exist to detect code smells in mobile apps, based specifically on static analysis techniques. Some of these mobile-specific smells are behavioural because they describe an inappropriate behaviour that may negatively impact software quality. In addition to common object-oriented code smells, mobile apps have their own code smells because of their limitations and constraints on resources like memory, performance and energy consumption. Code smells complexify source code and may impede the evolution and performance of mobile apps.

Each development iteration may introduce poor design choices, and therefore produce code smells. Mobile applications (apps) are developed quickly and evolve continuously.

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