Relevant bibliographies by topics / Control Flow Graph (CFG)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Control Flow Graph (CFG)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Control Flow Graph (CFG)"

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Koppel, James, Jackson Kearl, and Armando Solar-Lezama. "Automatically deriving control-flow graph generators from operational semantics." Proceedings of the ACM on Programming Languages 6, ICFP (2022): 742–71. http://dx.doi.org/10.1145/3547648.

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We develop the first theory of control-flow graphs from first principles, and use it to create an algorithm for automatically synthesizing many variants of control-flow graph generators from a language’s operational semantics. Our approach first introduces a new algorithm for converting a large class of small-step operational semantics to an abstract machine. It next uses a technique called ”abstract rewriting” to automatically abstract the semantics of a language, which is used both to directly generate a CFG from a program (”interpreted mode”) and to generate standalone code, similar to a hu
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Qiu, Dehong, Jialin Sun, and Hao Li. "Improving Similarity Measure for Java Programs Based on Optimal Matching of Control Flow Graphs." International Journal of Software Engineering and Knowledge Engineering 25, no. 07 (2015): 1171–97. http://dx.doi.org/10.1142/s0218194015500229.

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Measuring program similarity plays an important role in solving many problems in software engineering. However, because programs are instruction sequences with complex structures and semantic functions and furthermore, programs may be obfuscated deliberately through semantics-preserving transformations, measuring program similarity is a difficult task that has not been adequately addressed. In this paper, we propose a new approach to measuring Java program similarity. The approach first measures the low-level similarity between basic blocks according to the bytecode instruction sequences and t
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Kapoor, Akshay, and Sunita Dhavale. "Control Flow Graph Based Multiclass Malware Detection Using Bi-normal Separation." Defence Science Journal 66, no. 2 (2016): 138. http://dx.doi.org/10.14429/dsj.66.9701.

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<p>Control flow graphs (CFG) and OpCodes extracted from disassembled executable files are widely used for malware detection. Most of the research in static analysis is focused on binary class malware detection which only classifies an executable as benign or malware. To overcome this issue, CFG based multiclass malware detection system that automatically classifies the malware into their respective families is proposed. The use Bi-normal separation (BNS) as a feature scoring metric. Experimental results show that proposed method using BNS outperforms compared to hitherto use technique of
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WANG, XUEXIANG, HANLAI PU, JUN YANG, and LONGXING SHI. "EXTENDED CONTROL FLOW GRAPH BASED PERFORMANCE AND ENERGY CONSUMPTION OPTIMIZATION USING SCRATCH-PAD MEMORY." Journal of Circuits, Systems and Computers 18, no. 04 (2009): 697–711. http://dx.doi.org/10.1142/s0218126609005204.

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A Scratch-Pad memory (SPM) allocation method to improve the performance of a specified application while reducing its energy consumption is presented in this paper. Integrated in the design is an extended control flow graph (ECFG) built directly from the application's instruction flow. The application of the design is transformed into a directed graph that consists of nodes and relationships. Likewise, to provide a solution in decreasing the overhead of moving nodes to SPM, the design is enhanced with a refined greedy algorithm based on ECFG. An experiment is conducted to prove the feasibility
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WONG, W. ERIC, and YU LEI. "REACHABILITY GRAPH-BASED TEST SEQUENCE GENERATION FOR CONCURRENT PROGRAMS." International Journal of Software Engineering and Knowledge Engineering 18, no. 06 (2008): 803–22. http://dx.doi.org/10.1142/s0218194008003878.

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One common approach to test sequence generation for structurally testing concurrent programs involves constructing a reachability graph (RG) and selecting a set of paths from the graph to satisfy some coverage criterion. It is often suggested that test sequence generation methods for testing sequential programs based on a control flow graph (CFG) can also be used to select paths from an RG for testing concurrent programs. However, there is a major difference between these two, as the former suffers from a feasibility problem (i.e., some paths in a CFG may not be feasible at run-time) and the l
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Dong, Kwan Kim. "Enhancing code clone detection using control flow graphs." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3804–12. https://doi.org/10.11591/ijece.v9i5.pp3804-3812.

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Code clones are syntactically or semantically equivalent code fragments of source code. Copy-and-paste programming allows software developers to improve development productivity, but it could produce code clones that can introduce non-trivial difficulties in software maintenance. In this paper, a code clone detection framework is presented with a feature extractor and a clone classifier using deep learning. The clone classifier is trained with true and false clones and then is tested with a test dataset to evaluate the performance of the proposed approach to clone detection. In particular, the
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Qiang, Weizhong, Shizhen Wang, Hai Jin, and Jiangying Zhong. "Fine-Grained Control-Flow Integrity Based on Points-to Analysis for CPS." Security and Communication Networks 2018 (October 17, 2018): 1–11. http://dx.doi.org/10.1155/2018/3130652.

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A cyber-physical system (CPS) is known as a mix system composed of computational and physical capabilities. The fast development of CPS brings new security and privacy requirements. Code reuse attacks that affect the correct behavior of software by exploiting memory corruption vulnerabilities and reusing existing code may also be threats to CPS. Various defense techniques are proposed in recent years as countermeasures to emerging code reuse attacks. However, they may fail to fulfill the security requirement well because they cannot protect the indirect function calls properly when it comes to
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Gao, Hao, Tong Zhang, Songqiang Chen, Lina Wang, and Fajiang Yu. "FUSION: Measuring Binary Function Similarity with Code-Specific Embedding and Order-Sensitive GNN." Symmetry 14, no. 12 (2022): 2549. http://dx.doi.org/10.3390/sym14122549.

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Binary code similarity measurement is a popular research area in binary analysis with the recent development of deep learning-based models. Current state-of-the-art methods often use the pre-trained language model (PTLM) to embed instructions into basic blocks as representations of nodes within a control flow graph (CFG). These methods will then use the graph neural network (GNN) to embed the whole CFG and measure the binary similarities between these code embeddings. However, these methods almost directly treat the assembly code as a natural language text and ignore its code-specific features
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Stiawan, Deris, Somame Morianus Daely, Ahmad Heryanto, Nurul Afifah, Mohd Yazid Idris, and Rahmat Budiarto. "Ransomware Detection Based On Opcode Behavior Using K-Nearest Neighbors Algorithm." Information Technology and Control 50, no. 3 (2021): 495–506. http://dx.doi.org/10.5755/j01.itc.50.3.25816.

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Ransomware is a malware that represents a serious threat to a user’s information privacy. By investigating howransomware works, we may be able to recognise its atomic behaviour. In return, we will be able to detect theransomware at an earlier stage with better accuracy. In this paper, we propose Control Flow Graph (CFG) asan extracting opcode behaviour technique, combined with 4-gram (sequence of 4 “words”) to extract opcodesequence to be incorporated into Trojan Ransomware detection method using K-Nearest Neighbors (K-NN)algorithm. The opcode CFG 4-gram can fully represent the detailed behavi
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Park, Jungsoo, and Souhwan Jung. "Android Adware Detection using Soot and CFG." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 13, no. 4 (2022): 94–104. http://dx.doi.org/10.58346/jowua.2022.i4.006.

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Adware is the most common type of malware. While considered not harmful in nature, it disrupts the user experience and generates unwanted revenue. Adware is also difficult to analyze and detect, therefore it is actively distributed through the Google Play Store. In the case of known adware detection, analysis takes a long time due to prolonged dynamic analysis to reveal Ads, and the result of network traffic analysis is difficult to determine traffic such as multimedia streaming, which results in a high probability of false positives. In this paper, we introduce a method to efficiently detect
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Dissertations / Theses on the topic "Control Flow Graph (CFG)"

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Sánchez, Yagüe Mónica. "Information extraction and validation of CDFG in NoGap." Thesis, Linköpings universitet, Datorteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93905.

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A Control Data Flow Graph (CDFG) is a Directed Acyclic Graph (DAG) in which a node can be either an operation node or a control node. The target of this kind of graph is to capture allt he control and data flow information of the original hardware description while preserving the various dependencies. This kind of graph is generated by Novel Generator of Accelerators and Processors (NoGap), a design automation tool for Application Specific Instruction-set Processor (ASIP) and accelerator design developed by Per Karlström from the Department of Electrical Engineering of Linköping University. Th
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Self, Joel P. "On-the-Fly Dynamic Dead Variable Analysis." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/886.

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State explosion in model checking continues to be the primary obstacle to widespread use of software model checking. The large input ranges of variables used in software is the main cause of state explosion. As software grows in size and complexity the problem only becomes worse. As such, model checking research into data abstraction as a way of mitigating state explosion has become more and more important. Data abstractions aim to reduce the effect of large input ranges. This work focuses on a static program analysis technique called dead variable analysis. The goal of dead variable analysis
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LI, ZHEN. "Control Flow Graph Based Attacks : In the Context of Flattened Programs." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155770.

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This report addresses de-obfuscation on programs. The targeted obfuscation scheme is the control flow flattening, which is an obfuscation method focusing on hiding the control flow of a program. This scheme introduces a special block named dispatcher into the program. The control flow of the program is reconstructed to be directed back to the dispatcher whenever the execution of a basic block ends. By doing this, in the flattened program, each basic block could be recognized as a precursor or a successor of any other basic blocks. While the realcontrol flow of the program is merely disclosed d
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Torbey, Elie. "Control/data flow graph synthesis using evolutionary computation and behavioral estimation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ37080.pdf.

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Stanier, James. "Removing and restoring control flow with the Value State Dependence Graph." Thesis, University of Sussex, 2012. http://sro.sussex.ac.uk/id/eprint/7576/.

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This thesis studies the practicality of compiling with only data flow information. Specifically, we focus on the challenges that arise when using the Value State Dependence Graph (VSDG) as an intermediate representation (IR). We perform a detailed survey of IRs in the literature in order to discover trends over time, and we classify them by their features in a taxonomy. We see how the VSDG fits into the IR landscape, and look at the divide between academia and the 'real world' in terms of compiler technology. Since most data flow IRs cannot be constructed for irreducible programs, we perform a
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Torbey, Elie Carleton University Dissertation Engineering Electronics. "Control/data flow graph synthesis using evolutionary computation and behavioral estimation." Ottawa, 1999.

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Stange, Yuri. "Visualization of Code Flow." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-162108.

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Visual representation of Control Flow Graphs (CFG) is a feature available in many tools, such as decompilers. These tools often rely on graph drawing frameworks which implement the Sugiyama hierarchical style graph drawing method, a well known method for drawing directed graphs. The main disadvantage of the Sugiyama framework, is the fact that it does not take into account the nature of the graph to be visualized, specically loops are treated as second class citizens. The question this paper attempts to answer is; how can we improve the visual representation of loops in the graph? A method bas
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Pogulis, Jakob. "Generation of dynamic control-dependence graphs for binary programs." Thesis, Linköpings universitet, Databas och informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110247.

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Dynamic analysis of binary files is an area of computer science that has many purposes. It is useful when it comes to debugging software in a development environment and the developer needs to know which statements affected the value of a specific variable. But it is also useful when analyzing a software for potential vulnerabilities, where data controlled by a malicious user could potentially result in the software executing adverse commands or executing malicious code. In this thesis a tool has been developed to perform dynamic analysis of x86 binaries in order to generate dynamic control-de
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Caron, David. "Generating a control/data-flow graph representation of a circuit from VHDL for use in circuit synthesis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0011/MQ27011.pdf.

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Matsusaki, Cristina Toshie Motohashi. "Redes F-MFG (Functional Mark Flow Graph) e sua aplicação no projeto de sistemas antropocêntricos." Universidade de São Paulo, 1998. http://www.teses.usp.br/teses/disponiveis/3/3132/tde-17122004-154654/.

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Este trabalho introduz a formalização algébrica do F-MFG (Functional Mark Flow Graph) para a análise e simulação computacional de modelos de sistemas antropocêntricos de produção, onde são enfocadas a interação e a interface do elemento humano com o sistema produtivo. Abordando os sistemas antropocêntricos como uma classe de sistemas a eventos discretos, o F-MFG, que é uma técnica baseada nas redes de Petri, comprova ter potencial para descrever detalhadamente as ações e estados do sistema. O F-MFG, em conjunto com a Metodologia PFS/MFG (Production Flow Schema/ Mark Flow Graph), estabelece um
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Book chapters on the topic "Control Flow Graph (CFG)"

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Wei, Chenfeng, Tong Wu, Rafael Sa Menezes, et al. "ESBMC v7.7: Automating Branch Coverage Analysis Using CFG-Based Instrumentation and SMT Solving." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90900-9_15.

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Abstract ESBMC, a bounded model checking (BMC) verifier based on SMT solving, has demonstrated its effectiveness in bug detection in recent software verification competitions. We extend its capabilities to enable branch coverage analysis and test suite generation. Our contributions are twofold: (1) we define a branch coverage property and instrument the control flow graph (CFG) to compute branch coverage using SMT solving, and (2) we propose an incremental multi-property reasoning algorithm for efficient and sound test case generation. ESBMC is ranked 7th in the category of Test-Comp 2025.
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Hills, Mark. "Streamlining Control Flow Graph Construction with DCFlow." In Software Language Engineering. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11245-9_18.

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Arlt, Stephan, Philipp Rümmer, and Martin Schäf. "A Theory for Control-Flow Graph Exploration." In Automated Technology for Verification and Analysis. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02444-8_44.

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Xia, Rongze, and Baojiang Cui. "Malware Classification Based on Graph Neural Network Using Control Flow Graph." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90072-4_13.

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Behera, Chandan Kumar, Genius Sanjog, and D. Lalitha Bhaskari. "Control Flow Graph Matching for Detecting Obfuscated Programs." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8848-3_26.

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Jo, Jang-Wu, and Byeong-Mo Chang. "Constructing Control Flow Graph for Java by Decoupling Exception Flow from Normal Flow." In Computational Science and Its Applications – ICCSA 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24707-4_14.

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Evstiougov-Babaev, exander A. "Call Graph and Control Flow Graph Visualization for Developers of Embedded Applications." In Software Visualization. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45875-1_26.

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Shin, Donghwi, Kwangwoo Lee, and Dongho Won. "Malware Variant Detection and Classification Using Control Flow Graph." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24106-2_23.

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Santos Filho, D. J., and P. E. Miyagi. "Enhanced Mark Flow Graph to Control Autonomous Guided Vehicles." In Computer Applications in Production Engineering. Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34879-7_89.

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Bruschi, Danilo, Lorenzo Martignoni, and Mattia Monga. "Detecting Self-mutating Malware Using Control-Flow Graph Matching." In Detection of Intrusions and Malware & Vulnerability Assessment. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11790754_8.

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Conference papers on the topic "Control Flow Graph (CFG)"

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Zhu, Wenyan, Hoiio Kong, Wenzheng Cai, and Wenhao Zhu. "Traffic flow prediction based on graph attention network." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039334.

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Xu, Yang, and Wenliang Peng. "Code Search Oriented Node-Enhanced Control Flow Graph Embedding." In 2024 IEEE International Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2024. https://doi.org/10.1109/scam63643.2024.00016.

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Yu, Yongfu, Xingsheng Xie, and Xing Ju. "DPGARN: Dynamic Periodic Graph Attention Recurrent Network for Traffic Flow Prediction." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662753.

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Ráth, Andre, Lotte de Winter, Lauge Naur Hansen, et al. "A Graph-Based Realistic 3D Microvascular Flow Simulation for Ultrasound." In 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS). IEEE, 2024. https://doi.org/10.1109/uffc-js60046.2024.10794092.

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Xu, Yuan, Fan Qin, Wei Ke, et al. "Local-Global Dynamic Information Fusion Graph Learning for Traffic Flow Prediction." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606619.

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Mothukuri, Viraaji, Reza M. Parizi, and James L. Massa. "LLMSmartSec: Smart Contract Security Auditing with LLM and Annotated Control Flow Graph." In 2024 IEEE International Conference on Blockchain (Blockchain). IEEE, 2024. http://dx.doi.org/10.1109/blockchain62396.2024.00064.

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Li, Jielong, Long Chen, Jinze Liu, Jun Zhao, and Wei Wang. "Prediction of Converter Gas Recovery Flow based on A Dynamic Multi-graph Fusion Network." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065742.

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Ma, Yi-Fan, Yali Du, and Ming Li. "Capturing the Long-Distance Dependency in the Control Flow Graph via Structural-Guided Attention for Bug Localization." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/249.

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To alleviate the burden of software maintenance, bug localization, which aims to automatically locate the buggy source files based on the bug report, has drawn significant attention in the software mining community. Recent studies indicate that the program structure in source code carries more semantics reflecting the program behavior, which is beneficial for bug localization. Benefiting from the rich structural information in the Control Flow Graph (CFG), CFG-based bug localization methods have achieved the state-of-the-art performance. Existing CFG-based methods extract the semantic feature
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Putra, Irfan Sofyana, Satrio Adi Rukmono, and Riza Satria Perdana. "Abstract Syntax Tree (AST) and Control Flow Graph (CFG) Construction of Notasi Algoritmik." In 2021 International Conference on Data and Software Engineering (ICoDSE). IEEE, 2021. http://dx.doi.org/10.1109/icodse53690.2021.9648437.

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Moreira, João, and Sandro Rigo. "Go With the FLOW: Fine-Grained Control-Flow Integrity for the Kernel." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/sbseg.2016.19322.

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This paper describes FLOW: a fine-grained control-flow integrity (CFI) implementation that focuses on protecting the Linux kernel. By combining source-code and binary analysis, FLOW maps valid execution paths into a fine-grained control-flow graph, which is later used to instrument the kernel with label-based CFI checks that prevent control-flow hijacking attacks. FLOW induces an average overhead of 17% on system call latency and 5% on I/O throughput, while its impact on real-world applications is ≈ 1%.
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Reports on the topic "Control Flow Graph (CFG)"

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Kularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.

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Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration
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