Twin-Finder: Integrated Reasoning Engine for Pointer-Related Code Clone Detection

Xue, Hongfa; Mei, Yongsheng; Gogineni, Kailash; Venkataramani, Guru; Lan, Tian

doi:10.1109/iwsc50091.2020.9047638

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Techniques like CCFinder [11], VUDDY [12], SeClone [13], TwinFinder [14], Deckard [15] have high complexities in terms of usage of memory and processing power.…”

Section: Runtime Complexitymentioning

confidence: 99%

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SSA-HIAST: A Novel Framework for Code Clone Detection

Saini¹,

Singh²

2022

Computers, Materials &Amp; Continua

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In the recent era of software development, reusing software is one of the major activities that is widely used to save time. To reuse software, the copy and paste method is used and this whole process is known as code cloning. This activity leads to problems like difficulty in debugging, increase in time to debug and manage software code. In the literature, various algorithms have been developed to find out the clones but it takes too much time as well as more space to figure out the clones. Unfortunately, most of them are not scalable. This problem has been targeted upon in this paper. In the proposed framework, authors have proposed a new method of identifying clones that takes lesser time to find out clones as compared with many popular code clone detection algorithms. The proposed framework has also addressed one of the key issues in code clone detection i.e., detection of near-miss (Type-3) and semantic clones (Type-4) with significant accuracy of 95.52% and 92.80% respectively. The present study is divided into two phases, the first method converts any code into an intermediate representation form i.e., Hashinspired abstract syntax trees. In the second phase, these abstract syntax trees are passed to a novel approach "Similarity-based self-adjusting hash inspired abstract syntax tree" algorithm that helps in knowing the similarity level of codes. The proposed method has shown a lot of improvement over the existing code clones identification methods.

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“…Techniques like CCFinder [11], VUDDY [12], SeClone [13], TwinFinder [14], Deckard [15] have high complexities in terms of usage of memory and processing power.…”

Section: Runtime Complexitymentioning

confidence: 99%

“…For reducing false positives TwinFinder uses a feedback loop for formal loops to tune the machine learning algorithm. It lays special focus on false positives and was able to eliminate 99.32, 89 & 86.74% of false positives in bzip2, thttpd & Links respectively [14].…”

Section: Latest Work On Code Clone Detectionmentioning

confidence: 99%

SSA-HIAST: A Novel Framework for Code Clone Detection

Saini¹,

Singh²

2022

Computers, Materials &Amp; Continua

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“…Learning-based approach for vulnerability removal: Prior work has studied bug/vulnerabilities removal using learning-based approaches. StatSym [44,46] and SARRE [15] propose frameworks combining statistical and formal analysis for vulnerable path discovery. SIMBER [40] proposes a statistical inference framework to eliminate redundant bound checks and improve the performance of applications without sacrificing security.…”

Section: Related Workmentioning

confidence: 99%

AMASS: Automated Software Mass Customization via Feature Identification and Tailoring

Xue¹,

Chen²,

Venkataramani³

et al. 2019

ICST Transactions on Security and Safety

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The rapid inflation of software features brings inefficiency and vulnerabilities into programs, resulting in an increased attack surface with a higher possibility of exploitation. In this paper, we propose a novel framework for automated software mass customization (AMASS), which automatically identifies program features from binaries, tailors and eliminates the features to create customized program binaries in accordance with user needs, in a fully unsupervised fashion. It enables us to modularize program features and efficiently create customized program binaries at large scale. Evaluation using real-world executables including OpenSSL and LibreOffice demonstrates that AMASS can create a wide range of customized binaries for diverse feature requirements, with an average 92.76% accuracy for feature/function identification and up to 67% reduction of program attack surface.

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“…Hongfa Xue, et.al (2020) designed a new closed-loop known as Twin-Finder with the purpose of detecting the pointer-related code clone [26]. In this approach, the ML (Machine Learning) was put together with the symbolic execution methods for attaining precision.…”

mentioning

confidence: 99%

Analysis of Evolution of Recent Trends in Code Clone Detection Techniques from Antiquated Techniques

Sehrawat,

Solanki,

Dalal

et al. 2023

Data Science and Intelligent Computing Techniques

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The process used to detect and reveal bugs from software is known as software testing. This is a process as well as practice. This approach is different from software development. It can be recognized as an integral element of software development. Clone testing is a popular type of software testing. It is used during software development to check for duplication in software. This approach not only saves the time but the efforts of the software programmers. The copy and paste program generate the code over and over again. Therefore, a bug found in one unit recurs in each copy. This makes the removal of bugs and protection of the software complex. Code cloning complicates the protection of software. A variety of recent methods of code clone detection and their evolution from antiquated techniques has been studied in this paper.

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Twin-Finder: Integrated Reasoning Engine for Pointer-Related Code Clone Detection

Cited by 9 publications

References 36 publications

SSA-HIAST: A Novel Framework for Code Clone Detection

SSA-HIAST: A Novel Framework for Code Clone Detection

AMASS: Automated Software Mass Customization via Feature Identification and Tailoring

Analysis of Evolution of Recent Trends in Code Clone Detection Techniques from Antiquated Techniques

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