Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Morari, Alessandro"'
Autor:
Ding, Yangruibo, Chakraborty, Saikat, Buratti, Luca, Pujar, Saurabh, Morari, Alessandro, Kaiser, Gail, Ray, Baishakhi
Deep Learning (DL) models to analyze source code have shown immense promise during the past few years. More recently, self-supervised pre-training has gained traction for learning generic code representations valuable for many downstream SE tasks, su
Externí odkaz:
http://arxiv.org/abs/2306.03234
Autor:
Pujar, Saurabh, Buratti, Luca, Guo, Xiaojie, Dupuis, Nicolas, Lewis, Burn, Suneja, Sahil, Sood, Atin, Nalawade, Ganesh, Jones, Matthew, Morari, Alessandro, Puri, Ruchir
The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages, such as the ones used for IT Automation, have received far less atte
Externí odkaz:
http://arxiv.org/abs/2305.02783
Autor:
Ding, Yangruibo, Suneja, Sahil, Zheng, Yunhui, Laredo, Jim, Morari, Alessandro, Kaiser, Gail, Ray, Baishakhi
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in today's software ecosystem, where vulnerable code can flow easily and
Externí odkaz:
http://arxiv.org/abs/2112.10893
AI modeling for source code understanding tasks has been making significant progress, and is being adopted in production development pipelines. However, reliability concerns, especially whether the models are actually learning task-related aspects of
Externí odkaz:
http://arxiv.org/abs/2111.05827
Autor:
Ding, Yangruibo, Buratti, Luca, Pujar, Saurabh, Morari, Alessandro, Ray, Baishakhi, Chakraborty, Saikat
Understanding the functional (dis)-similarity of source code is significant for code modeling tasks such as software vulnerability and code clone detection. We present DISCO(DIS-similarity of COde), a novel self-supervised model focusing on identifyi
Externí odkaz:
http://arxiv.org/abs/2110.03868
Autor:
Zhuang, Yufan, Suneja, Sahil, Thost, Veronika, Domeniconi, Giacomo, Morari, Alessandro, Laredo, Jim
Identifying vulnerable code is a precautionary measure to counter software security breaches. Tedious expert effort has been spent to build static analyzers, yet insecure patterns are barely fully enumerated. This work explores a deep learning approa
Externí odkaz:
http://arxiv.org/abs/2109.03341
Autor:
Zheng, Yunhui, Pujar, Saurabh, Lewis, Burn, Buratti, Luca, Epstein, Edward, Yang, Bo, Laredo, Jim, Morari, Alessandro, Su, Zhong
Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static analysis tools are known to generate an excess of false positives. The
Externí odkaz:
http://arxiv.org/abs/2102.07995
This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use case, we evaluate the models' ability to capture the correct vulnerability signals to produce their predictions. Our pred
Externí odkaz:
http://arxiv.org/abs/2011.14934
Autor:
Buratti, Luca, Pujar, Saurabh, Bornea, Mihaela, McCarley, Scott, Zheng, Yunhui, Rossiello, Gaetano, Morari, Alessandro, Laredo, Jim, Thost, Veronika, Zhuang, Yufan, Domeniconi, Giacomo
The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis through the use of a pre-trained transformer-based language model to p
Externí odkaz:
http://arxiv.org/abs/2006.12641
We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective. Specifically, whether signatures of vulnerabilities in source code can be learned from its graph representation, in terms of rel
Externí odkaz:
http://arxiv.org/abs/2006.08614