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Flexible control flow graph alignment for delivering data-driven feedback to novice programming learner

JOURNAL OF SYSTEMS AND SOFTWARE 2024年 210卷

作者： Chowdhury, Md Towhidul Absar Contractor, Maheen Riaz Rivero, Carlos R. Rochester Inst Technol One Lomb Mem Dr Rochester NY 14623 USA

Supporting learners in introductory programming assignments at scale is a necessity. This support includes automated feedback on what learners did incorrectly. Existing approaches cast the problem as automatically repairing learners' incorrect programs extrapolating the data from an existing correct program from other learners. However, such approaches are limited because they only compare programs with similar control flow and order of statements. A potentially valuable set of repair feedback from flexible comparisons is thus missing. In this paper, we present several modifications to CLARA, a data-driven automated repair approach that is open source, to deal with real-world introductory programs. We extend CLARA's abstract syntax tree processor to handle common introductory programming constructs. Additionally, we propose a flexible alignment algorithm over control flow graphs where we enrich nodes with semantic annotations extracted from programs using operations and calls. Using this alignment, we modify an incorrect program's control flow graph to match correct programs to apply CLARA's original repair process. We evaluate our approach against a baseline on the twenty most popular programming problems in Codeforces. Our results indicate that flexible alignment has a significantly higher percentage of successful repairs at 46% compared to 5% for baseline CLARA. Our implementation is available at https://***/towhidabsar/clara.

关键词： Automated program repair control flow graph Approximated graph alighment Data driven feedback

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A control flow graph Optimization Method for Enhancing Fuzz Testing

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IEEE ACCESS 2024年 12卷 169370-169378页

作者： He, Yuan Xue, Bo Zhang, Lina Lu, Chengyang Dali Univ Sch Math & Comp Sci Dali 671003 Peoples R China

The American Fuzzy Lop (AFL) is a tool that automatically generates random data to expose vulnerabilities in software programs. However, it has some drawbacks, such as the lack of visibility in test case generation and suboptimal allocation of test case variation energy. To address these issues, we propose an enhanced fuzz testing approach based on the Program control flow graph. Firstly, we construct the program control flow graph using a symbolic execution tool. The precise addresses of the assembly instructions are obtained using the IDA Pro reverse engineering tool. Secondly, the control flow graph is then optimized by adjusting for address jumps, and nodes without edges are considered difficult to reach within the program. A set of paths is then extracted by slicing the refined control flow graph, in which paths through these difficult nodes are marked with sensitive signals. Thirdly, the constraint solver then generates inputs capable of traversing each path within the set. Finally, these inputs are used as the initial seed for fuzz testing. In the testing process, the sensitive signal paths obtained during symbol execution serve as the benchmark for the fuzz testing energy allocation mechanism, and the seed passing through the sensitive signal path is endowed with higher variation energy to guide the seed variation and make the fuzz testing explore in the deeper level of the program. The experimental results show that the enhanced fuzz testing method based on the program control flow graph is more efficient than the original fuzz testing. On the Cyber Grand Challenge (CGC) test-set, this method finds 2.7 times more unique crashes compared to the original fuzz testing. In addition, our approach uncovers two more unique crashes than standard fuzz testing in two real-world programs.

关键词： flow graphs Resource management Image edge detection Random number generation Fuzzy logic Software quality Computer crashes Reverse engineering Benchmark testing control flow graph fuzz testing symbolic solution AFL

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Dynamic Taint Analysis with control flow graph for vulnerability Analysis

Dynamic Taint Analysis with Control Flow Graph for vulnerabi...

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2011 International conference on Instrumentation,Measurement,Computer,Communication and control (IMCCC2011)(第一届仪器测量、计算机通信与控制国际会议)

作者： Zhe Chen XinXin Zhang XiaoJuan Wang North electronic equipments research institute Beijing China Beijing Technology and Business University Beijing China

In recent years,software vulnerabilities are one of the main causes of security incidents in computer *** taint analysis is an efficient method to track the flow of sensitive information for soft vulnerability *** this paper,we present the method for dynamic taint analysis with control dependent is to tracking sensitive *** finally,the simulation results are proposed to show the correctness of the proposed methods.

关键词： control flow graph Dynamic Taint Analysis Implicit Information flow

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An Evaluation of Code Coverage Adequacy in Automatic Testing using control flow graph Visualization

An Evaluation of Code Coverage Adequacy in Automatic Testing...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Ani Rahmani Joe Lian Min Asri Maspupah Politeknik Negeri Bandung Bandung Indonesia

ISBN: (数字)9781728150338

ISBN: (纸本)9781728150345

There are some issues that frequently experienced by some software industries at the testing stage, that is the coverage test case. This is the extent to which the completeness of the test case in the tested unit program meets the code coverage criteria. Testers usually design test cases just by looking at the source code for which it is very likely that the test case is incomplete or missing. There are currently many tools to check the coverage test cases. These tools only provide information on the percentage of line code that has been executed. On the other hand, the information from the testing results is presented in the form of XML, HTML or other formats that contain the line code status. It causes the information is still difficult to understand, especially for the beginner testers. This paper explains the results of a study of code coverage visualization using a control flow graph (CFG) to assist the tester as an effort to fulfill the complete test case. The experiment was conducted by involving 32 respondents who were the testers from various industries and a number of students.

关键词： code coverage visualization adequacy test case control flow graph unit testing control flow diagram CASING INSPECTION Automatic testing Computer and computer services industries Coverage Source code line code

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Representing Database Application Interaction Structure Based on an Enhanced control flow graph

Representing Database Application Interaction Structure Base...

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2010 International Conference on Future Information Technology and Computing (FITC 2010)

作者： WANG Xi-wu,MI Dong,ZHONG Qi-gen,WANG Yin-long The Fifth Dept.of Ordnance Engeering College Shijiazhung,China

This paper explores an Enhanced control flow graph based methodology to representing database application interaction *** widely used in representing control flow structure of programs,CFG tends to confront with problems in representing SQL statements in database applications,namely the Interaction *** modeling SQL statements in DAs as finite state machines,data objects used and defined in SQL statements can be extracted from the states transfering,based on which new graphic nodes coresponding to the statements are constructed and pluged into the CFG,so that detailed information of interaction structure is intuitivelly and clearly represented in ECFG.

关键词： Program Representation control flow graph FSM Interaction Structure Representation

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Discovering Vulnerable Functions by Extrapolation: A control-flow graph Similarity Based Approach 12th

Discovering Vulnerable Functions by Extrapolation: A Control...

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12th International Conference on Information Systems Security (ICISS)

作者： Jain, Lokesh Chandran, Aditya Rawat, Sanjay Srinathan, Kannan Int Inst Informat Technol Hyderabad Andhra Pradesh India Vrije Univ Amsterdam Netherlands

ISBN: (纸本)9783319498065;9783319498058

We present a method for vulnerability extrapolation to identify vulnerable functions in source code. Given a known vulnerable function, the proposed method extrapolates to find similar functions in the code base. Vulnerability extrapolation is based on the observation that given a starting vulnerability, similar behavior may be present in many other functions. In order to capture similarity, we represent functions in terms of syntactic and semantic patterns. These patterns are based on several code features like API usage pattern, argument types and control flow graph (CFG) of the functions. We employ a recent technique, called graph kernel to compute similarity directly on the CFGs of functions. We empirically demonstrate the capabilities of the proposed method by evaluating real-world applications to identify vulnerabilities.

关键词： Software vulnerability control flow graph graph kernel Extrapolation Code similarity

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Classifying Malware Represented as control flow graphs using Deep graph Convolutional Neural Network 49

Classifying Malware Represented as Control Flow Graphs using...

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49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

作者： Yan, Jiaqi Yan, Guanhua Jin, Dong IIT Chicago IL 60616 USA SUNY Binghamton Binghamton NY 13902 USA

ISBN: (纸本)9781728100562

Malware have been one of the biggest cyber threats in the digital world for a long time. Existing machine learning-based malware classification methods rely on handcrafted features extracted from raw binary files or disassembled code. The diversity of such features created has made it hard to build generic malware classification systems that work effectively across different operational environments. To strike a balance between generality and performance, we explore new machine learning techniques to classify malware programs represented as their control flow graphs (CFGs). To overcome the drawbacks of existing malware analysis methods using inefficient and non-adaptive graph matching techniques, in this work, we build a new system that uses deep graph convolutional neural network to embed structural information inherent in CFGs for effective yet efficient malware classification. We use two large independent datasets that contain more than 20K malware samples to evaluate our proposed system and the experimental results show that it can classify CFG-represented malware programs with performance comparable to those of the state-of-the-art methods applied on handcrafted malware features.

关键词： malware classification control flow graph deep learning graph convolution

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Directional graph Transformer-Based control flow Embedding for Malware Classification 22nd

Directional Graph Transformer-Based Control Flow Embedding f...

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22nd International Conference on Intelligent Data Engineering and Automated Learning

作者： Moon, Hyung-Jun Bu, Seok-Jun Cho, Sung-Bae Yonsei Univ Dept Artificial Intelligence Seoul 03722 South Korea Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

ISBN: (纸本)9783030916077;9783030916084

Considering the fatality of malware attacks, the data-driven approach using massive malware observations has been verified. Deep learning-based approaches to learn the unified features by exploiting the local and sequential nature of control flowgraph achieved the best performance. However, only considering local and sequential information from graph-based malware representation is not enough to model the semantics, such as structural and functional nature of malware. In this paper, functional nature are combined to the control flowgraph by adding opcodes, and structural nature is embedded through DeepWalk algorithm. Subsequently, we propose the transformer-based malware control flow embedding to overcome the difficulty in modeling the long-term control flow and to selectively learn the code embeddings. Extensive experiments achieved performance improvement compared to the latest deep learning-based graph embedding methods, and in a 37.50% improvement in recall was confirmed for the Simda botnet attack.

关键词： Malware classification control flow graph graph embedding Attention mechanism Transformer encoder

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graph Learning on Instruction Stream-Augmented CFG for Malware Variant Detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3015-3030页

作者： Mi, Jiaxin Li, Qi Han, Zewei Liao, Weilue Fu, Junsong Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China China Informat Technol Secur Evaluat Ctr Beijing 100085 Peoples R China

As malware as a service (MaaS) and organized attacks develop and drive a shift in malware variant generation mechanism, current variant detection, designed to counter conventional obfuscation and anti-detection strategies, falls short in facing new challenges, particularly in identifying variants that maintain core functionalities while altering local behaviors, or those sharing similar code logic but diverge in actual functionalities. To tackle the problems, we present ISCMVD, an Instruction Stream-augmented CFG-based Malware Variant Detection scheme, melding control flow structures with machine semantic information from instruction streams within blocks to build a comprehensive functional representation for variants' basic and detailed behaviors. Leveraging a global-enhanced attentive graph neural network to integrate local and global functional features, we significantly boost the capture of representative stable primary behaviors' similarity from variants within the same family identifying variants generated under attackers' code rewriting, module modification, and other transformation means. Additionally, through cross-family associative analysis, we eliminate classification interference of variants' logic similarities stemming from the same organization generating. Evaluation results on public and real-world datasets demonstrate the superiority and robustness of ISCMVD with an average of 99.29% in AC and 99.25% in F1 and perform well even in few-shot cases. What's more important, we achieve a breakthrough in two special sample sets including variants related to MaaS and APT group, and outperform state-of-the-art methods under the current variant generation mechanism, proving its suitability for future trends.

关键词： Feature extraction Malware Encoding Codes Logic Market research Streams Semantics Interference Data mining Variant generation mechanism malware variant detection variant functionality control flow graph graph learning

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Towards Improving the Performance of Comment Generation Models by Using BytecodeInformation

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第2期51卷 503-520页

作者： Huang, Yuan Huang, Jinbo Chen, Xiangping Zheng, Zibin Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China GuangDong Engn Technol Res Ctr Blockchain Zhuhai 510006 Peoples R China Sun Yat Sen Univ Sch Journalism & Commun Guangzhou 510275 Peoples R China

Code comment plays an important role in program understanding, and a large number of automatic comment generation methods have been proposed in recent years. To get a better effect of generating comments, many studies try to extract a variety of information (e.g., code tokens, AST traverse sequence, APIs call sequence) from source code as model input. In this study, we found that the bytecode compiled from the source code can provide useful information for comment generation, hence we propose to use the information from bytecode to assist the comment generation. Specifically, we extract the control flow graph (CFG) from the bytecode and propose a serialization method to obtain the CFG sequence that preserves the program structure. Then, we discuss three methods for introducing bytecode information for different models. We collected 390,000 Java methods from the maven repository, and created a dataset of 101,124 samples after deduplication and preprocessing to evaluate our method. The results show that introducing the information extracted from the bytecode can improve the BLEU-4 of 7 comment generation models.

关键词： Codes Source coding Data mining flow graphs Transformers Training Software engineering Neural networks Java Data models Code comment comment generation control flow graph bytecode

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