Cross Project Software Attack Location Method Based on Context Awareness

Guangdong Shen

2023

Abstract

Conventional software attack localization methods mainly focus on project defect prediction. Although the prediction is consistent with the actual demand, the tag data of the source project has a high similarity problem, which affects the accuracy of attack localization. Therefore, a context aware cross project software attack localization method is designed. Extract the tag features of cross project software attack domain, and eliminate the high similarity data in the tag data. Based on context awareness, a cross project software attack location model is constructed to determine the statement priority order of software attacks. Measure the cost of wrong positioning of cross project software attacks, avoid wrong positioning problems, and achieve accurate positioning of cross project software attacks. The simulation experiment verifies that the positioning method has higher accuracy and can be applied in real life.

Paper Citation

in Harvard Style

Shen G. (2023). Cross Project Software Attack Location Method Based on Context Awareness. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 409-414. DOI: 10.5220/0012285000003807

in Bibtex Style

@conference{anit23,
author={Guangdong Shen},
title={Cross Project Software Attack Location Method Based on Context Awareness},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={409-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012285000003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Cross Project Software Attack Location Method Based on Context Awareness
SN - 978-989-758-677-4
AU - Shen G.
PY - 2023
SP - 409
EP - 414
DO - 10.5220/0012285000003807
PB - SciTePress