Search Results - Verification and Program Analysis for Machine Learning Models

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

“…Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT…”
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FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model by Shao, Shuo, Yang, Wenyuan, Gu, Hanlin, Qin, Zhan, Fan, Lixin, Yang, Qiang, Ren, Kui

“…Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data…”
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Implementation of machine learning tool for continued process verification of process chromatography unit operation by Anupa, Anupa, Jesubalan, Naveen G., Trivedi, Rishika, Nitika, Nitika, Buddhiraju, Venkata Sudheendra, Runkana, Venkataramana, Rathore, Anurag S

“… Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning…”
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DeepLocalize: Fault Localization for Deep Neural Networks by Wardat, Mohammad, Le, Wei, Rajan, Hridesh

“… To that end, we first enable dynamic analysis of deep learning applications: by converting it into an imperative representation and alternatively…”
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Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods by Tussupov, Jamalbek, Yessenova, Moldir, Abdikerimova, Gulzira, Aimbetov, Aidyn, Baktybekov, Kazbek, Murzabekova, Gulden, Aitimova, Ulzada

“…This article is devoted to a set of important areas of research: the analysis of formal representations and verification of pests and pathogens affecting crops using spectral brightness coefficients (SBR…”
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Inferring extended finite state machine models from software executions by Walkinshaw, Neil, Taylor, Ramsay, Derrick, John

“…The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks…”
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DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs by Wardat, Mohammad, Cruz, Breno Dantas, Le, Wei, Rajan, Hridesh

“… Furthermore, those buggy models are non-trivial to diagnose and fix due to inexplicit errors with several options to fix…”
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Research on a Regional Landslide Early-Warning Model Based on Machine Learning—A Case Study of Fujian Province, China by Liu, Yanhui, Huang, Junbao, Xiao, Ruihua, Ma, Shiwei, Zhou, Pinggen

“… The traditional regional landslide disaster early-warning model, however, is limited by the complex landslide induction mechanism, limited data accumulation, and insufficient big data analysis…”
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Video deepfake detection using a hybrid CNN-LSTM-Transformer model for identity verification by Petmezas, Georgios, Vanian, Vazgken, Konstantoudakis, Konstantinos, Almaloglou, Elena E. I., Zarpalas, Dimitris

“… Our model leverages 3DMMs for detailed facial feature extraction, a CNN for fine-grained spatial analysis, an LSTM for short-term temporal dynamics, and a Transformer for capturing long-term…”
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NEUROSPF: A Tool for the Symbolic Analysis of Neural Networks by Usman, Muhammad, Noller, Yannic, Pasareanu, Corina S., Sun, Youcheng, Gopinath, Divya

“… Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic…”
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Some Investigations of Machine Learning Models for Software Defects by Bhutamapuram, Umamaheswara Sharma

“…Software defect prediction (SDP) and software defect severity prediction (SDSP) models alleviate the burden on the testers by providing the automatic assessment of a newly-developed program in a short amount of time…”
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PROMAL: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning by Liu, Changlin, Wang, Hanlin, Liu, Tianming, Gu, Diandian, Ma, Yun, Wang, Haoyu, Xiao, Xusheng

“… A key component for these automated analysis techniques is to create a graphical user interface (GUI) model of an app, i.e…”
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Natural Attack for Pre-trained Models of Code by Yang, Zhou, Shi, Jieke, He, Junda, Lo, David

“… Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation…”
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Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned by Davis, Jesse, Bransen, Lotte, Devos, Laurens, Jaspers, Arne, Meert, Wannes, Robberechts, Pieter, Van Haaren, Jan, Van Roy, Maaike

“… from it. Typically, machine learning is used to build models and indicators that capture the skills, capabilities, and tendencies of athletes and teams…”
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An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair by 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…”
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D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis by Zheng, Yunhui, Pujar, Saurabh, Lewis, Burn, Buratti, Luca, Epstein, Edward, Yang, Bo, Laredo, Jim, Morari, Alessandro, Su, Zhong

“… The recent ability of Machine Learning models to understand programming languages opens new possibilities when applied to static analysis…”
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Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations by Applis, Leonhard, Panichella, Annibale, van Deursen, Arie

“… Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition tasks…”
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An Automated Fault Attack Framework for Block Ciphers Through Property Mining and Verification by Wang, Xingxin, Hu, Wei, Tang, Shibo, Wang, Xinxin, Zhou, Huisi

“… Taking inspiration from machine learning and formal verification, we propose an automated fault attack framework, which supports multi-byte fault attacks on both SPN and generalized Feistel structure ciphers…”
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Fault Localization with Code Coverage Representation Learning by Li, Yi, Wang, Shaohua, Nguyen, Tien

“… DeepRL4FL does so via novel code coverage representation learning (RL) and data dependencies RL for program statements…”
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What are we really testing in mutation testing for machine learning?: a critical reflection by Panichella, Annibale, Liem, Cynthia C. S.

“… In recent years, mutation testing has been extended to machine learning (ML) systems, and deep learning (DL) in particular…”
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