Due to its wide applications in different missions, identification of mathematical models of Unmanned Aerial Vehicles (UAVs) from flight test data becomes necessary for development of observers for states estimation, computer simulation, systems analysis, and in particular for autonomous flight control laws design. For an arbitrarily chosen aerial vehicle, Trex-700E -a single-rotor unmanned helicopter, a Low Order Equivalent System (LOES) model in longitudinal and lateral channels is identified respectively by utilizing CIFER ® system identification techniques. The flight test data is obtained at MicroPilot Inc. site by implementing piloted frequency sweep cyclic and collective inputs. Firstly, this paper presents a spectrum analysis performed on the input excitation signals of the flight tests.Secondly, non-parametric frequency-response models for on-axis longitudinal and lateral responses are acquired. Finally, a second-order transfer function model is identified for the longitudinal and lateral response respectively. The results show that LOES model for shortperiod mode can effectively predict the UAV system's relevant response. However, for lateral mode, the identified LOES model needs to be modified further with more flight test data. Nomenclature= vector of parameters to be estimated = cost function of parameter estimation = transfer function value evaluated in frequency and vector of estimated parameters = experimentally-determined frequency-responses evaluated in frequency = cost function weighting coefficient = equivalent time delay for the lumped effect of rotor and control system dynamics = equivalent time delay for effect of actuator and linkage dynamics = rotor time constant = short-period damping ratio = short-period undamped natural frequency = rotor-flap stiffness = lateral input gain = longitudinal input gain

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Context: Specification mining techniques are typically used to extract the specification of a software in the absence of (up-to-date) specification documents. This is useful for program comprehension, testing, and anomaly detection. However, specification mining can also potentially be used for debugging, where a faulty behavior is abstracted to give developers a context about the bug and help them locating it. Objective: In this project, we investigate this idea in an industrial setting. We propose a very basic semi-automated specification mining approach for debugging and apply that on real reported issues from an AutoPilot software system from our industry partner, MicroPilot Inc. The objective is to assess the feasibility and usefulness of the approach in a real-world setting. Method: The approach is developed as a prototype tool, working on C code, which accept a set of relevant state fields and functions, per issue, and generates an extended finite state machine that represents the faulty behavior, abstracted with respect to the relevant context (the selected fields and functions). Results:We qualitatively evaluate the approach by a set of interviews (including observational studies) with the company's developers on their real-world reported bugs. The results show that a) our approach is feasible, b) it can be automated to some extent, and c) brings advantages over only using their code-level debugging tools. We also compared this approach with traditional fully automated state-merging algorithms and reported several issues when applying those techniques on a real-world debugging context. Conclusion: The main conclusion of this study is that the idea of an "interactive" specification mining rather than a fully automated mining tool is NOT impractical and indeed is useful for the debugging use case.