{DynAMICS: A tool-based method for the specification and dynamic detection of Android behavioural code smells

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Dimitri Prestat
Last updated:
Mon, 12/19/2022 - 11:20
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Code smells are the result of poor design choices within software systems that complexify source code and impede evolution and performance. Therefore, detecting code smells within software systems is an important priority to decrease technical debt. Furthermore, the emergence of mobile applications (apps) has brought new types of Android-specific code smells, which relate to limitations and constraints on resources like memory, performance and energy consumption. Among these Android-specific smells are those that describe inappropriate behaviour during the execution that may negatively impact software quality. Static analysis tools, however, show limitations for detecting these behavioural code smells and properly detecting behavioural code smells requires considering the dynamic behaviour of the apps. To dynamically detect behavioural code smells, we hence propose three contributions : (1) A method, the DynAMICS method, a step-by-step method for the specification and dynamic detection of Android behavioural code smells; (2) A tool, the DynAMICS tool, implementing this method on seven code smells; and (3) A validation of our approach on 538 apps from F-Droid with a comparison with the static analysis detection tools, aDoctor and Paprika, from the literature. Our method consists of four steps: (1) the specification of the code smells, (2) the instrumentation of the app, (3) the execution of the apps, and (4) the detection of the behavioural code smells. Our results show that many instances of code smells that cannot be detected with static detection tools are indeed detected with our dynamic approach with an average precision of 92.8% and an average recall of 53.4%.


Thank you for your interest in our work. We organised the data as follows:

  • Dataset Apps contains the name of the 538 apps used in the Validation.
  • Results contains the results of the detection of DynAMICS, aDoctor and Paprika on the dataset. This data is used mostly in Table 4.
  • Manual Validation contains the details of the manual validation depicted in the validation process. This data is used mostly in Section 6.5.3 for the Table 5.
  • DynAMICS Events contains details about the events encountered by DynAMICS. This data is used mostly in Table 6.

The folder also contains the DynAMICS Tool itself (source code and bytecode).

The sources and apks can be found here : https://doi.org/10.6084/m9.figshare.c.6349562.v1

The tool can also be found here : https://github.com/diprestat/DynamicVerification