Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Maniatis, Petros"'
Large Language Models have demonstrated exceptional proficiency on coding tasks, but it is challenging to precisely evaluate their code reasoning ability. Existing benchmarks are insufficient as they are unrealistic and conflate semantic reasoning ab
Externí odkaz:
http://arxiv.org/abs/2408.08453
Autor:
Mathai, Alex, Huang, Chenxi, Maniatis, Petros, Nogikh, Aleksandr, Ivancic, Franjo, Yang, Junfeng, Ray, Baishakhi
Large Language Models (LLMs) are consistently improving at increasingly realistic software engineering (SE) tasks. In real-world software stacks, significant SE effort is spent developing foundational system software like the Linux kernel. Unlike app
Externí odkaz:
http://arxiv.org/abs/2407.02680
Autor:
Vijayvergiya, Manushree, Salawa, Małgorzata, Budiselić, Ivan, Zheng, Dan, Lamblin, Pascal, Ivanković, Marko, Carin, Juanjo, Lewko, Mateusz, Andonov, Jovan, Petrović, Goran, Tarlow, Daniel, Maniatis, Petros, Just, René
Modern code review is a process in which an incremental code contribution made by a code author is reviewed by one or more peers before it is committed to the version control system. An important element of modern code review is verifying that code c
Externí odkaz:
http://arxiv.org/abs/2405.13565
Autor:
Sahu, Surya Prakash, Mandal, Madhurima, Bharadwaj, Shikhar, Kanade, Aditya, Maniatis, Petros, Shevade, Shirish
Developers often have questions about semantic aspects of code they are working on, e.g., "Is there a class whose parent classes declare a conflicting attribute?". Answering them requires understanding code semantics such as attributes and inheritanc
Externí odkaz:
http://arxiv.org/abs/2209.08372
Autor:
Bieber, David, Shi, Kensen, Maniatis, Petros, Sutton, Charles, Hellendoorn, Vincent, Johnson, Daniel, Tarlow, Daniel
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitab
Externí odkaz:
http://arxiv.org/abs/2208.07461
Autor:
Chen, Xinyun, Maniatis, Petros, Singh, Rishabh, Sutton, Charles, Dai, Hanjun, Lin, Max, Zhou, Denny
Spreadsheet formula prediction has been an important program synthesis problem with many real-world applications. Previous works typically utilize input-output examples as the specification for spreadsheet formula synthesis, where each input-output p
Externí odkaz:
http://arxiv.org/abs/2106.15339
We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer. Our approach is inspired from the fact that human developers seldom get their program correct on the first
Externí odkaz:
http://arxiv.org/abs/2006.10924
Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A significant advancement in natural-language understanding has come with the de
Externí odkaz:
http://arxiv.org/abs/2001.00059
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. I
Externí odkaz:
http://arxiv.org/abs/1904.01720
Autor:
Bittau, Andrea, Erlingsson, Úlfar, Maniatis, Petros, Mironov, Ilya, Raghunathan, Ananth, Lie, David, Rudominer, Mitch, Kode, Usharsee, Tinnes, Julien, Seefeld, Bernhard
Publikováno v:
Proceedings of the 26th Symposium on Operating Systems Principles (SOSP), pp. 441-459, 2017
The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (E
Externí odkaz:
http://arxiv.org/abs/1710.00901