Digital Library[ Search Result ]
An Empirical Study of MISRA-C Related Source Code Changes in Open-source Software Projects
Suhyun Park, Jaechang Nam, Shin Hong
http://doi.org/10.5626/JOK.2024.51.8.718
This paper presents empirical studies on 75 open-source projects hosted on GitHub to explore how source code changes align with MISRA C coding guidelines. Through our analysis of the studied projects, we have identified eight distinctive keywords that represent the software domains where compliance with MISRA C coding guidelines is likely to be found. Additionally, we discovered that 54.75% of the studied projects utilizes at least one static rule checker. In the 75 studied projects, we found code changes associated with 75 MISRA C coding rules. The analyses of these code changes reveal that multiple MISRA C-related code changes often occur in a short timeframe, and, on average, each MISRA C-related code change modifies 1124 lines of code at once.
C++ based Deep Learning Open Source Framework WICWIU.v3 that Supports Natural Language and Time-series Data Processing
Junseok Oh, Chanhyo Lee, Okkyun Koo, Injung Kim
http://doi.org/10.5626/JOK.2023.50.4.313
WICWIU is the first open-source deep learning framework developed by Korean university. In this work, we developed WICWIU.v3 that includes features for natural language and time-series data processing. WICWIU was designed for C++ environment, and supports GPU-based parallel processing, and has excellent readability and extensibility, allowing users to easily add new features. In addition to WICWIU.v1 and v2 that focus on image processing models, such as convolutional neural networks (CNN) and general adversarial networks (GAN), WICWIU.v3 provides classes and functions for natural language and time-series data processing, such as recurrent neural networks (RNN), including LSTM (Long Short-Term Memory Networks) and GRU (Gated Recurrent Units), attention modules, and Transformers. We validated the newly added functions for natural language and time-series data by implementing a machine translator and a text generator with WICWIU.v3.
Performance-Aware Multi-Cloud Infrastructure Provisioning Based on Cloud-Barista Open Source Project
Seokho Son, Jihoon Seo, Byoungseob Kim, Dongjae Kang
http://doi.org/10.5626/JOK.2022.49.10.816
Cloud infrastructures have been expanding all over the world, and types of services have been diversified. Cloud users are adopting multi-cloud, in which two or more clouds are utilized to overcome restrictions that might arise from using a single cloud. However, using multi-cloud increases the complexity of provisioning and managing cloud resources. In this paper, to alleviate problems, we researched a way to efficiently provision multi-cloud infrastructures. In particular, we analyzed the performance of each cloud service through performance benchmarking and proposed a performance-based optimal provisioning technique. The main contribution points of this paper are as follows: 1) it introduces a dynamic provisioning structure of multi-cloud infrastructures implemented through CB-Tumblebug of the Cloud-Barista open source project, 2) it presents a mechanism for evaluating heterogeneous cloud infrastructures" performances, and 3) it analyzes results of performance experiments for major cloud infrastructures. Lastly, we demonstrated the process of configuring and provisioning multi-cloud infrastructures based on their performances through CB-MapUI, a web client for CB-Tumblebug. The effectiveness of the appropriate multi-cloud infrastructure configuration can be examined through the experimental results and demonstrations.
Fair Hungarian Algorithm for Swarming Drone Flight Formation Transformation
http://doi.org/10.5626/JOK.2022.49.6.459
The drone show impressed people through the convergence of technology and art in the sky during 2018 Pyeongchang Winter Olympics. For the stable swarm flight, the system should consider efficient communication, accurate position estimation, and fast and efficient scenario without collision between drones. Especially, the scenario transformation algorithm is a core technology of the drone show, and can be performed as an assignment problem. Hungarian algorithm is commonly used for the assignment problem. However, Hungarian algorithm is not suitable for formation transformation of the swarm flight, because the battery usage of individual drones is not taken into account. Thus, an increase in the amount of movement of some drones increases battery consumption and reduces operating time. In this paper, the fair Hungarian algorithm is proposed to increase operating time considering fair battery consumption. The proposed algorithm was verified using the swarming flight system at a drone show performed with 100 drones.
C++ based General-purpose Open Source Deep Learning Framework, WICWIU
Chunmyong Park, Jeewoong Kim, Yunho Kee, Jihyeon Kim, Seonggyeol Yoon, Eunseo Choi, Injung Kim
http://doi.org/10.5626/JOK.2019.46.3.253
In this paper, we introduce WICWIU, the first open source deep learning framework among Korean universities. WICWIU provides a variety of operators and modules together with a network structure that can represent an arbitrary general computational graph. The WICWIU features are sufficient to compose widely used deep learning models such as Inception, ResNet, and DenseNet. WICWIU also supports GPU-based massive parallel computing which significantly accelerates the training of neural networks. It is also easily accessible for C++ developers because the whole API is provided in C++. WICWIU has an advantage over Python-based frameworks in memory and performance optimization based on the C++ environment. This eases the customizability of WICWIU for environments with limited resources. WICWIU is readable and extensible because it is composed of C++ codes coupled with consistent APIs. With Korean documentation, it is particularly suitable for Korean developers. WICWIU applies the Apache 2.0 license which is available for any research or commercial purposes for free.
Efficient Similarity Analysis Methods for Same Open Source Functions in Different Versions
http://doi.org/10.5626/JOK.2017.44.10.1019
Binary similarity analysis is used in vulnerability analysis, malicious code analysis, and plagiarism detection. Proving that a function is equal to a well-known safe functions of different versions through similarity analysis can help to improve the efficiency of the binary code analysis of malicious behavior as well as the efficiency of vulnerability analysis. However, few studies have been carried out on similarity analysis of the same function of different versions. In this paper, we analyze the similarity of function units through various methods based on extractable function information from binary code, and find a way to analyze efficiently with less time. In particular, we perform a comparative analysis of the different versions of the OpenSSL library to determine the way in which similar functions are detected even when the versions differ.
Detection of an Open-Source Software Module based on Function-level Features
As open-source software (OSS) becomes more widely used, many users breach the terms in the license agreement of OSS, or reuse a vulnerable OSS module. Therefore, a technique needs to be developed for investigating if a binary program includes an OSS module. In this paper, we propose an efficient technique to detect a particular OSS module in an executable program using its function-level features. The conventional methods are inappropriate for determining whether a module is contained in a specific program because they usually measure the similarity between whole programs. Our technique determines whether an executable program contains a certain OSS module by extracting features such as its function-level instructions, control flow graph, and the structural attributes of a function from both the program and the module, and comparing the similarity of features. In order to demonstrate the efficiency of the proposed technique, we evaluate it in terms of the size of features, detection accuracy, execution overhead, and resilience to compiler optimizations.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr