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13th International Conference on Intelligent Tutoring Systems (ITS)

作者： Choudhury, Rohan Roy Yin, Hezheng Fox, Armando Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9783319395821;9783319395838

While the use of autograders for code correctness is widespread, less effort has focused on automating feedback for good programming style: the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We present a system that can provide real-time actionable code style feedback to students in large introductory computer science classes. We demonstrate that in a randomized controlled trial, 70% of students using our system achieved the best style solution to a coding problem in less than an hour, while only 13% of students in the control group achieved the same. Students using our system also showed a statistically-significant greater improvement in code style than students in the control group.

关键词： coding style Autograding Automatic hint generation MOOCs

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Autostyle: Toward coding style Feedback At Scale 19

AutoStyle: Toward Coding Style Feedback At Scale

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19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

作者： Choudhury, Rohan Roy Yin, HeZheng Moghadam, Joseph Fox, Armando Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450339506

While large-scale automatic grading of student programs for correctness is widespread, less effort has focused on automating feedback for good programming style: the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We hypothesize that with a large enough (MOOC-sized) corpus of submissions to a given programming problem, we can observe a range of stylistic mastery from naive to expert, and many points in between, and that we can exploit this continuum to automatically provide hints to learners to improve their code style based on the key stylistic differences between a given learner's submission and one that is stylistically slightly better. We present a system with two key interfaces. The first is an instructor-facing GUI that allows an instructor to browse student submissions clustered by stylistic patterns and view chains from a particular submission to a canonical one. The second is a student-facing GUI that allows a student to submit a solution and receive instantaneous style feedback.

关键词： coding style MOOCs autograding

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Bring Your Own coding style 25

Bring Your Own Coding Style

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25th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Ogura, Naoto Matsumoto, Shinsuke Hata, Hideaki Kusumoto, Shinji Osaka Univ Grad Sch Informat Sci & Technol Osaka Japan Nara Inst Sci & Technol Raduate Sch Informat Sci Nara Japan

ISBN: (纸本)9781538649695

coding style is a representation of source code, which does not affect the behavior of program execution. The choice of coding style is purely a matter of developer preference. Inconsistency of coding style not only decreased readability but also can cause frustration during programming. In this paper, we propose a novel tool, called styleCOORDINATOR, to solve both of the following problems, which would appear to contradict each other: ensuring a consistent coding style for all source codes managed in a repository and ensuring the ability of developers to use their own coding styles in a local environment. In order to validate the execution performance, we apply the proposed tool to an actual software repository.

关键词： coding style style feature style inconsistency software repository Git

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Robust PDF Files Forensics Using coding style 37th

Robust PDF Files Forensics Using Coding Style

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37th IFIP TC 11 International Conference on ICT Systems Security and Privacy Protection (IFIP SEC)

作者： Adhatarao, Supriya Lauradoux, Cedric Univ Grenoble Alpes INRIA Lyon France

ISBN: (纸本)9783031069758;9783031069741

Identifying how a file has been created is often interesting in security. It could be used by both attackers and defenders. Attackers can exploit this information to tune their attacks and defenders can understand how a malicious file has been created after an incident. As malicious PDF files are commonly used by attackers, in this work, we want to identify how a PDF file has been created. This problem is important because PDF files are extremely popular and widely used. Our approach to detect which software has been used to produce a PDF file is based on the coding style: given patterns that are only created by certain PDF producers. We have analysed the coding style of 900 PDF files produced using 11 PDF producer tools on 3 different Operating Systems. We have obtained a set of 192 rules which can be used to identify 11 PDF producers. We have tested our detection tool on 508836 PDF files published on scientific preprint servers. Our tool can detect certain producers with an accuracy of 100%. Its overall detection is still high (74%).

关键词： Forensics coding style PDF file

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RoPGen: Towards Robust Code Authorship Attribution via Automatic coding style Transformation 22

RoPGen: Towards Robust Code Authorship Attribution via Autom...

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ACM/IEEE 44th International Conference on Software Engineering (ICSE)

作者： Li, Zhen Chen, Guenevere (Qian) Chen, Chen Zou, Yayi Xu, Shouhuai Univ Texas San Antonio San Antonio TX 78249 USA Huazhong Univ Sci & Technol Wuhan Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Northeastern Univ Shenyang Peoples R China Univ Colorado Colorado Springs CO 80907 USA

ISBN: (纸本)9781450392211

Source code authorship attribution is an important problem often encountered in applications such as software forensics, bug fixing, and software quality analysis. Recent studies show that current source code authorship attribution methods can be compromised by attackers exploiting adversarial examples and coding style manipulation. This calls for robust solutions to the problem of code authorship attribution. In this paper, we initiate the study on making Deep Learning (DL)-based code authorship attribution robust. We propose an innovative framework called Robust coding style Patterns Generation (RoPGen), which essentially learns authors' unique coding style patterns that are hard for attackers to manipulate or imitate. The key idea is to combine data augmentation and gradient augmentation at the adversarial training phase. This effectively increases the diversity of training examples, generates meaningful perturbations to gradients of deep neural networks, and learns diversified representations of coding styles. We evaluate the effectiveness of RoPGen using four datasets of programs written in C, C++, and Java. Experimental results show that RoPGen can significantly improve the robustness of DL-based code authorship attribution, by respectively reducing 22.8% and 41.0% of the success rate of targeted and untargeted attacks on average.

关键词： Authorship attribution source code coding style robustness deep learning

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A TWO-STEP IN-CLASS SOURCE CODE PLAGIARISM DETECTION METHOD UTILIZING IMPROVED CM ALGORITHM AND SIM

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2011年第8期7卷 4729-4739页

作者： Ohno, Asako Murao, Hajime Shijonawate Gakuen Jr Coll Dept Life Design Osaka 5740011 Japan Kobe Univ Grad Sch Intercultural Studies Nada 6578501 Japan

Source code plagiarism is becoming one of the most serious problems in academia. There have been many proposed methods that attempt to detect source code plagiarism in programming classes. Most of them extract algorithmic features from the source code and measure the similarity between them. These methods show high levels of accuracy in evaluation experiments, and however, it is concerning that the similarity detected by the methods might not be caused by plagiarism. As a result, we propose a method called the CM Algorithm, which utilizes a student's coding style, the way the student writes source code, to check whether the source code submitted by the student was produced by him/her. In this paper, we propose a combined method that measures the similarity between source codes by using SIM [7], one of the well-known in-class source code plagiarism detection systems, and then checks the outputs of SIM against our improved CM Algorithm. The new method is expected to reduce false positives in plagiarism detection systems. This paper also gives a detailed explanation of the improved CM Algorithm, which assumes fluctuations in the source code produced by a student's coding style.

关键词： coding style In-class source code plagiarism coding model Hidden Markov model

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Long-term behavioural and neuroendocrine changes in Roman high-(RHA/Verh) and low-(RLA-Verh) avoidance rats following neonatal handling

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INTERNATIONAL JOURNAL OF DEVELOPMENTAL NEUROSCIENCE 1998年第3-4期16卷 165-174页

作者： Steimer, T Escorihuela, RM Fernández-Teruel, A Driscoll, P Univ Geneva Inst Univ Psychiat & Geriatr Dept Psychiat Unite Psychopharmacol ClinHUG CH-1225 Chene Bourg Switzerland Autonomous Univ Barcelona Dept Psychiat Toxicol & Forens Med Med Psychol Unit E-08193 Barcelona Spain ETH Inst Nutztierwissensch Physiol & Tierhaltung CH-8603 Schwerzenbach Switzerland

Roman high-(RHA/Verh) and low-(RLA/Verh) avoidance rats, originally selected and bred for rapid vs poor acquisition of a two-way active avoidance response, differ in emotional reactivity and coping style. These differences are associated with particular neuroendocrine and neurochemical characteristics. New data are presented here to show that the behavioural changes specifically induced by neonatal handling, i.e. decreased emotional reactivity, are associated with marked changes in the neuroendocrine responses of (hyperemotional) RLA/Verh rats to a novel environment. Eight months after neonatal handling, self-grooming behaviour, a reliable marker of emotional reactivity in this line of rats, was significantly decreased in RLA/Verh rats. Defecation scores were also significantly reduced in both lines. Moreover, there was a significant reduction in prolactin and corticosterone release following exposure to a novel environment in neonatally-handled RLA/Verh rats as compared to control, non-handled rats. No effects on prolactin and corticosterone release were observed in RHA/Verh rats. There was also no apparent effect of neonatal handling on coping style i.e. RLA/Verh rats did not increase their spontaneous exploration of novel environments. Thus, the phenotypic expression of basic traits of(high) neuroendocrine/emotional reactivity was specifically modulated by neonatal handling in RLA/Verh rats, whereas both the (hypo-emotional) RHA/Verh rats as well as coping style in both lines remained unaffected. Changes in emotional reactivity were still apparent at 12 months of age when rats from the same groups were tested for hyponeophagia. These results suggest that psychogenetically selected lines such as RHA/RLA rats are suitable animal models to investigate interactions between genes and the environment in determining individual sensitivity to stress and coping styles, as well as potential vulnerability (or resistance) to the development of maladaptive syndromes similar t

关键词： stress neonatal handling emotionality coding style corticosterone prolactic RHA/Verh-RLA/Verh rats genetic selection

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style Analysis for Source Code Plagiarism Detection - an Analysis of a Dataset of Student Coursework 17

Style Analysis for Source Code Plagiarism Detection - an Ana...

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17th IEEE International Conference on Advanced Learning Technologies (ICALT)

作者： Mirza, Olfat M. Joy, Mike Cosma, Georgina Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Nottingham Trent Univ Sch Sci & Technol Nottingham NG1 4FQ England

ISBN: (纸本)9781538638705

Plagiarism has become an increasing problem in higher education in recent years. coding style can be used to detect source code plagiarism that involves writing and deciding the structure of the code which does not affect the logic of a program, thus offering a way to differentiate between different code authors. This paper focuses to identify whether a data set consisting of student programming assignments is rich enough to apply coding style metrics to detect similarities between code sequences, and we use the BlackBox dataset as a case study.

关键词： component Source Code Plagairism Detection style Analysis coding style

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Work in Progress - A Novel Methodology to Reduce Instructors' and Students' Psychological Burdens in Source Code Plagiarism Detection

Work in Progress - A Novel Methodology to Reduce Instructors...

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40th Annual Frontiers in Education Conference

作者： Ohno, Asako Murao, Hajime Shijonawate Gakuen Jr Coll Daito Osaka Japan Kobe Univ Kobe Hyogo Japan

ISBN: (纸本)9781424462599

Detecting plagiarism in programming classes is a laborious task for instructors. Many methods have been proposed to reduce the physical strain on them, most of which measure algorithmic or structural similarity between a pair of source codes and are accurate in finding similar source codes. However, two important issues are yet to be discussed. Firstly, the similarity is not always caused by plagiarism;it could be caused by coincidence. Secondly, instructors might feel the burden of asking students if they have plagiarized someone's source code, and students might feel the burden of being asked. In this paper, we introduce our novel index for source code plagiarism detection, which can be utilized to check the cause of the similarity outputted from existing methods, report the result of questionnaire investigation with 89 freshmen in our junior college, and discuss the possibility of reducing psychological burdens in source code plagiarism detection with our method.

关键词： coding style In-class source codes Psychological burden Source code plagiarism

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Suitability of BlackBox Dataset for style Analysis in Detection of Source Code Plagiarism 7

Suitability of BlackBox Dataset for Style Analysis in Detect...

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7th International Conference on Innovative Computing Technology (INTECH)

作者： Mirza, Olfat M. Joy, Mike Cosma, Georgina Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Nottingham Trent Univ Sch Sci & Technol Nottingham NG1 4FQ England

ISBN: (纸本)9781509039890

Plagiarism is one of the most common problem that has been increasing in the field of higher education. Many research papers have highlighted the issue of plagiarism in context to its detection and source that is often obtained from the text books and online sources, there is a variety of easy ways for students to copy others' work. coding style can be used to detect source code plagiarism because it relates to programmer personality but does not affect the logic of a program, thus offering a way to differentiate between different code authors. The immediate objective of this paper is to identify whether a data set consisting of student programming assignments is rich enough to apply coding style metrics on in order to detect similarities between code sequences, and we use the BlackBox data set as a case study.

关键词： component Source Code Plagairism Detection style Analysis coding style

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