Zobrazeno 1 - 10
of 226
pro vyhledávání: '"Bissyandé, Tegawendé F."'
Autor:
Sun, Tiezhu, Daoudi, Nadia, Kim, Kisub, Allix, Kevin, Bissyandé, Tegawendé F., Klein, Jacques
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graphs that often fail to capture complex malicious behaviors. DexBERT, a p
Externí odkaz:
http://arxiv.org/abs/2408.16353
Static analysis is sound in theory, but an implementation may unsoundly fail to analyze all of a program's code. Any such omission is a serious threat to the validity of the tool's output. Our work is the first to measure the prevalence of these omis
Externí odkaz:
http://arxiv.org/abs/2407.07804
Autor:
Chen, Daihang, Liu, Yonghui, Zhou, Mingyi, Zhao, Yanjie, Wang, Haoyu, Wang, Shuai, Chen, Xiao, Bissyandé, Tegawendé F., Klein, Jacques, Li, Li
When mobile meets LLMs, mobile app users deserve to have more intelligent usage experiences. For this to happen, we argue that there is a strong need to appl LLMs for the mobile ecosystem. We therefore provide a research roadmap for guiding our fello
Externí odkaz:
http://arxiv.org/abs/2407.06573
Autor:
Ouédraogo, Wendkûuni C., Kaboré, Kader, Tian, Haoye, Song, Yewei, Koyuncu, Anil, Klein, Jacques, Lo, David, Bissyandé, Tegawendé F.
Unit testing, crucial for ensuring the reliability of code modules, such as classes and methods, is often overlooked by developers due to time constraints. Automated test generation techniques have emerged to address this, but they frequently lack re
Externí odkaz:
http://arxiv.org/abs/2407.00225
Autor:
Yang, Boyang, Tian, Haoye, Pian, Weiguo, Yu, Haoran, Wang, Haitao, Klein, Jacques, Bissyandé, Tegawendé F., Jin, Shunfu
Program repair techniques offer cost-saving benefits for debugging within software development and programming education scenarios. With the proven effectiveness of Large Language Models (LLMs) in code-related tasks, researchers have explored their p
Externí odkaz:
http://arxiv.org/abs/2406.13972
Autor:
Yang, Boyang, Tian, Haoye, Ren, Jiadong, Zhang, Hongyu, Klein, Jacques, Bissyandé, Tegawendé F., Goues, Claire Le, Jin, Shunfu
Large language models (LLMs) have demonstrated remarkable capabilities on a broad spectrum of downstream tasks. Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine
Externí odkaz:
http://arxiv.org/abs/2404.12636
This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming languages. In particular, we explore the usefulness of these metrics and com
Externí odkaz:
http://arxiv.org/abs/2404.08817
Autor:
Philippy, Fred, Guo, Siwen, Haddadan, Shohreh, Lothritz, Cedric, Klein, Jacques, Bissyandé, Tegawendé F.
Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at the input layer of the PLM, without modifying its parameters. This paper
Externí odkaz:
http://arxiv.org/abs/2402.03782
Autor:
Tang, Xunzhu, Kim, Kisub, Song, Yewei, Lothritz, Cedric, Li, Bei, Ezzini, Saad, Tian, Haoye, Klein, Jacques, Bissyande, Tegawende F.
Code review, which aims at ensuring the overall quality and reliability of software, is a cornerstone of software development. Unfortunately, while crucial, Code review is a labor-intensive process that the research community is looking to automate.
Externí odkaz:
http://arxiv.org/abs/2402.02172
Autor:
Ouédraogo, Wendkûuni C., Plein, Laura, Kaboré, Kader, Habib, Andrew, Klein, Jacques, Lo, David, Bissyandé, Tegawendé F.
The quality of a software is highly dependent on the quality of the tests it is submitted to. Writing tests for bug detection is thus essential. However, it is time-consuming when done manually. Automating test cases generation has therefore been an
Externí odkaz:
http://arxiv.org/abs/2312.14898