Zobrazeno 1 - 10
of 202
pro vyhledávání: '"NAGAPPAN, NACHIAPPAN"'
Autor:
Harzevili, Nima Shiri, Mohajer, Mohammad Mahdi, Shin, Jiho, Wei, Moshi, Uddin, Gias, Yang, Jinqiu, Wang, Junjie, Wang, Song, Ming, Zhen, Jiang, Nagappan, Nachiappan
Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and can lead to silent failures, incorrect results, or unexpected program
Externí odkaz:
http://arxiv.org/abs/2410.06440
Autor:
Abreu, Rui, Murali, Vijayaraghavan, Rigby, Peter C, Maddila, Chandra, Sun, Weiyan, Ge, Jun, Chinniah, Kaavya, Mockus, Audris, Mehta, Megh, Nagappan, Nachiappan
Release engineering has traditionally focused on continuously delivering features and bug fixes to users, but at a certain scale, it becomes impossible for a release engineering team to determine what should be released. At Meta's scale, the responsi
Externí odkaz:
http://arxiv.org/abs/2410.06351
Autor:
Dunay, Omer, Cheng, Daniel, Tait, Adam, Thakkar, Parth, Rigby, Peter C, Chiu, Andy, Ahmad, Imad, Ganesan, Arun, Maddila, Chandra, Murali, Vijayaraghavan, Tayyebi, Ali, Nagappan, Nachiappan
CodeCompose is an AI-assisted code authoring tool powered by large language models (LLMs) that provides inline suggestions to 10's of thousands of developers at Meta. In this paper, we present how we scaled the product from displaying single-line sug
Externí odkaz:
http://arxiv.org/abs/2402.04141
Autor:
Rigby, Peter C., Rogers, Seth, Saleem, Sadruddin, Suresh, Parth, Suskin, Daniel, Riggs, Patrick, Maddila, Chandra, Nagappan, Nachiappan
Code review ensures that a peer engineer manually examines the code before it is integrated and released into production. At Meta, we develop a wide range of software at scale, from social networking to software development infrastructure, such as ca
Externí odkaz:
http://arxiv.org/abs/2312.17169
Automatic detection of software bugs is a critical task in software security. Many static tools that can help detect bugs have been proposed. While these static bug detectors are mainly evaluated on general software projects call into question their
Externí odkaz:
http://arxiv.org/abs/2307.04080
Autor:
Harzevili, Nima Shiri, Belle, Alvine Boaye, Wang, Junjie, Wang, Song, Ming, Zhen, Jiang, Nagappan, Nachiappan
Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic vulnerability
Externí odkaz:
http://arxiv.org/abs/2306.11673
Autor:
Murali, Vijayaraghavan, Maddila, Chandra, Ahmad, Imad, Bolin, Michael, Cheng, Daniel, Ghorbani, Negar, Fernandez, Renuka, Nagappan, Nachiappan, Rigby, Peter C.
Generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and depl
Externí odkaz:
http://arxiv.org/abs/2305.12050
This paper investigates how the duration of various code review periods changes over a projects' lifetime. We study four open-source software (OSS) projects: Blender, FreeBSD, LLVM, and Mozilla. We mine and analyze the characteristics of 283,235 code
Externí odkaz:
http://arxiv.org/abs/2303.04293
To understand applications' memory usage details, engineers use instrumented builds and profiling tools. Both approaches are impractical for use in production environments or deployed mobile applications. As a result, developers can gather only high-
Externí odkaz:
http://arxiv.org/abs/2212.11866
For decades, the guidance given to software engineers has been to check the memory allocation results. This validation step is necessary to avoid crashes. However, in user mode, in modern operating systems (OS), such as Android, FreeBSD, iOS, and mac
Externí odkaz:
http://arxiv.org/abs/2208.08484