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pro vyhledávání: '"Venkatesh, Ashwin Prasad Shivarpatna"'
Autor:
Venkatesh, Ashwin Prasad Shivarpatna, Sunil, Rose, Sabu, Samkutty, Mir, Amir M., Reis, Sofia, Bodden, Eric
Large Language Models (LLMs) are increasingly being explored for their potential in software engineering, particularly in static analysis tasks. In this study, we investigate the potential of current LLMs to enhance call-graph analysis and type infer
Externí odkaz:
http://arxiv.org/abs/2410.00603
Autor:
Venkatesh, Ashwin Prasad Shivarpatna, Sabu, Samkutty, Mir, Amir M., Reis, Sofia, Bodden, Eric
The application of Large Language Models (LLMs) in software engineering, particularly in static analysis tasks, represents a paradigm shift in the field. In this paper, we investigate the role that current LLMs can play in improving callgraph analysi
Externí odkaz:
http://arxiv.org/abs/2402.17679
Autor:
Venkatesh, Ashwin Prasad Shivarpatna, Sabu, Samkutty, Wang, Jiawei, Mir, Amir M., Li, Li, Bodden, Eric
In light of the growing interest in type inference research for Python, both researchers and practitioners require a standardized process to assess the performance of various type inference techniques. This paper introduces TypeEvalPy, a comprehensiv
Externí odkaz:
http://arxiv.org/abs/2312.16882
Autor:
Venkatesh, Ashwin Prasad Shivarpatna, Sabu, Samkutty, Chekkapalli, Mouli, Wang, Jiawei, Li, Li, Bodden, Eric
Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating and sharing machine-learning based solutions, primarily written in P
Externí odkaz:
http://arxiv.org/abs/2301.04419
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