Search Results - (( (stated OR state) python code analysis ) OR ( (static OR stateeeeeen) python code analysis ))

Methods and Benchmark for Detecting Cryptographic API Misuses in Python by Frantz, Miles, Xiao, Ya, Pias, Tanmoy Sarkar, Meng, Na, Yao, Danfeng

“… The current static code analysis tools for Python are unable to scan the increasing complexity of the source code…”
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galpy: A python LIBRARY FOR GALACTIC DYNAMICS by Bovy, Jo

“…I describe the design, implementation, and usage of galpy, a python package for galactic-dynamics calculations…”
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DeVAIC: A tool for security assessment of AI-generated code by Cotroneo, Domenico, De Luca, Roberta, Liguori, Pietro

“… This research work introduces DeVAIC (Detection of Vulnerabilities in AI-generated Code), a tool to evaluate the security of AI-generated Python code, which overcomes the challenge of examining incomplete code…”
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PyTy: Repairing Static Type Errors in Python by Chow, Yiu Wai, Di Grazia, Luca, Pradel, Michael

“… As more and more code bases get type-annotated, static type checkers detect an increasingly large number of type errors…”
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PyCG: Practical Call Graph Generation in Python by Salis, Vitalis, Sotiropoulos, Thodoris, Louridas, Panos, Spinellis, Diomidis, Mitropoulos, Dimitris

“… We propose a pragmatic, static approach for call graph generation in Python. We compute all assignment relations between program identifiers of functions, variables, classes, and modules through an inter-procedural analysis…”
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MTpy: A Python toolbox for magnetotellurics by Krieger, Lars, Peacock, Jared R.

“… Written in Python, the code is open source, containing sub-packages and modules for various tasks within the standard MT data processing and handling scheme…”
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CryptoPyt: Unraveling Python Cryptographic APIs Misuse with Precise Static Taint Analysis by Guo, Xiangxin, Jia, Shijie, Lin, Jingqiang, Ma, Yuan, Zheng, Fangyu, Li, Guangzheng, Xu, Bowen, Cheng, Yueqiang, Ji, Kailiang

“… Based on PCAST, we design and implement CryptoPyt, a static code analysis tool that leverages precise taint analysis and 17 cryptographic misuse rules to automatically…”
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Analyzing LLM-Generated Code According to Four ISO/IEC 5055:2021 Categories by Krebs, Rasmus, Mazumdar, Somnath

“… This paper addresses a gap in current research by evaluating Python code generated by nine state-of-the-art LLMs according to the four main code quality categories…”
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Interactive Cross-Language Pointer Analysis for Resolving Native Code in Java Programs by Zhang, Chenxi, Liang, Yufei, Tan, Tian, Xu, Chang, Kan, Shuangxiang, Sui, Yulei, Li, Yue

“… While JNI mechanism significantly enhances the Java platform's capabilities, it also presents challenges for static analysis of Java programs due to the complex behaviors introduced by native code…”
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TESTING THE DYNAMIC EXECUTION OF PYTHON PROGRAM CODE DURING THE CERTIFICATION TESTING (DEVELOPMENT) STAGE IN THE CERTIFICATION SYSTEM OF THE MINISTRY OF DEFENSE OF RUSSIA by V.V. Samarov

“…" (State Technical Commission of Russia, Moscow, 1999) 1, hereinafter – RD NDV, along with static analysis, a dynamic analysis should be carried…”
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Inspect4py: A Knowledge Extraction Framework for Python Code Repositories by Filgueira, Rosa, Garijo, Daniel

“…This work presents inspect4py, a static code analysis framework designed to automatically extract the main features, metadata and documentation of Python code repositories…”
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MRSimulator: A cross-platform, object-oriented software package for rapid solid-state NMR spectral simulation and analysis by Srivastava, Deepansh J, Giammar, Matthew, Venetos, Maxwell C, McCarthy-Carney, Lexi, Grandinetti, Philip J

“…The open-source Python package, MRSimulator, is presented as a simple-to-use, fast, versatile, and extendable package capable of simulating one- and higher-dimensional Nuclear Magnetic Resonance (NMR…”
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Bloat beneath Python’s Scales: A Fine-Grained Inter-Project Dependency Analysis by Drosos, Georgios-Petros, Sotiropoulos, Thodoris, Spinellis, Diomidis, Mitropoulos, Dimitris

“… In this work, we conduct a large-scale, fine-grained analysis to understand bloated dependency code in the PyPI ecosystem…”
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Treefix: Enabling Execution with a Tree of Prefixes by Souza, Beatriz, Pradel, Michael

“…The ability to execute code is a prerequisite for various dynamic program analyses…”
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Legacy Code, Live Risk: Empirical Evidence of Malware Detection Gaps by Huang, Gang-Cheng, Lai, Tai-Hung

“… Our results reveal significant detection gaps: loaders compiled in legacy languages (Fortran, COBOL) consistently evade static and dynamic antivirus engines that easily flag their C…”
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SAGA: Detecting Security Vulnerabilities Using Static Aspect Analysis by Marquer, Yoann, Bianculli, Domenico, Briand, Lionel C

“… However, existing state-of-the-art analysis tools for Python only support a few vulnerability types…”
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Is Quantization a Deal-Breaker? Empirical Insights From Large Code Models by Afrin, Saima, Xu, Bowen, Mastropaolo, Antonio

“…). While recent studies have established quantization as a promising approach for optimizing large code models (LCMs…”
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Facial Emotion-Based Stress Analysis Using Python by Shaheed, Mohammad, Venkateshwarlu, Dr. S China, Nagaraju, Dr. V Siva, Bhavani, Ms. P Ganga

“…In the current fast-paced world, tracking mental health has become imperative. This project demonstrates a real- time stress detection and analysis system based on facial emotion recognition through deep learning approaches…”
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Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning by Zhao, Wenting, Ibrahim, Abdelaziz, Dolby, Julian, Srinivas, Kavitha, Helali, Mossad, Mansour, Essam

“… This flexibility, however, makes static analysis very hard. While creating a sound, or a soundy, analysis for Python remains an open problem, we present in this work Serenity…”
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Next-Generation Refactoring: Combining LLM Insights and IDE Capabilities for Extract Method by Pomian, Dorin, Bellur, Abhiram, Dilhara, Malinda, Kurbatova, Zarina, Bogomolov, Egor, Bryksin, Timofey, Dig, Danny

“… Given that Large Language Models (LLMs) have been trained on large code corpora, if we harness their familiarity with the way developers form functions, we could suggest refactorings that developers are likely to accept…”
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