Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Machine/deep learning for software engineering: A systematic literature review
Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
A literature review of using machine learning in software development life cycle stages
The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …
modern-day software towards highly intelligent and self-learning systems. However, the …
Combining graph-based learning with automated data collection for code vulnerability detection
This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …
learning framework for building vulnerability detection models. Funded leverages the …
[HTML][HTML] A survey on machine learning techniques applied to source code
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
Deep learning-based software engineering: progress, challenges, and opportunities
Researchers have recently achieved significant advances in deep learning techniques,
which in turn has substantially advanced other research disciplines, such as natural …
which in turn has substantially advanced other research disciplines, such as natural …
Learning approximate execution semantics from traces for binary function similarity
Detecting semantically similar binary functions–a crucial capability with broad security
usages including vulnerability detection, malware analysis, and forensics–requires …
usages including vulnerability detection, malware analysis, and forensics–requires …
Machine learning methods for software vulnerability detection
Software vulnerabilities are a primary concern in the IT security industry, as malicious
hackers who discover these vulnerabilities can often exploit them for nefarious purposes …
hackers who discover these vulnerabilities can often exploit them for nefarious purposes …
Mitigating false positive static analysis warnings: Progress, challenges, and opportunities
Static analysis (SA) tools can generate useful static warnings to reveal the problematic code
snippets in a software system without dynamically executing the corresponding source code …
snippets in a software system without dynamically executing the corresponding source code …
NTFuzz: Enabling type-aware kernel fuzzing on windows with static binary analysis
Although it is common practice for kernel fuzzers to leverage type information of system
calls, current Windows kernel fuzzers do not follow the practice as most system calls are …
calls, current Windows kernel fuzzers do not follow the practice as most system calls are …