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7th International Conference on Software and System Engineering (ICoSSE)

作者： Gyen, Attila Pataki, Norbert Eotvos Lorand Univ Dept Programming Languages & Compilers Budapest Hungary

ISBN: (纸本)9798350386608;9798350386592

Nowadays, visualization is one of the most important things an integrated development environment or any smart code editor needs to have. There are several different options when it comes to the visualization. It plays a crucial role in software development and has several important benefits, like understanding the code structure, debugging, or troubleshooting. code visualization enhances developer productivity, facilitates collaboration, and contributes to the maintainability and scalability of software projects. It is an essential aspect of modern software development practices. In this paper, we focus on one of the most widely used code editors - Visual Studio code - and on a core component that drives the Visual Studio code called Monaco Editor, mainly on how they can visualize different sections of a code and their complexity when it comes to visualization. Source code visualization refers to representing code visually to enhance understanding, analysis, and communication of software systems.

关键词： code visualization Monaco VS code Language Server Protocol

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CNN- and GAN-based classification of malicious code families: A code visualization approach

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 12472-12489页

作者： Wang, Ziyue Wang, Weizheng Yang, Yaoqi Han, Zhaoyang Xu, Dequan Su, Chunhua Univ Aizu Div Comp Sci Fukushima 9658580 Japan City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China Army Engn Univ PLA Coll Commun Engn Nanjing Peoples R China Guizhou Univ Sch Comp Sci & Technol Guiyang Peoples R China

Malicious code attacks have severely hindered the current development of the Internet technologies. Once the devices are infected with virus, the damages to companies and users are unpredictable. Although researchers have developed malware detection methods, the analysis result still cannot achieve the desired accuracy due to complicated malicious code families and fast-growing variants. In this paper, to solve this problem, we combine Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to design an efficient and accurate malware detection method. First, we implement a code visualization method and utilize GAN to generate more samples of malicious code variants in the role of data augmentation. Then, the lightweight AlexNet originated from CNN to classify malware families. Finally, simulation experiments are conducted to evaluate that our CNN plus GAN model can achieve a higher classification accuracy (i.e., 97.78%) compared with some related work.

关键词： code visualization convolutional neural networks generative adversarial networks malware detection

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code Confabulator: Harnessing LLMs to Compile code for visualization 15

Code Confabulator: Harnessing LLMs to Compile Code for Visua...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rayvanth, Nannapaneni Pranavi, Pasupuleti Suryaa, E. Challa, Venkatahemant Kumar Reddy Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

code visualization tools play a core role for visualization of the code and helping novices in understanding, analyzing, and optimization of their programs. Tools such as Algorithm Visualizer and PythonTutor are developed to make algorithm visualization possible across different programming languages like Python, Java, C, and C++. Despite their advantages, these tools suffer from unnecessary data collection, increased time complexity, and limitation in visualizing a few data structures. In our research, we want to introduce a more humanlike view of understanding and solve the shortcomings mentioned above by proposing a framework, which will make use of LLMs for extracting data from the code in Python, Java, C, or C++ to be visualized. In our framework, we have used LLMs instead of the traditional compiler for feasibility and adaptability. Our objective is the appropriateness evaluation of LLMs for this task, starting from freeform prompts. The LLM is asked to generate outputs in a given format;hence, these outputs are taken and converted into visualizations with a rule-based approach. The task will be done to enhance quality visualization and reduce time complexity while allowing the user to view the code at a human level view. We have also publicly released the framework so that people are able to provide further contributions and improvements. © 2024 IEEE.

关键词： code visualization Large language Models

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ROCAT on KATARIBE: code visualization for Communities

ROCAT on KATARIBE: Code Visualization for Communities

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4th Int Conf on Applied Computing and Information Technology / 3rd Int Conf on Computational Science/Intelligence and Applied Informatics / 1st Int Conf on Big Data, Cloud Computing, Data Science and Engineering (ACIT-CSII-BCD)

作者： Ichinose, Tomohiro Uemura, Kvohei Tanaka, Daiki Hata, Hideaki Iida, Hajimu Matsumoto, Kenichi Nara Inst Sci & Technol Grad Sch Informat Sci Takayama 8916-5 Ikoma Nara 6300192 Japan

ISBN: (纸本)9781509048717

As globally distributed software development has become generalized, social coding platforms like Github are becoming increasing needed for team collaboration. To share and help understand the overview of projects among teams, source code visualization is useful. In this paper, we present a new visual software development tool, Rocat, a city-like software code visualization in virtual reality. In addition, we integrate Rocat with a GitLabbased code hosting service, Kataribe. By integrating Rocat into Kataribe, line-grained code information can be visualized, and city visualization can be easily shared. We present our tool and provide the features of Rocat on liataribe. A Screencast for demonstration is available at the following URI. hittps://***?v ZQTT091v4No

关键词： code hosting code visualization Virtual reality

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Augmenting code Review Experience Through visualization

Augmenting Code Review Experience Through Visualization

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Working Conference on Software visualization (VISSOFT)

作者： Balci, Faruk Haliloglu, Dilruba Sultan Sahin, Onur Tilki, Cankat Yurtsever, Mehmet Ata Tuzun, Eray Bilkent Univ Ankara Turkey

ISBN: (纸本)9781665431446

code review is a systematic inspection of the codebase. It ensures that the software satisfies the required functionalities and standards;thus, it is an essential stage in the modern development process and is used frequently in the industry. For a successful review, the reviewer should be able to identify defects;therefore, the process is highly dependent on the awareness of the reviewer. Currently, code review is done by comparing the line-by-line differences on the codebase. However, this does not give structural information, such as design pattern changes or dependency changes between services. To address this problem, we propose an augmented next-generation code review experience using visual and holistic approaches to streamline the reviewing process. The proposed code review experience shows the structural representation of the change by visualizing it into a UML-like relationship diagram. Along with this diagram, the risk percentage for each commit is highlighted to emphasize the differences that possibly can be most affected by the change. An artifact map is also produced that includes issue-commit relation for bug tracking, to make reviewers aware of the frequent issues that are brought up. This map also highlights the change frequency of the files, for focusing the reviewers on the possibly vulnerable parts of the software for better quality reviews. The proposed visualizations have been implemented in a plugin-based proof-of-concept tool integrated within GitHub, supporting the visual reviewing of changes in codebases developed with Java language.

关键词： code review visual code review line-based dills pull request review UML code visualization

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Software Defect Prediction Method Based on Stable Learning

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Computers, Materials & Continua 2024年第1期78卷 65-84页

作者： Xin Fan Jingen Mao Liangjue Lian Li Yu Wei Zheng Yun Ge College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210012China School of Software Nanchang Hangkong UniversityNanchang330029China Software Testing and Evaluation Center Nanchang Hangkong UniversityNanchang330029China

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and *** previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data ***,target projects often lack sufficient data,which affects the performance of the transfer learning *** addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning *** address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual *** method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code *** the model training process,target project data are not required as prior *** the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the *** approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction *** evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE *** experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction ***,SDP-SL can effectively enhance within-and cross-project defect predictions.

关键词： Software defect prediction code visualization stable learning sample reweight residual network

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A GAN-EfficientNet-Based Traceability Method for Malicious code Variant Families

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Computers, Materials & Continua 2024年第7期80卷 801-818页

作者： Li Li Qing Zhang Youran Kong School of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains *** this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code *** method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code *** method includes a lightweight classifier and a *** classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other *** simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s *** process helps identify model vulnerabilities and security risks,facilitating model enhancement and *** classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,*** simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±*** classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network *** meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.

关键词： Malicious code variant traceability feature reuse lightweight neural networks code visualization attention mechanism

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Demonstration of CFlow: Supporting Semantic Flow Analysis of Students' code in Programming Problems at Scale 11

Demonstration of CFlow: Supporting Semantic Flow Analysis of...

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11h ACM Conference on Learning@Scale (L@S)

作者： Zhang, Ashley Ge Tang, Xiaohang Oney, Steve Chen, Yan Univ Michigan Ann Arbor MI 48109 USA Virginia Tech Blacksburg VA USA

ISBN: (纸本)9798400706332

Introductory programming courses have been growing rapidly, now enrolling hundreds or thousands of students. In such large courses, it can be overwhelmingly difficult for instructors to understand class-wide problem-solving patterns or issues, which is crucial for improving instruction and addressing important pedagogical challenges. In this paper, we propose a technique and system, CFlow, for creating understandable and navigable representations of code at scale. CFlow is able to represent thousands of code samples in a visualization that resembles a single code sample. CFlow creates scalable code representations by (1) clustering individual statements with similar semantic purposes, (2) presenting clustered statements in a way that maintains semantic relationships between statements, (3) representing the correctness of different variations as a histogram, and (4) allowing users to navigate through solutions interactively using semantic filters. With a multi-level view design, users can navigate high-level patterns, and low-level implementations. This is in contrast to prior tools that either limit their focus on isolated statements (and thus discard the surrounding context of those statements) or cluster entire code samples (which can lead to large numbers of clusters-for example, if there are.. code features and m implementations of each, there can be m(n) clusters).

关键词： code visualization Programming Education code Clustering

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CFlow: Supporting Semantic Flow Analysis of Students' code in Programming Problems at Scale 11

CFlow: Supporting Semantic Flow Analysis of Students' Code i...

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11h ACM Conference on Learning@Scale (L@S)

作者： Zhang, Ashley Ge Tang, Xiaohang Oney, Steve Chen, Yan Univ Michigan Ann Arbor MI 48109 USA Virginia Tech Blacksburg VA USA

ISBN: (纸本)9798400706332

Introductory programming courses have been growing rapidly, now enrolling hundreds or thousands of students. In such large courses, it can be overwhelmingly difficult for instructors to understand class-wide problem-solving patterns or issues, which is crucial for improving instruction and addressing important pedagogical challenges. In this paper, we propose a technique and system, CFlow, for creating understandable and navigable representations of code at scale. CFlow is able to represent thousands of code samples in a visualization that resembles a single code sample. CFlow creates scalable code representations by (1) clustering individual statements with similar semantic purposes, (2) presenting clustered statements in a way that maintains semantic relationships between statements, (3) representing the correctness of different variations as a histogram, and (4) allowing users to navigate through solutions interactively using semantic filters. With a multi-level view design, users can navigate high-level patterns, and low-level implementations. This is in contrast to prior tools that either limit their focus on isolated statements (and thus discard the surrounding context of those statements) or cluster entire code samples (which can lead to large numbers of clusters-for example, if there are.. code features and.. implementations of each, there can be m(n) clusters). We evaluated the effectiveness of CFlow with a comparison study, found participants using CFlow spent only half the time identifying mistakes and recalled twice as many desired patterns from over 6,000 submissions.

关键词： code visualization Programming Education code Clustering

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Challenges of Processing Data Clumps within Plugin Architectures of Integrated Development Environment 1

Challenges of Processing Data Clumps within Plugin Architect...

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1st IDE Workshop (IDE)

作者： Baumgartner, Nils Pulvermueller, Elke Osnabruck Univ Sch Math Comp Sci Phys Software Engn Res Grp Osnabruck Germany

ISBN: (纸本)9798400705809

In this study, we explore advanced strategies for enhancing software quality by detecting and refactoring data clumps, special types of code smells. Our approach transcends the capabilities of integrated development environments, utilizing a novel method that separates the detection of data clumps from the source access. This method facilitates data clump processing. We introduce a command-line interface plugin to support this novel method of processing data clumps. This research highlights the efficacy of modularized algorithms and advocates their integration into continuous workflows, promising enhanced code quality and efficient project management across various programming and integrated development environments.

关键词： IntelliJ Plugin Source Extraction code visualization Analysis Software Engineering Interactive

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