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Published in JSE, 2023
Software complexity is the very essence of computer programming. As the complexity increases, the potential risks and defects of software systems will increase. This makes the software correctness analysis and the software quality improvement more difficult. In this paper, we present a quantitative metric to describe the complexity of a hierarchical software and a Complexity-oriented Software Architecture Refactoring (CoSSR) approach to reduce the complexity. The main idea is to identify and then reassemble subcomponents into one hierarchical component, which achieves minimum complexity in terms of the solution algorithm. Moreover, our algorithm can be improved by introducing partition constraint, heuristic search strategy, and spectral clustering. We implement the proposed method as an automated refactoring tool and demonstrate our algorithm through a case study of battery management system (BMS). The results show that our approach is more efficient and effective to reduce the complexity of hierarchical software system.
Recommended citation: Yongxin Zhao, Wenhan Wu, Yuan Fei, Zhihao Liu, Yang Li, Yilong Yang, Ling Shi, Bo Zhang. An architecture refactoring approach to reducing software hierarchy complexity. J Softw Evol Proc. 2023;e2573. doi:10.1002/smr.2573 https://onlinelibrary.wiley.com/doi/abs/10.1002/smr.2573
Published in SMC, 2021
Location-based recommendation has become a significant method to help people locate fascinating and appealing points of interest (POIs) with the rapid popularity of smart mobile devices and the prevalence of location-based social networks (LBSN). However, the sparsity of the user-POI matrix and the cold-start issue have generated serious challenges, resulting in a substantial decrease in collaborative filtering methods’ recommendation results. In reality, location-based recommendation demands spatiotemporal context awareness. In order to overcome these challenges, we develop an embedding model based on the heterogeneous graph attention network. Geographic influence, social relation and historical check-in influence are captured in a unified way by constructing a user-POI heterogeneous graph. Subsequently, we use the LSTM-based model to learn the category weight of the next POI to select. We are developing a score function to recommend the next POI for users by integrating category weights, user preferences and time impact. We conduct experiments on existing large-scale datasets to evaluate the performance of our model. The results demonstrate our proposal is superior to other rivals. Additionally, our method has been significantly improved compared with other competitive approaches in terms of recommending cold-start POI.
Recommended citation: Chenchao Wang, Chao Peng, Mengdan Wang, Rui Yang, Wenhan Wu, Qilin Rui, Neal N. Xiong: CTHGAT: Category-aware and Time-aware Next Point-of-Interest via Heterogeneous Graph Attention Network. SMC 2021: 2420-2426 https://ieeexplore.ieee.org/document/9658805
Published in KSEM, 2021
In the system design process, it is an important issue to consider the order of class development. Different orders of class development may have great impact on the cost, efficiency and fault tolerance of the project. Because of that, it is an essential issue to consider which class should be developed before the others. In this paper, we present an approach to recommend a reasonable development order of classes with minimum development cost based on design class diagram and genetic algorithm. It helps the designer to improve their development strategy and to prevent mistakes resulted from improper development order of classes. We also provide a phase tree to help developers visualize and analyze the details of each development phase. At last, we implement a tool and illustrate that the proposed approach is sound and effective with two case studies.
Recommended citation: Wenhan Wu, Yongxin Zhao, Chao Peng, Yongjian Li, Qin Li: Analyzing and Recommending Development Order Based on Design Class Diagram. KSEM 2021: 524-537 https://link.springer.com/chapter/10.1007/978-3-030-82147-0_43
