Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Pujar, Saurabh"'
Autor:
Sahoo, Priyam, Pujar, Saurabh, Nalawade, Ganesh, Gebhardt, Richard, Mandel, Louis, Buratti, Luca
The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as a
Externí odkaz:
http://arxiv.org/abs/2402.17442
Autor:
Ullah, Saad, Han, Mingji, Pujar, Saurabh, Pearce, Hammond, Coskun, Ayse, Stringhini, Gianluca
Large Language Models (LLMs) have been suggested for use in automated vulnerability repair, but benchmarks showing they can consistently identify security-related bugs are lacking. We thus develop SecLLMHolmes, a fully automated evaluation framework
Externí odkaz:
http://arxiv.org/abs/2312.12575
Large language models (LLMs) have become remarkably good at improving developer productivity for high-resource programming languages. These models use two kinds of data: large amounts of unlabeled code samples for pre-training and relatively smaller
Externí odkaz:
http://arxiv.org/abs/2310.16937
Autor:
Min, Marcus J., Ding, Yangruibo, Buratti, Luca, Pujar, Saurabh, Kaiser, Gail, Jana, Suman, Ray, Baishakhi
Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the conventional accuracy evaluates the performance of Code LLMs on a set of individual tasks, their self-consiste
Externí odkaz:
http://arxiv.org/abs/2310.14053
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, 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:
Puri, Ruchir, Kung, David S., Janssen, Geert, Zhang, Wei, Domeniconi, Giacomo, Zolotov, Vladimir, Dolby, Julian, Chen, Jie, Choudhury, Mihir, Decker, Lindsey, Thost, Veronika, Buratti, Luca, Pujar, Saurabh, Ramji, Shyam, Finkler, Ulrich, Malaika, Susan, Reiss, Frederick
Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applications ages, it is now more critical than ever to increase software deve
Externí odkaz:
http://arxiv.org/abs/2105.12655
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
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