A Discovery Method for Hierarchical Software Execution Behavior Models Based on Components

Author:

Tang Yahui1ORCID,Li Tong2ORCID,Zhu Rui3ORCID,Du Fei1ORCID,Wang Jishu3ORCID,Ma Zifei4ORCID

Affiliation:

1. School of Information, Yunnan University, Kunming 650500, China

2. School of Big Data, Yunnan Agricultural University, Kunming 650201, China

3. School of Software, Yunnan University, Kunming 650091, China

4. School of Water Conservancy, Yunnan Agriculture University, Kunming 650201, China

Abstract

Software is rapidly evolving and operates in a changing environment; therefore, in addition to software design and testing, it is essential to observe and understand the software execution behavior by modeling data recorded during the execution of the software to improve its reliability. The nested call relationship between methods during the execution of software is common, but most process-mining methods are unable to discover them, only generating flat models with low fitness. Meanwhile, it is easy to generate “spaghetti-like” models with low comprehensibility when dealing with complex software execution data. This paper proposes a component-based hierarchical software behavior model discovery method that can discover hierarchical nested call structures during software runtime, improving the fitness of the model; meanwhile, the proposed method partitions the discovery model into several parts by component information to improve the comprehensibility of the model, which can also reflect the interaction behavior within and between components. The proposed approach was implemented in a process mining toolkit. Using real-life software event logs and public datasets, we demonstrated that compared with other advanced process mining techniques, our approach can visualize actual software execution behavior in a more accurate and easy-to-understand way while balancing time performance.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Science Applications,Software

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Software Execution Data Component Identification Algorithm Based on Spectral Clustering;2023 10th International Conference on Dependable Systems and Their Applications (DSA);2023-08-10

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