Zobrazeno 1 - 10
of 1 062
pro vyhledávání: '"Meiyappan A"'
Memory leak bugs are a major problem in C/C++ programs. They occur when memory objects are not deallocated.Developers need to manually deallocate these objects to prevent memory leaks. As such, several techniques have been proposed to automatically f
Externí odkaz:
http://arxiv.org/abs/2408.04764
Software bugs require developers to exert significant effort to identify and resolve them, often consuming about one-third of their time. Bug localization, the process of pinpointing the exact source code files that need modification, is crucial in r
Externí odkaz:
http://arxiv.org/abs/2407.17631
Autor:
Chakraborty, Partha, Arumugam, Krishna Kanth, Alfadel, Mahmoud, Nagappan, Meiyappan, McIntosh, Shane
The impact of software vulnerabilities on everyday software systems is significant. Despite deep learning models being proposed for vulnerability detection, their reliability is questionable. Prior evaluations show high recall/F1 scores of up to 99%,
Externí odkaz:
http://arxiv.org/abs/2407.03093
Bug localization refers to the identification of source code files which is in a programming language and also responsible for the unexpected behavior of software using the bug report, which is a natural language. As bug localization is labor-intensi
Externí odkaz:
http://arxiv.org/abs/2406.17615
Recent Large Language Models (LLMs) have demonstrated significant capabilities in generating code snippets directly from problem statements. This increasingly automated process mirrors traditional human-led software development, where code is often w
Externí odkaz:
http://arxiv.org/abs/2402.13521
Manual confirmation of static analysis reports is a daunting task. This is due to both the large number of warnings and the high density of false positives among them. Fuzzing techniques have been proposed to verify static analysis warnings. However,
Externí odkaz:
http://arxiv.org/abs/2402.01923
Despite the continued research and progress in building secure systems, Android applications continue to be ridden with vulnerabilities, necessitating effective detection methods. Current strategies involving static and dynamic analysis tools come wi
Externí odkaz:
http://arxiv.org/abs/2401.01269
Diversity with respect to ethnicity and gender has been studied in open-source and industrial settings for software development. Publication avenues such as academic conferences and journals contribute to the growing technology industry. However, the
Externí odkaz:
http://arxiv.org/abs/2310.16132
Code generation tools driven by artificial intelligence have recently become more popular due to advancements in deep learning and natural language processing that have increased their capabilities. The proliferation of these tools may be a double-ed
Externí odkaz:
http://arxiv.org/abs/2308.06587
Autor:
Sarabeth M. Mathis, Alexander E. Webber, Tomás M. León, Erin L. Murray, Monica Sun, Lauren A. White, Logan C. Brooks, Alden Green, Addison J. Hu, Roni Rosenfeld, Dmitry Shemetov, Ryan J. Tibshirani, Daniel J. McDonald, Sasikiran Kandula, Sen Pei, Rami Yaari, Teresa K. Yamana, Jeffrey Shaman, Pulak Agarwal, Srikar Balusu, Gautham Gururajan, Harshavardhan Kamarthi, B. Aditya Prakash, Rishi Raman, Zhiyuan Zhao, Alexander Rodríguez, Akilan Meiyappan, Shalina Omar, Prasith Baccam, Heidi L. Gurung, Brad T. Suchoski, Steve A. Stage, Marco Ajelli, Allisandra G. Kummer, Maria Litvinova, Paulo C. Ventura, Spencer Wadsworth, Jarad Niemi, Erica Carcelen, Alison L. Hill, Sara L. Loo, Clifton D. McKee, Koji Sato, Claire Smith, Shaun Truelove, Sung-mok Jung, Joseph C. Lemaitre, Justin Lessler, Thomas McAndrew, Wenxuan Ye, Nikos Bosse, William S. Hlavacek, Yen Ting Lin, Abhishek Mallela, Graham C. Gibson, Ye Chen, Shelby M. Lamm, Jaechoul Lee, Richard G. Posner, Amanda C. Perofsky, Cécile Viboud, Leonardo Clemente, Fred Lu, Austin G. Meyer, Mauricio Santillana, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Michal Ben-Nun, Pete Riley, James Turtle, Chis Hulme-Lowe, Shakeel Jessa, V. P. Nagraj, Stephen D. Turner, Desiree Williams, Avranil Basu, John M. Drake, Spencer J. Fox, Ehsan Suez, Monica G. Cojocaru, Edward W. Thommes, Estee Y. Cramer, Aaron Gerding, Ariane Stark, Evan L. Ray, Nicholas G. Reich, Li Shandross, Nutcha Wattanachit, Yijin Wang, Martha W. Zorn, Majd Al Aawar, Ajitesh Srivastava, Lauren A. Meyers, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan L. Lewis, Madhav Marathe, Srinivasan Venkatramanan, Patrick Butler, Andrew Farabow, Naren Ramakrishnan, Nikhil Muralidhar, Carrie Reed, Matthew Biggerstaff, Rebecca K. Borchering
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predi
Externí odkaz:
https://doaj.org/article/b22341a399b644b886f816887477dc5b