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Sequence-to-Sequence Learning-Based Conversion of Pseudo-code to source code Using Neural Translation Approach

IEEE ACCESS 2022年 10卷 26730-26742页

作者： Acharjee, Uzzal Kumar Arefin, Minhazul Hossen, Kazi Mojammel Uddin, Mohammed Nasir Uddin, Md Ashraf Islam, Linta Jagannath Univ Dept Comp Sci & Engn Dhaka 1100 Bangladesh Federat Univ Internet Commerce Secur Lab Ctr Informat & Appl Optimisat Ballarat Vic 3350 Australia

Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy.

关键词： codes Natural language processing Semantics Computer languages Deep learning Transformers Tokenization Sequence-to-sequence learning model natural language processing pseudo-code machine translation source code

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BiAn: Smart Contract source code Obfuscation

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第9期49卷 4456-4476页

作者： Zhang, Pengcheng Yu, Qifan Xiao, Yan Dong, Hai Luo, Xiapu Wang, Xiao Zhang, Meng Hohai Univ Coll Comp & Informat Nanjing 210000 Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen 518000 Peoples R China RMIT Univ Sch Comp Technol Melbourne Vic 3000 Australia Hong Kong Polytech Univ Dept Comp Hong Kong 999077 Peoples R China

With the rising prominence of smart contracts, security attacks targeting them have increased, posing severe threats to their security and intellectual property rights. Existing simplistic datasets hinder effective vulnerability detection, raising security concerns. To address these challenges, we propose BiAn, a source code level smart contract obfuscation method that generates complex vulnerability test datasets. BiAn protects contracts by obfuscating data flows, control flows, and code layouts, increasing complexity and making it harder for attackers to discover vulnerabilities. Our experiments with buggy contracts showed an average complexity enhancement of approximately 174% after obfuscation. Decompilers Vandal and Gigahorse had total failure rate increments of 38.8% and 40.5% respectively. Obfuscated contracts also decreased vulnerability detection rates in more than 50% of cases for ten widely-used static analysis detection tools.

关键词： Smart contracts codes source coding Security Complexity theory Intellectual property Layout Blockchain Ethereum smart contract source code obfuscation

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Reducing the Impact of Time Evolution on source code Authorship Attribution via Domain Adaptation

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第6期33卷 1-27页

作者： Li, Zhen Zhao, Shasha Chen, Chen Chen, Qian Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Texas San Antonio San Antonio TX 78249 USA Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

source code authorship attribution is an important problem in practical applications such as plagiarism detection, software forensics, and copyright disputes. Recent studies show that existing methods for source code authorship attribution can be significantly affected by time evolution, leading to a decrease in attribution accuracy year by year. To alleviate the problem of Deep Learning (DL)-based source code authorship attribution degrading in accuracy due to time evolution, we propose a new framework called Time Domain Adaptation (TimeDA) by adding new feature extractors to the original DL-based code attribution framework that enhances the learning ability of the original model on source domain features without requiring new or more source data. Moreover, we employ a centroid-based pseudo-labeling strategy using neighborhood clustering entropy for adaptive learning to improve the robustness of DL-based code authorship attribution. Experimental results show that TimeDA can significantly enhance the robustness of DL-based source code authorship attribution to time evolution, with an average improvement of 8.7% on the Java dataset and 5.2% on the C++ dataset. In addition, our TimeDA benefits from employing the centroid-based pseudo-labeling strategy, which significantly reduced the model training time by 87.3% compared to traditional unsupervised domain adaptive methods.

关键词： Authorship attribution source code time evolution deep learning domain adaptation

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Comparing techniques for authorship attribution of source code

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SOFTWARE-PRACTICE & EXPERIENCE 2014年第1期44卷 1-32页

作者： Burrows, Steven Uitdenbogerd, Alexandra L. Turpin, Andrew RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic 3001 Australia

Attributing authorship of documents with unknown creators has been studied extensively for natural language text such as essays and literature, but less so for non-natural languages such as computer source code. Previous attempts at attributing authorship of source code can be categorised by two attributes: the software features used for the classification, either strings of n tokens/bytes (n-grams) orsoftware metrics; and the classification technique that exploits those features, either information retrieval ranking or machine learning. The results of existing studies, however, are not directly comparable as all use different test beds and evaluation methodologies, making it difficult to assess which approach is superior. This paper summarises all previous techniques to source code authorship attribution, implements feature sets that are motivated by the literature, and applies information retrieval ranking methods or machine classifiers for each approach. Importantly, all approaches are tested on identical collections from varying programming languages and author types. Our conclusions are as follows: (i) ranking and machine classifier approaches are around 90% and 85% accurate, respectively, for a one-in-10 classification problem; (ii) the byte-level n-gram approach is best used with different parameters to those previously published; (iii) neural networks and support vector machines were found to be the most accurate machine classifiers of the eight evaluated; (iv) use of n-gram features in combination with machine classifiers shows promise, but there are scalability problems that still must be overcome; and (v) approaches based on information retrieval techniques are currently more accurate than approaches based on machine learning. Copyright copyright 2012 John Wiley & Sons, Ltd.

关键词： authorship attribution benchmarking information retrieval machine learning source code

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source code Verification Tools for Software Security Bugs

Source Code Verification Tools for Software Security Bugs

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5th International Conference on Software Methodologies, Tools and Techniques (SoMeT 2006)

作者： Michaud, Frederic Painchaud, Frederic Def Res & Dev Canada Valcartier Quebec City PQ G3J 1X5 Canada

ISBN: (纸本)9781586036737

We investigated errors and vulnerabilities that emerge from software defects in C/C++ and Java programs. This allowed us to create a meaningful testbench in order to evaluate best-of-breed automatic source code verification tools. Our results show that current static tools cannot significantly reduce the risk associated with confidential data processing in a military context. Dynamic tools should be used in conjunction in order to provide the necessary assurance level.

关键词： source code Verification Tools Static Analysis Dynamic Analysis Flaws Defects Errors Vulnerabilities C plus Java

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ASAP: A source code Authorship Program

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INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER 2019年第4期21卷 471-484页

作者： Tennyson, Matthew F. Murray State Univ Murray KY 42071 USA

source code authorship attribution is the task of determining who wrote a computer program, based on its source code, usually when the author is either unknown or under dispute. Areas where this can be applied include software forensics, cases of software copyright infringement, and detecting plagiarism. Numerous methods of source code authorship attribution have been proposed and studied. However, there are no known easily accessible and user-friendly programs that perform this task. Instead, researchers typically develop software in an ad hoc manner for use in their studies, and the software is rarely made publicly available. In this paper, we present a software tool called A source code Authorship Program (ASAP), which is suitable to be used by either the layperson or the expert. An author can be attributed to individual documents one at a time, or complex authorship attribution experiments can easily be performed on large datasets. In this paper, the interface and implementation of the ASAP tool is presented, and the tool is validated by using it to replicate previously published authorship attribution experiments.

关键词： Authorship attribution source code Software forensics Plagiarism detection Software copyright infringement Similarity search Information retrieval Machine learning

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How to Succeed in the Software Business While Giving Away the source code The AdaCore Experience

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IEEE SOFTWARE 2019年第6期36卷 17-22页

作者： Brosgol, Benjamin M. AdaCore New York NY 10001 USA

Open source software or, more accurately, freely licensed open source software (FLOSS) at first appears to present a dilemma when adopted as part of a business model. If users are allowed to access, modify, and/or redistribute the source code, how does a company protect its intellectual property and, more fundamentally, sell something that can be easily and legally reproduced?

关键词： DP Industry Industrial Property Public Domain Software source code Software Software Business source code Ada Core Experience Open source Software Business Model FLOSS Intellectual Property Licenses Market Research Software Tools Open source Software Business

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source code Based Component Recognition in Software Stacks for Embedded Systems

Source Code Based Component Recognition in Software Stacks f...

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IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications

作者： Schreiner, Dietmar Schordan, Markus Barany, Gergoe Goeschka, Karl M. Vienna Univ Technol Compilers & Languages Grp Argentinierstr 8 A-1040 Vienna Austria Vienna Univ Technol Distributed Syst Grp A-1040 Vienna Austria

ISBN: (纸本)9781424423675

Current trends in embedded systems software aim at an increase of reusability, exchangeability and maintainability and thus at a significant reduction of time- and costs-to-market. One way to reach these goals is the adaption of Component Based Software Engineering (CBSE) for the embedded systems domain. Unfortunately most existing embedded systems applications are realized as coarse-grained layered or even monolithic software that can hardly be reused. This paper demonstrates how to recognize reusable and exchangeable components within existing typically monolithic or stacked embedded systems software via a semi-automatic analysis of the system's source code. The complexity of the proposed analysis is kept linear to code size by utilizing expert-knowledge on the application-domain, and deployment specific configuration data. To prove our approach, a functional decomposition for an existing automotive middleware stock is calculated and is finally compared to a human designed one.

关键词： Embedded systems source code computer software functional decomposition computer programs Maintainability Reusable embedded system software

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source code Transformation for Software-Based On-Line Error Detection

Source Code Transformation for Software-Based On-Line Error ...

引用

IEEE Conference on Dependable and Secure Computing

作者： Tsai, Tsung-Yen Huang, Jiun-Lang Natl Taiwan Univ Grad Inst Elect Engn Taipei 106 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 106 Taiwan

ISBN: (纸本)9781509055692

With the widespread deployment of ICs into safetycritical systems, ensuring in-field reliability for the expected IC life span becomes a serious challenge. One solution to improving reliability is software-based on-line error detection;the idea is to empower the application program to detect run-time errors which may be permanent, intermittent, or transient. In this paper, we propose a set of high-level source code transformation rules to facilitate software-based on-line error detection. Compared to previous works, the proposed source code transformation rules improve the coverage on flow control constructs and can handle structures as well as structure pointers. Experiments on singlebit error injection show that in average only 1.08% of faults that modify the program behavior are undetected.

关键词： computer software Error detection Program Behavior source code Integrated circuits IN-LINE TIMING ERROR safety-critical software Coverage

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Detecting vulnerability in source code using CNN and LSTM network

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SOFT COMPUTING 2023年第2期27卷 1131-1141页

作者： Guo, Junjun Wang, Zhengyuan Li, Haonan Xue, Yang Xian Technol Univ Sch Comp Sci & Engn Xian Shanxi Peoples R China

Automated vulnerability detection has become a research hot spot because it is beneficial for improving software quality and security. The code metric (CM) is one class of important representations of vulnerability in source code. The implicit relationships among different metric attributes have not been sufficiently considered in traditional vulnerability detection based on CMs. In this paper, in view of the local perception capability of convolutional neural network (CNN) and the time-series prediction capability of long short-term memory (LSTM), we propose VulExplore, a compound neural network model for vulnerability detection that consists of a CNN for feature extraction and an LSTM network for deep representation. Moreover, to further indicate the vulnerability features in the source code, we reconstruct a CM dataset that includes two additional important attributes: maintainability index and average number of vulnerabilities committed per line. Our proposed numerical method can obtain both false-negative rate (FNR) and false-positive rate (FPR) under 20% and, meanwhile, achieve recall and precision over 80%, respectively.

关键词： Vulnerability detection code metrics (CMs) Convolutional neural network (CNN) Long short-term memory (LSTM) source code

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