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 Dynamicsmethod, a step-by-step method for the specification and dynamic detection of Android behavioural code smells; (2) A tool, the Dynamicstool, implementing this method on seven code smells; and (3) A validation of our approach on 538 apps from F-Droidwith a comparison with the static analysis detection tools,aDoctorand 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%.

Faults in safety critical real-time systems are not only logical, but they can correspond to violations of timing constraints. They must be detected to avoid system failures with adverse consequences. Developing efficient fault detection techniques for varieties of system models is still challenging. In this paper, we deal with fault detection for timed finite state machines with timeouts (TFSMs-T). TFSM-T is an extension of FSM to model timing constraints in safety-critical real-time systems. We lift a fault detection approach developed for FSM to generate tests detecting both logical faults and violations of time constraints in TFSMs-T. The approach is based on constraint solving and uses mutation machines to represent domains of faulty implementations (mutants) of a specification TFSMs-T. It also avoids enumerating the implementations one by one. We develop a prototype tool and we conduct experiments to evaluate the scalability of the proposed methods.