Zhaoxia Li, Jianxing Zhu, K. Arumugam, J. Bhola, Rahul Neware
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Research on computer static software defect detection system based on big data technology
Abstract To study the static software defect detection system, based on the traditional static software defect detection system design, a new static software defect detection system design based on big data technology is proposed. The proposed method can optimize the distribution of test resources and improve the quality of software products by predicting the potential defect program modules and design the software and hardware of the static software defect detection system of big data technology. It is found that the traditional static software defect detection system design based on code source data takes a long time, averaging 65 h /day. However, the traditional static software defect detection system based on deep learning has a short detection time, averaging 35 h/day. In this article, the detection time of the static software defect detection system based on big data is shorter than that of the other two traditional system designs, with an average of 15 h/day. Because the system design adjusts the operating state of the system, it improves the accuracy of data operation. On the premise of data collection, the system inspection research is completed, which ensures the operational safety of software data, alleviates the contradiction between system and data to a high degree, improves the efficiency of system operation, reduces unnecessary operations, further shortens the time required for inspection, improves the system performance, and has higher research and operation value.
期刊介绍:
The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.