Research on computer static software defect detection system based on big data technology
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 improv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-09-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2021-0260 |
| _version_ | 1797998357164064768 |
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| author | Li Zhaoxia Zhu Jianxing Arumugam K. Bhola Jyoti Neware Rahul |
| author_facet | Li Zhaoxia Zhu Jianxing Arumugam K. Bhola Jyoti Neware Rahul |
| author_sort | Li Zhaoxia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T10:47:25Z |
| format | Article |
| id | doaj.art-3294cef11c4f44d8a27f18b5c493ee6b |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-11T10:47:25Z |
| publishDate | 2022-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-3294cef11c4f44d8a27f18b5c493ee6b2022-12-22T04:29:00ZengDe GruyterJournal of Intelligent Systems2191-026X2022-09-013111055106410.1515/jisys-2021-0260Research on computer static software defect detection system based on big data technologyLi Zhaoxia0Zhu Jianxing1Arumugam K.2Bhola Jyoti3Neware Rahul4College of Mathematics and Information Technology, XingTai University, XingTai, 054001, ChinaCollege of Mathematics and Information Technology, XingTai University, XingTai, 054001, ChinaDepartment of Computer Science, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, IndiaElectronics & Communication Engineering Department, National Institute of Technology, Hamirpur, IndiaDepartment of Computing, Mathematics and Physics, Høgskulen på Vestlandet, Bergen, NorwayTo 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.https://doi.org/10.1515/jisys-2021-0260software defect predictiondetection systembig data technologystatic softwaresoftware evolution |
| spellingShingle | Li Zhaoxia Zhu Jianxing Arumugam K. Bhola Jyoti Neware Rahul Research on computer static software defect detection system based on big data technology Journal of Intelligent Systems software defect prediction detection system big data technology static software software evolution |
| title | Research on computer static software defect detection system based on big data technology |
| title_full | Research on computer static software defect detection system based on big data technology |
| title_fullStr | Research on computer static software defect detection system based on big data technology |
| title_full_unstemmed | Research on computer static software defect detection system based on big data technology |
| title_short | Research on computer static software defect detection system based on big data technology |
| title_sort | research on computer static software defect detection system based on big data technology |
| topic | software defect prediction detection system big data technology static software software evolution |
| url | https://doi.org/10.1515/jisys-2021-0260 |
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