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pro vyhledávání: '"Fakhoury, A."'
Autor:
Rao, Nikitha, Gilbert, Elizabeth, Ramananandro, Tahina, Swamy, Nikhil, Goues, Claire Le, Fakhoury, Sarah
Differential testing can be an effective way to find bugs in software systems with multiple implementations that conform to the same specification, like compilers, network protocol parsers, and language runtimes. Specifications for such systems are o
Externí odkaz:
http://arxiv.org/abs/2410.04249
Autor:
Chakraborty, Saikat, Ebner, Gabriel, Bhat, Siddharth, Fakhoury, Sarah, Fatima, Sakina, Lahiri, Shuvendu, Swamy, Nikhil
Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite the use of Satisfiability Modulo Theories (SMT) solvers to automate proo
Externí odkaz:
http://arxiv.org/abs/2405.01787
Improper parsing of attacker-controlled input is a leading source of software security vulnerabilities, especially when programmers transcribe informal format descriptions in RFCs into efficient parsing logic in low-level, memory unsafe languages. Se
Externí odkaz:
http://arxiv.org/abs/2404.10362
Publikováno v:
in IEEE Transactions on Software Engineering, vol. 50, no. 09, pp. 2254-2268, 2024
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, given NL is informal, it does not lend easily to checking that the gene
Externí odkaz:
http://arxiv.org/abs/2404.10100
Autor:
Ismail, Idil, Chaudhuri, Shayantan, Morgan, Dylan, Woodgate, Christopher D., Fakhoury, Ziad, Targett, James M., Pilgrim, Charlie, Maino, Carlo
This article is intended as a guide for new graduate students in the field of computational science. With the increasing influx of students from diverse backgrounds joining the ever-popular field, this short guide aims to help students navigate throu
Externí odkaz:
http://arxiv.org/abs/2310.13514
Autor:
Chakraborty, Saikat, Lahiri, Shuvendu K., Fakhoury, Sarah, Musuvathi, Madanlal, Lal, Akash, Rastogi, Aseem, Senthilnathan, Aditya, Sharma, Rahul, Swamy, Nikhil
Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot s
Externí odkaz:
http://arxiv.org/abs/2310.09342
Informal natural language that describes code functionality, such as code comments or function documentation, may contain substantial information about a programs intent. However, there is typically no guarantee that a programs implementation and nat
Externí odkaz:
http://arxiv.org/abs/2310.01831
Publikováno v:
International Journal of Organizational Analysis, 2023, Vol. 32, Issue 8, pp. 1440-1463.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJOA-02-2023-3656
Large language models (LLMs), such as OpenAI's Codex, have demonstrated their potential to generate code from natural language descriptions across a wide range of programming tasks. Several benchmarks have recently emerged to evaluate the ability of
Externí odkaz:
http://arxiv.org/abs/2304.03816