13 papers · ranked by Valyu relevance
Yiwen Zhang, Wei Liu, Fazhong Jiang, Jiquan Ma + 4 more
Large Language Models of the Transformer architecture display great promise in automated code error detection based on their strength in processing sequential data. Nevertheless, their efficacy could be further improved by addressing the inherent weakness in handling structural code dependencies. In response to this…
Melih Peker, Ozcan Ozturk
Selecting a good set of optimization flags requires extensive effort and expert input. While most of the prior research considers using static, spatial, or dynamic features, some of the latest research directly applied deep neural networks to source code. We combined the static features, spatial features, and deep…
Xiaojian Liu, Yangyang Zhang, Chee Wei Tan, Wenyi Zhang
Code coverage-guided unit test generation (CGTG) and large language model-based test generation (LLMTG) are two principal approaches for the generation of unit tests. Each of these approaches has its inherent advantages and drawbacks. Tests generated by CGTG have been shown to exhibit high code coverage and high…
Tom Pollard, Thomas Sounack, Catherine A. Gao, Leo Anthony Celi + 5 more
Introduction The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement was published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to…
Damiano Paoli, Marcella Lanza, Federico Banchelli, Manila Boarini + 3 more
Accurate medical coding is essential for disease registries, particularly in the context of rare conditions. Manually transforming electronic health records data into standardized codes is time-consuming and resource intensive. This study evaluates an automated coding system using synthetic health records data and…
Tongcheng Geng, Muhammad Ahsan
Deep code models face security vulnerabilities through backdoor attacks. Previous approaches have primarily relied on single-trigger mechanisms, resulting in limited stealth and vulnerability to defense strategies. This paper proposes a novel hybrid backdoor attack method that combines function signature features and…
Lia Chin-Purcell, Elena Rosenberg-Carlson, Hélène Chokron Garneau, Marzan Hamid + 1 more
To critically evaluate our proposed method, we conducted both a quantitative assessment of agreement between human coders and the NLP-assisted method, and a qualitative assessment of the soundness of the codes assigned by the NLP-assisted method. We conducted both assessments to have a direct comparison against human…
Ali Muzaffar, Hani Ragab Hassen, Hind Zantout, Michael A. Lones + 1 more
We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a contemporary Android environment and a balanced dataset of 124,000 applications.…
Helen Lumbard, Lauren Cadwallader, Devin Soper
PLOS Medicine has always championed open science and data transparency. Now, recognizing that code is as essential a research artifact as the data it analyzes, we are strengthening our code sharing policy to further ensure reproducibility and trust in the scientific record.
Roberto Di Cosmo, Sabrina Granger, Konrad Hinsen, Nicolas Jullien + 6 more
FAIR principles are a set of guidelines aiming at simplifying the distribution of scientific data to enhance reuse and reproducibility. This article focuses on research software, which significantly differs from data in its living nature, and its relationship with free and open-source software. We provide a tiered…
Aurora Papotti, Serena Elisa Ponta, Antonino Sabetta, Fabio Massacci
Goal: Machine learning (ML) has been proposed to identify security fixing commits with mixed success. We evaluate a different alternative in which expert-defined common sense rules with power-law weights are used to identify security fixing commit. Experiments: We first evaluated how those rules perform against ML…
Lauren K. Salig, Jorge R. Valdés Kroff, Jared M. Novick, L. Robert Slevc
Bilinguals frequently code-switch during conversations-a behavior often viewed as creating processing challenges for listeners. However, code-switching may also enhance comprehension and memory by directing attention to key information. This study tested whether bilinguals recall information better in code-switched…
Phi-Yen Nguyen, Joanne E McKenzie, Zainab Alqaidoom, Daniel G Hamilton + 2 more
Objective To determine how often meta-analyses of effects of interventions are reproducible. Design Meta-research study. Setting Systematic reviews with meta-analyses of the effects of health, social, behavioural, or educational interventions indexed in five databases (PubMed, Science Citation Index, Social Sciences…