基于大数据技术的计算机静态软件缺陷检测系统研究

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2022-01-01 DOI:10.1515/jisys-2021-0260
Zhaoxia Li, Jianxing Zhu, K. Arumugam, J. Bhola, Rahul Neware
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引用次数: 0

摘要

摘要以静态软件缺陷检测系统为研究对象,在传统静态软件缺陷检测系统设计的基础上,提出了一种基于大数据技术的静态软件缺陷检测系统设计。该方法通过预测潜在缺陷程序模块,设计基于大数据技术的静态软件缺陷检测系统的软硬件,优化测试资源分配,提高软件产品质量。研究发现,传统的基于代码源数据的静态软件缺陷检测系统设计耗时较长,平均为65小时/天。而传统的基于深度学习的静态软件缺陷检测系统检测时间较短,平均为35小时/天。在本文中,基于大数据的静态软件缺陷检测系统的检测时间比其他两种传统系统设计的检测时间短,平均为15小时/天。由于系统设计调整了系统的运行状态,提高了数据操作的准确性。在数据采集的前提下,完成系统巡检研究,保证了软件数据的运行安全,在很大程度上缓解了系统与数据之间的矛盾,提高了系统运行效率,减少了不必要的操作,进一步缩短了巡检所需的时间,提高了系统性能,具有较高的研究和运行价值。
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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.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
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.
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