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Software evolution is a fact of life. Over the past thirty years, researchers have proposed hypotheses on how software changes, and provided evidence that both supports and refutes these hypotheses. To paint a clearer image of the software evolution process, we performed an empirical study on long spans in the lifetime of seven open source projects. Our analysis covers 653 official releases, and a combined 69 years of evolution. We first tried to verify Lehman's laws of software evolution. Our findings indicate that several of these laws are confirmed, while the rest can be either confirmed or infirmed depending on the laws' operational definitions. Second, we analyze the growth rate for projects' development and maintenance branches, and the distribution of software changes. We find similarities in the evolution patterns of the programs we studied, which brings us closer to constructing rigorous models for software evolution.

Unmanned aerial vehicle technology has made great progress in the past and is widely used in many fields. However, they are unable to meet large-scale and complex missions with a limited energy reserve. Only multiple unmanned aerial vehicles (multi-UAV) work together to better cope with this problem and have been extensively studied. In this paper, a new systematic framework is proposed to solve the problem of multi-UAV collaborative task allocation. It is formulated as a combinatorial optimization problem and solved by the improved clustering algorithm. The purpose is to enable multi-UAV to complete tasks with lower energy consumption. As the number of UAVs rises, it also appears the flight safety issues such as collisions among the UAVs, an improved multi-UAV collision-resistant method based on the improved artificial potential field is proposed. Besides, the UAVs connected with the internet are vulnerable to the various type of network attacks, a method based on the intrusion detection system is proposed to resist the network attack during multi-UAV mission execution. We have also proposed an improved method to improve the accuracy of task allocation further. In addition, an online real-time path planning is proposed to enhance the robustness of multi-UAV to cope with sudden problems. Finally, the numerical simulations and real physical flying experiments showed that the proposed method could provide a viable solution for multi-UAV task allocation; moreover, compared with other task allocation methods, our method has great performance.

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