Výsledky vyhľadávania - "Software verification and validation"

CURE: Code-Aware Neural Machine Translation for Automatic Program Repair Autor Jiang, Nan, Lutellier, Thibaud, Tan, Lin

“…Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to…”
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On the Efficiency of Test Suite based Program Repair A Systematic Assessment of 16 Automated Repair Systems for Java Programs Autor Liu, Kui, Wang, Shangwen, Kisub Kim, Anil Koyuncu, Bissyande, Tegawende F., Kim, Dongsun, Wu, Peng, Klein, Jacques, Mao, Xiaoguang, Traon, Yves Le

“…Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been…”
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Evaluating and Improving Fault Localization Autor Pearson, Spencer, Campos, Jose, Just, Rene, Fraser, Gordon, Abreu, Rui, Ernst, Michael D., Pang, Deric, Keller, Benjamin

“…Most fault localization techniques take as input a faulty program, and produce as output a ranked list of suspicious code locations at which the program may be…”
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SMARTIAN: Enhancing Smart Contract Fuzzing with Static and Dynamic Data-Flow Analyses Autor Choi, Jaeseung, Kim, Doyeon, Kim, Soomin, Grieco, Gustavo, Groce, Alex, Cha, Sang Kil

“…Unlike traditional software, smart contracts have the unique organization in which a sequence of transactions shares persistent states. Unfortunately, such a…”
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Precise Condition Synthesis for Program Repair Autor Yingfei Xiong, Jie Wang, Runfa Yan, Jiachen Zhang, Shi Han, Gang Huang, Lu Zhang

“…Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test…”
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Fault Localization with Code Coverage Representation Learning Autor Li, Yi, Wang, Shaohua, Nguyen, Tien

“…In this paper, we propose DeepRL4FL, a deep learning fault localization (FL) approach that locates the buggy code at the statement and method levels by…”
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Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis Autor Wang, Zan, You, Hanmo, Chen, Junjie, Zhang, Yingyi, Dong, Xuyuan, Zhang, Wenbin

“…Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the quality of DNNs. However, labeling test inputs to check the correctness…”
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Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts Autor Durieux, Thomas, Ferreira, Joao F., Abreu, Rui, Cruz, Pedro

“…Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not…”
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DeepLocalize: Fault Localization for Deep Neural Networks Autor Wardat, Mohammad, Le, Wei, Rajan, Hridesh

“…Deep Neural Networks (DNNs) are becoming an integral part of most software systems. Previous work has shown that DNNs have bugs. Unfortunately, existing…”
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An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair Autor Huang, Kai, Meng, Xiangxin, Zhang, Jian, Liu, Yang, Wang, Wenjie, Li, Shuhao, Zhang, Yuqing

“…The advent of large language models (LLMs) has opened up new opportunities for automated program repair (APR). In particular, some recent studies have explored…”
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WDD: Weighted Delta Debugging Autor Zhou, Xintong, Xu, Zhenyang, Zhang, Mengxiao, Tian, Yongqiang, Sun, Chengnian

“…Delta Debugging is a widely used family of algorithms (e.g., ddmin and ProbDD) to automatically minimize bug-triggering test inputs, thus to facilitate…”
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Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts Autor Mossberg, Mark, Manzano, Felipe, Hennenfent, Eric, Groce, Alex, Grieco, Gustavo, Feist, Josselin, Brunson, Trent, Dinaburg, Artem

“…An effective way to maximize code coverage in software tests is through dynamic symbolic execution-a technique that uses constraint solving to systematically…”
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PyCG: Practical Call Graph Generation in Python Autor Salis, Vitalis, Sotiropoulos, Thodoris, Louridas, Panos, Spinellis, Diomidis, Mitropoulos, Dimitris

“…Call graphs play an important role in different contexts, such as profiling and vulnerability propagation analysis. Generating call graphs in an efficient…”
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Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks Autor Mastropaolo, Antonio, Scalabrino, Simone, Cooper, Nathan, Nader Palacio, David, Poshyvanyk, Denys, Oliveto, Rocco, Bavota, Gabriele

“…Deep learning (DL) techniques are gaining more and more attention in the software engineering community. They have been used to support several code-related…”
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RobOT: Robustness-Oriented Testing for Deep Learning Systems Autor Wang, Jingyi, Chen, Jialuo, Sun, Youcheng, Ma, Xingjun, Wang, Dongxia, Sun, Jun, Cheng, Peng

“…Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems…”
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An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications Autor Chen, Zhenpeng, Yao, Huihan, Lou, Yiling, Cao, Yanbin, Liu, Yuanqiang, Wang, Haoyu, Liu, Xuanzhe

“…Deep learning (DL) is moving its step into a growing number of mobile software applications. These software applications, named as DL based mobile applications…”
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Owl Eyes: Spotting UI Display Issues via Visual Understanding Autor Liu, Zhe, Chen, Chunyang, Wang, Junjie, Huang, Yuekai, Hu, Jun, Wang, Qing

“…Graphical User Interface (GUI) provides a visual bridge between a software application and end users, through which they can interact with each other. With the…”
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AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair System Autor Zhang, Xiaoyu, Zhai, Juan, Ma, Shiqing, Shen, Chao

“…With machine learning models especially Deep Neural Network (DNN) models becoming an integral part of the new intelligent software, new tools to support their…”
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Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting Autor Li, Tsz-On, Zong, Wenxi, Wang, Yibo, Tian, Haoye, Wang, Ying, Cheung, Shing-Chi, Kramer, Jeff

“…Automated detection of software failures is an important but challenging software engineering task. It involves finding in a vast search space the…”
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Automatic Web Testing Using Curiosity-Driven Reinforcement Learning Autor Zheng, Yan, Liu, Yi, Xie, Xiaofei, Liu, Yepang, Ma, Lei, Hao, Jianye, Liu, Yang

“…Web testing has long been recognized as a notoriously difficult task. Even nowadays, web testing still mainly relies on manual efforts in many cases while…”
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