It is difficult to quickly locate and search for specific vulnerabilities and their solutions because vulnerability information is scattered in the existing vulnerability management library. To alleviate this problem,...
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ISBN:
(纸本)9781450394130
It is difficult to quickly locate and search for specific vulnerabilities and their solutions because vulnerability information is scattered in the existing vulnerability management library. To alleviate this problem, we extract knowledge from vulnerability reports and organize the vulnerability information into the form of a knowledge graph. Then, we implement a tool for knowledge-driven vulnerability searching, KVS. This tool mainly uses the BERT model to realize the vulnerability named entity recognition and construct the vulnerability knowledge graph (VulKG). Finally, we can search vulnerabilities of interest-based on VulKG. The URL of this tool is https://***/Neo4j-D3-VKG/. Video of our demo is available at https://***/FT1BaLUGPk0.
Internet of Things (IoT) is an integral part of home automation, where physical devices are interconnected such that, they enable communication amongst electronics, sensors, software, and network. The data collection ...
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With the rapid growth of e-commerce and the increasing importance of recommender systems in enhancing customer experience, there is a pressing need for customized systems that can be quickly developed in collaboration...
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This paper introduces a novel methodology for enhancing software effort estimation accuracy by incorporating observed ratings into measuring project similarity. Unlike traditional methods that rely only on historical ...
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作者：
Nandish, N.Kumar, JaleshMohan, G.JNNCE
Shivamogga Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Belagavi India
Binary code similarity detection (BCSD) is utilized in various critical areas such as malware detection, vulnerability search, software version control, reverse engineering, digital forensics, and ensuring software in...
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In recent years, the rise of big data has popularized data-driven decision-making. However, the interpretability shortcomings of artificial intelligence (AI) models limit their reliability for critical decisions. This...
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Unit testing validates the correctness of the unit under test and has become an essential activity in software development process. A unit test consists of a test prefix that drives the unit under test into a particul...
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ISBN:
(纸本)9798350329964
Unit testing validates the correctness of the unit under test and has become an essential activity in software development process. A unit test consists of a test prefix that drives the unit under test into a particular state, and a test oracle (e.g., assertion), which specifies the behavior in that state. To reduce manual efforts in conducting unit testing, Yu et al. proposed an integrated approach (integration for short), combining information retrieval with a deep learning-based approach, to generate assertions for a unit test. Despite being promising, there is still a knowledge gap as to why or where integration works or does not work. In this paper, we describe an in-depth analysis of the effectiveness of integration. Our analysis shows that: (1) The overall performance of integration is mainly due to its success in retrieving assertions. (2) integration struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test, resulting in many tokens being incorrectly modified;(3) integration is limited to specific types of edit operations (i.e., replacement) and cannot handle token addition or deletion. To improve the effectiveness of assertion generation, this paper proposes a novel retrieve-and-edit approach named EDITAS. Specifically, EDITAS first retrieves a similar focal-test from a pre-defined corpus and treats its assertion as a prototype. Then, EDITAS reuses the information in the prototype and edits the prototype automatically. EDITAS is more generalizable than integration because it can (1) comprehensively understand the semantic differences between input and similar focal-tests;(2) apply appropriate assertion edit patterns with greater flexibility;and (3) generate more diverse edit actions than just replacement operations. We conduct experiments on two large-scale datasets and the experimental results demonstrate that EDITAS outperforms the state-of-the-art
Computer chess games are one of the most significant research subjects in the field of artificial intelligence. Self-play learning depends only on the chess gaming process and the outcome. Neither knowledge nor expert...
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This study centers on the development of a narrative-driven, science fiction-themed animated video using Vyond's Go AI-powered platform. The objective is to enhance conceptual understanding and cognitive engagemen...
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The field-oriented control (FOC) algorithm for permanent magnet synchronous motor (PMSM) is limited by the processing power of the microcontroller, which affects the control performance. In order to improve the execut...
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