Abstract-During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice.Index Terms-API usage, usage pattern, sequence mining, software reuse, mining software repositories.
This paper proposes a new Simultaneous Localization and Mapping (SLAM) method on the basis of graph-based optimization through the combination of the Light Detection and Ranging (LiDAR), RGB-D camera, encoder and Inertial Measurement Unit (IMU). It can conduct joint positioning of four sensors by taking advantaging of the unscented Kalman filter (UKF) to design the related strategy of the 2D LiDAR point cloud and RGB-D camera point cloud. 3D LiDAR point cloud information generated by the RGB-D camera under the 2D LiDAR has been added into the new SLAM method in the sequential registration stage, and it can match the 2D LiDAR point cloud and the 3D RGB-D point cloud by using the method of the Correlation Scan Matching (CSM); In the loop closure detection stage, this method can further verify the accuracy of the loop closure after the 2D LiDAR matching by describing 3D point cloud.Additionally, this new SLAM method has been verified feasibility and availability through the processes of theoretical derivation, simulation experiment and physical verification. As a result, the experiment shows that the multi-sensor SLAM framework designed has a good mapping effect, high precision and accuracy.
The jerk, the third derivative of position of desired joint trajectory, adversely affects the efficiency of the control algorithms and stabilization of whole space robot system and therefore should be minimized. On the other hand, the importance of minimizing the jerk is to reduce the vibrations of manipulator. In this formulation, a global genetic-approach determines the trajectory by minimizing the maximum jerk in joint space. The planning procedure is performed with respect to all constraints, such as joint angle constraints, joint velocity constraints, joint angular acceleration and torque constraints, and so on. We use an genetic algorithm to search the optimal joint inter-knot parameters in order to realize the minimum jerk. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation result shows that GA-based minimum-jerk trajectory planning method has satisfactory performance and real significance in engineering.
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