Data Flow Analysis Research Articles

Electric Load Profile of 5G Base Station in Distribution Systems Based on Data Flow Analysis

This paper proposes an electric load demand model of the 5th generation (5G) base station (BS) in a distribution system based on data flow analysis. First, the electric load model of a 5G BS is developed according to its components and their characteristics. Second, critical factors of the power consumption of 5G BS, including area, data flow with uncertainty number of activated terminals and data usage of the terminal, are discussed. A statistical model of the number of activated terminals and data usage of the terminal are established using a probability distribution, and Monte Carlo sampling is used to handle the uncertainties. Then, the model is extended for multiple terminal connections. A case study is conducted to analyze the impact of the critical factors on the load of 5G BS and the influence of 5G BSs load on the other loads in three typical areas. Numerical results demonstrate that the proposed model is effective and can be employed as an accurate representation of the 5G BS load profile for the analysis of load characteristics. Case studies also show that the 5G BS loads have diverse impact on different typical areas in a distribution system.

Steady-State Availability Evaluation for Heterogeneous Edge Computing-Enabled WSNs with Malware Infections

To evaluate the steady-state availability of heterogeneous edge computing-enabled wireless sensor networks (HECWSNs) with malware infections, we first propose a Stackelberg attack-defence game to predict the optimal strategies of malware and intrusion detection systems (IDSs) deployed in heterogeneous sensor nodes (HSNs). Next, we present a new malware infection model—heterogeneous susceptible-threatened-active-recovered-dead (HSTARD) based on epidemic theory. Then, considering the heterogeneity of sink sensor nodes and common sensor nodes and the malware attack correlation, we derive the state transition probability matrix of an HSN based on a semi-Markov process (SMP), as well as the steady-state availability of an HSN. Furthermore, based on a data flow analysis of HSNs, we deduce the steady-state availability of HECWSNs with various topologies, including the star topology, cluster topology, and mesh topology. Finally, numerical analyses illustrate the influence of the IDS parameters on the optimal infection probability of malware and reveal the effect of multiple factors on the steady-state availability of HSNs, including the initial infection rate, the infection change rate, and the malware attack correlation. In addition, we present data analyses of the steady-state availability of HECWSNs with various topologies, including the star topology, cluster topology, and mesh topology, which provide a theoretical basis for the design, deployment, and maintenance of high-availability HECWSNs.

An Automated Tool for Analysis and Tuning of GPU-Accelerated Code in HPC Applications

The US Department of Energy’s fastest supercomputers and forthcoming exascale systems employ Graphics Processing Units (GPUs) to increase the computational performance of compute nodes. However, the complexity of GPU architectures makes tailoring sophisticated applications to achieve high performance on GPU-accelerated systems a major challenge. At best, prior performance tools for GPU code only provide coarse-grained tuning advice at the kernel level. In this article, we describe GPA, a performance advisor that suggests potential code optimizations at a hierarchy of levels, including individual lines, loops, and functions. To gather the fine-grained measurements needed to produce such insights, GPA uses instruction sampling and binary instrumentation to monitor execution of GPU code. At the time of this writing, GPU instruction sampling is only available on NVIDIA GPUs. To understand performance losses, GPA uses data flow analysis to approximately attribute measured instruction stalls back to their causes. GPA then analyzes patterns of stalls using information about a program’s structure and the GPU architecture to identify optimization strategies that address inefficiencies observed. GPA then employs detailed performance models to estimate the potential speedup that each optimization might provide. Experiments with benchmarks and applications show that GPA provides useful advice for tuning GPU code. We applied GPA to analyze and tune a collection of codes on NVIDIA V100 and A100 GPUs. GPA suggested optimizations that it estimates will accelerate performance across the set of codes by a geometric mean of 1.21×. Applying these optimizations suggested by GPA accelerated these codes by a geometric mean of 1.19×.

Research and application of cluster distributed generation virtual power plant regulation mode based on multi-source heterogeneous data flow analysis

Abstract In view of the increasing demand for multi-source heterogeneous data interaction in cluster distributed power virtual power plant(VPP), this paper analyzes the data sources and data flows in each interactive system, and studies the interface strategy between these heterogeneous data and VPP; On this basis, a multi-source heterogeneous data integration platform is built. Based on the data platform, a two-level coordination mode of intelligent regulation of VPP is proposed and realized; Finally, a practical demonstration project is carried out to verify the feasibility of the relevant schemes.

Static data-flow analysis for software product lines in C

Many critical codebases are written in C, and most of them use preprocessor directives to encode variability, effectively encoding software product lines. These preprocessor directives, however, challenge any static code analysis. SPLlift, a previously presented approach for analyzing software product lines, is limited to Java programs that use a rather simple feature encoding and to analysis problems with a finite and ideally small domain. Other approaches that allow the analysis of real-world C software product lines use special-purpose analyses, preventing the reuse of existing analysis infrastructures and ignoring the progress made by the static analysis community. This work presents VarAlyzer, a novel static analysis approach for software product lines. VarAlyzer first transforms preprocessor constructs to plain C while preserving their variability and semantics. It then solves any given distributive analysis problem on transformed product lines in a variability-aware manner. VarAlyzer ’s analysis results are annotated with feature constraints that encode in which configurations each result holds. Our experiments with 95 compilation units of OpenSSL show that applying VarAlyzer enables one to conduct inter-procedural, flow-, field- and context-sensitive data-flow analyses on entire product lines for the first time, outperforming the product-based approach for highly-configurable systems.

AndroEvolve: automated Android API update with data flow analysis and variable denormalization

The Android operating system is frequently updated, with each version bringing a new set of APIs. New versions may involve API deprecation; Android apps using deprecated APIs need to be updated to ensure the apps’ compatibility with old and new Android versions. Updating deprecated APIs is a time-consuming endeavor. Hence, automating the updates of Android APIs can be beneficial for developers. CocciEvolve is the state-of-the-art approach for this automation. However, it has several limitations, including its inability to resolve out-of-method variables and the low code readability of its updates due to the addition of temporary variables. In an attempt to further improve the performance of automated Android API update, we propose an approach named AndroEvolve, that addresses the limitations of CocciEvolve through the addition of data flow analysis and variable name denormalization. Data flow analysis enables AndroEvolve to resolve the value of any variable within the file scope. Variable name denormalization replaces temporary variables that may present in the CocciEvolve update with appropriate values in the target file. We have evaluated the performance of AndroEvolve and the readability of its updates on 372 target files containing 565 deprecated API usages. Each target file represents a file from an Android application that uses a deprecated API in its code. AndroEvolve successfully updates 481 out of 565 deprecated API invocations correctly, achieving an accuracy of 85.1%. Compared to CocciEvolve, AndroEvolve produces 32.9% more instances of correct updates. Moreover, our manual and automated evaluation shows that AndroEvolve updates are more readable than CocciEvolve updates.

Scalable Path Search for Automated Test Case Generation

Test case generation is an important task during software testing. In this paper, we present a new test-case generation framework for C programs. This approach combines dataflow analysis and dynamic symbolic execution together, and more importantly, it efficiently searches the program path space for potential faults based on the tabu search strategy and the program fault statistics. Unlike the traditional symbolic execution, which explores the program space exhaustively and is difficult to apply to complicated programs effectively, our approach automatically explores the feasible paths of hidden faults with high probability. The scalable and efficient path search strategy facilitates bug finding with much fewer test cases generated. We implemented this approach, and the experimental results presented in this paper are attractive.

Explaining Static Analysis With Rule Graphs

As static data-flow analysis becomes able to report increasingly complex bugs, using an evergrowing set of complex internal rules encoded into flow functions, the analysis tools themselves grow more and more complex. In result, for users to be able to effectively use those tools on specific codebases, they require special configurations—a task which in industry is typically performed by individual developers or dedicated teams. To efficiently use and configure static analysis tools, developers need to build a certain understanding of the analysis’ rules, i.e., how the underlying analyses interpret the analyzed code and their reasoning for reporting certain warnings. In this article, we explore how to assist developers in understanding the analysis’ warnings, and finding weaknesses in the analysis’ rules. To this end, we introduce the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rule graphs</i> that expose to the developer selected information about the internal rules of data-flow analyses. We have implemented rule graphs on top of a taint analysis, and show how the graphs can support the abovementioned tasks. Our user study and empirical evaluation show that using rule graphs helps developers understand analysis warnings more accurately than using simple warning traces, and that rule graphs can help developers identify causes for false positives in analysis rules.

Software Project Duration Estimation Based on COSMIC Method Applied to Data Flow Diagram

Business process models are created before the detailed software design, during requirements phase of software development. Even disciplined agile methodology includes business process modeling, before development iterations start. Their use in an estimation of software development duration could be beneficial, because they provide sufficient elements for mapping with software design and development planning. In this paper we propose the method for software project duration estimation based on Common Software Measurement International Consortium (COSMIC) method, applied to data flow diagram. This method is based on data flow diagram analysis and extraction of primitive business procceses,data flows and data stores. The paper contributes with the approach to enhance COSMIC method with calculation of software development process duration based on both data movement-related and data-manipulation-related software functional sub-processes. Data movement-related functional sub-processes are derived from Create, Read, Update, Delete (CRUD) operations assigned to data flows. Calculation of data manipulation-related functional sub-processes duration is derived from number of business processes. The proposed metric enables calculating effort (expressed in Cosmic Functional Points units) and duration (presented with Man/Hours units). An example of the approach application and an empirical study demonstrates the applicability of the approach.

МЕТОД КЛАСИФІКАЦІЇ ДОДАТКІВ ТРАФІКА КОМП'ЮТЕРНИХ МЕРЕЖ НА ОСНОВІ МАШИННОГО НАВЧАННЯ В УМОВАХ НЕВИЗНАЧЕНОСТІ

The paper proposes a method for classifying applications of computer network traffic based on machine learning in conditions of uncertainty. Modern methods of classification of computer network traffic applications (such as the classification of transport layer protocols by port numbers) have significant shortcomings, which leads to and is the reason for the growth of research in the direction of classification of computer network traffic applications. The rapid growth in recent years of the types and number of transport layer network protocols increases the relevance of research in this area, the development of appropriate algorithms and methods for classifying applications of computer network traffic, which reduce computational complexity. At the present stage, the problem that needs to be urgently addressed is the classification of computer network traffic applications using appropriate protocols and encryption algorithms. A promising area of classification of computer network traffic applications is statistical methods, which are based on the analysis and identification of statistical characteristics of IP traffic. The most promising are the intellectual analysis of data flow, as well as machine learning technologies, which are currently widely used in related fields of science. The problem of research and training according to precedents is solved - classification of computer network traffic applications on the basis of pre-known set of attributes of their features, in order to improve the technical base of computer networks and theoretical base, while ensuring high performance and quality networks. example of using transport layer protocols (TCP / IP stack). The result of solving this problem is to assign the application, in accordance with the rules of the educational sample, to one of the outstanding classes, which are predetermined, which contains the relevant, but already classified applications. Statistical analysis and research of the attributes of Internet applications showed that the most important attributes associated with changes in the volume of Internet traffic flow are exponential. Fisher's criterion can be used to calculate anomalous changes in the amount of Internet traffic of applications to calculate averages. To classify Internet applications in data streaming mode, an algorithm for detecting the offset of the concept (drift) of data flow traffic is proposed for continuous data flow. Fisher's drift detector is based on the statistical characteristics of the attributes of Internet applications, analyzed using sliding windows that monitor changes in traffic current statistical characteristics of the attributes of applications.

Статический анализатор для языков с обработкой исключений

This paper describes static analysis for the languages with exception handling. A low level intermediate representation, which supports exceptions, is proposed in this study. Data flow analyses for unreachable code detection were described. А general scheme of static analysis that takes exception related paths into account was given. The algorithms were implemented as a part of the static analysis tool Svace for C++, Java and Kotlin languages.

Compiler IR-Based Program Encoding Method for Software Defect Prediction

With the continuous expansion of software applications, people's requirements for software quality are increasing. Software defect prediction is an important technology to improve software quality. It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers, which can estimate the software areas is clean or buggy. However, the current encoding methods are mainly based on the traditional manual features or the AST of source code. Traditional manual features are difficult to reflect the deep semantics of programs, and there is a lot of noise information in AST, which affects the expression of semantic features. To overcome the above deficiencies, we combined with the Convolutional Neural Networks (CNN) and proposed a novel compiler Intermediate Representation (IR) based program encoding method for software defect prediction (CIR-CNN). Specifically, our program encoding method is based on the compiler IR, which can eliminate a large amount of noise information in the syntax structure of the source code and facilitate the acquisition of more accurate semantic information. Secondly, with the help of data flow analysis, a Data Dependency Graph (DDG) is constructed on the compiler IR, which helps to capture the deeper semantic information of the program. Finally, we use the widely used CNN model to build a software defect prediction model, which can increase the adaptive ability of the method. To evaluate the performance of the CIR-CNN, we use seven projects from PROMISE datasets to set up comparative experiments. The experiments results show that, in WPDP, with our CIR-CNN method, the prediction accuracy was improved by 12% for the AST-encoded CNN-based model and by 20.9% for the traditional features-based LR model, respectively. And in CPDP, the AST-encoded DBN-based model was improved by 9.1% and the traditional features-based TCA+ model by 19.2%, respectively.

The Effectiveness of Accounting Information Systems on Vehicle Sales Growth

To get big profits, companies must have a good management system, namely how the business is managed professionally, one of which is having a sales accounting information system. This system helps companies record all transactions, thereby generating information that helps companies monitor business progress. This research was conducted on the basis of qualitative methods with descriptive analysis techniques. This research was conducted at PT. Istana Kebon Jeruk by analyzing data flow diagrams and internal control questionnaire techniques. The results show that the accounting system provides an actual picture of all business processes, thereby reducing various risks of loss. In addition, the accounting system provides information about sales and revenue trends, thereby helping operations managers make informed decisions.

FineDIFT: Fine-Grained Dynamic Information Flow Tracking for Data-Flow Integrity Using Coprocessor

Dynamic Information Flow Tracking (DIFT) is a technique that facilitates run-time data-flow analysis on a running process, allowing a system to overcome the limitations of finding data dependencies statically at compilation time. DIFT serves as the backbone for applications including data-flow integrity (DFI). However, previous uses of DIFT towards DFI often have large overhead in terms of hardware, software or both, and often cannot provide fine-granularity tracking for software object, such as variables. To address these limitations, we present FineDIFT as a DFI framework which utilizes DIFT to generate a live data-flow graph of a running process and perform hardware-based assisted analysis at fine-granularity, thus being able to enforce the application’s Data-Flow Graph (DFG). We provide a sample implementation on a RISC-V core with a performance overhead of 5.03% for BEEBS benchmarks and hardware overhead of 6% LUTs and 8% Flip-Flops in the FPGA implementation, if excluding the Content-Addressable Memory (CAM) like structure used for metadata storage. With CAM-like structure being synthesized using FPGA logic, the total hardware overhead is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 2 \times $ </tex-math></inline-formula> LUTs and 33% Flip-Flops compared to the original RISC-V core. We also use the real-world application and customized vulnerable application to demonstrate the effectiveness of the proposed framework in protecting computing systems.

Penerapan Manajemen Penanganan Keluhan Peserta Pelatihan (Studi Kasus Di Pusdiklat Badan Siber dan Sandi Negara )

Training centers as a place to conduct training receive complaints about the implementation of training and facilities available from training participants as recipients of services and the organizers of the training center as implementers. So far, there has not been a precise complaint handling mechanism so that complaints are often not conveyed to the authorities in charge so that they appear to be handling complaints slowly or not at all. Whereas complaints are one of the inputs needed to improve an organization, BSSN Training Center certainly requires an integrated complaint handling mechanism. So, complaints are input for organizational improvement but also can be resolved appropriately and in relatively short and observable time. This research used a descriptive method with a qualitative approach. This method will describe the complaint handling management process in detail in handling complaints of training participants at the BSSN Training Center. Qualitative analysis techniques used interpretive techniques. Data collection techniques used triangulation techniques that combine several techniques, namely document review, interviews, and discussion results. The method of analyzing data uses the method of analyzing data flow models. The study obtained results in the form of a description of the Complaints Handling Management Process in Handling Complaints of Participants in the BSSN Training Center which utilizes online platforms ranging from delivery to complaint report management. The Complaints Handling Management is more easy to do, faster because it cuts through the bureaucratic path and the measurement process is more measurable

USAGE OF HASH METHODS IN THE CRYPTOGRAPHIC DATA ANALYSIS

The tasks of information security system include identifying potential or actual sources of threat to system’s work and minimizing consequences of unauthorized influence on it. While solving them, arises the need of restoration of the initial state of the information system, especially the data integrity. While performing information message analysis the other task may be finding differences between two data fragments or their instances. This paper offers methods of the complex solution of the information security tasks and the analysis of data streams using the means of cryptography and presents the experience of developing the reliable implementation of these methods. The developed methods allow detecting falsifications in data part of the sent message and restoring the initial message. During the cryptographic analysis, the area of change in a data block is localized using cross hashing which is performed by computing the hash of information message block by block. The result is the program implementation of the offered method of information stream analysis that is based on comparing three frames of input data. The effectiveness of detecting falsifications in a data stream depending on algorithm’s sensitivity was researched with the developed instrument. The dependence of the share of falsifications detected by the system in the information block on the established maximum allowable relative deviation from the median and the properties of the input stream, in particular, the division of the input data into frames, was experimentally revealed. It is expected that the advantages of the method will be higher in the preliminary stage of data flow analysis related to its segmentation before addressing the selected fragments to more accurate and specialized algorithms.

Big Data and sustainable functioning of geotechnical systems

The authors use the theory of Big Data to discuss the stages of induced transformation in the Earth&rsquo;s interior. The amount of data on the mining sector exponentially grows with its digital transformation. Mining sciences call for efficient processing and integration of data acquired from numerous digital systems. Such data reach the amount of hundreds terabytes annually at a scale of a single company. This article gives grounds for the fact that the mining sector data belong to Big Data. The current world&rsquo;s mining industry uses a few hundreds of different digital technologies. The amount of incoming data is colossal as a consequence. At the same time, digital technologies are unequally distributed among the mining companies. Based on the data-flow analysis, the problem on systematization of sources and amounts of information on a geotechnical system is formulated and solved. The systematization allows demonstrating the scale of the information received and used, and discloses the principle of the data processing and storage. The trends of research in the sphere of Big Data towards sustainable functioning of geotechnical systems in correlation with the environment and society. Creation of the efficient processing methods for Big Data of a geotechnical systems can help reveal more general nature-and-technology interactions in the future. This can help studying transformation of the structure and properties of the Earth&rsquo;s interior under the manmade impact. Later on, Big Data can become a tool for the analyses of hydrological behavior and gas dynamics, environment, social aspects of subsoil use, etc.The study was carried out under state contracts between the Geophysical Center, IPKON RAS and the Ministry of Science and Higher Education of Russia. The authors appreciate participation of Associate Professor, Candidate of Engineering Sciences D. N. Radchenko and Head of Laboratory, Candidate of Engineering Sciences D. A. Klebanov from the Academician Melnikov Research Institute of Comprehensive Exploitation of Mineral Resources &ndash; IPKON RAS in this study.

Modeling Gene Expression and Protein Delivery as an End-to-End Digital Communication System

Introduction: Digital communication theories have been well-established and extensively used to model and analyze information transfer and exchange processes. Due to their robustness and thoroughness, they have been recently extended to the modeling and analyzing data flow, storage, and networking in biological systems. Methods: This article analyses gene expression from a digital communication system perspective. Specifically, network theories, such as addressing, error control, flow control, traffic control, and Shannon's theorem are used to design an end-to-end digital communication system representing gene expression. We provide a layered network model representing the transcription and translation of deoxyribonucleic acid (DNA) and the end-to-end transmission of proteins to a target organ. The layered network model takes advantage of digital communication systems' key features, such as efficiency and performance, to transmit biological information in gene expression systems. Results: Thus, we define the transmission of information through a bio-internetwork (LAN-WAN-LAN) composed of a transmitter network (nucleus of the cell, ribosomes and endoplasmic reticulum), a router (Golgi Apparatus), and a receiver network (target organ). Conclusion: Our proposal can be applied in critical scenarios such as the development of communication systems for medical purposes. For instance, in cancer treatment, the model and analysis presented in this article may help understand side effects due to the transmission of drug molecules to a target organ to achieve optimal treatments.

ImReMuDF: Redundant Mutants Identification Method Based on Definition and Reference of Variables

Mutation testing is an effective defect-based software testing method, but a large number of mutants lead to expensive testing costs, which hinders the application of variation testing in industrial engineering. To solve this problem and enable mutation testing to be applied in industrial engineering, this paper improves the method of identifying redundant mutants based on data flow analysis and proposes the inclusion relationship between redundant mutants, so that the redundancy rate of mutants is reduced. In turn, the cost of mutation testing can be reduced. The redundant mutants identification method based on definition and reference of variables (ImReMuDF) was validated and evaluated using 8 C programs. The minimum improvement in redundant mutant identification rate was 34.0%, and the maximum improvement was 71.3% in the 8 C programs tested, and the verification results showed that the method is feasible and effective and has been improved in reducing redundant mutants and effectively reducing the execution time of mutation testing.

Static detection of silent misconfigurations with deep interaction analysis

The behavior of large systems is guided by their configurations: users set parameters in the configuration file to dictate which corresponding part of the system code is executed. However, it is often the case that, although some parameters are set in the configuration file, they do not influence the system runtime behavior, thus failing to meet the user’s intent. Moreover, such misconfigurations rarely lead to an error message or raising an exception. We introduce the notion of silent misconfigurations which are prohibitively hard to identify due to (1) lack of feedback and (2) complex interactions between configurations and code. This paper presents ConfigX, the first tool for the detection of silent misconfigurations. The main challenge is to understand the complex interactions between configurations and the code that they affected. Our goal is to derive a specification describing non-trivial interactions between the configuration parameters that lead to silent misconfigurations. To this end, ConfigX uses static analysis to determine which parts of the system code are associated with configuration parameters. ConfigX then infers the connections between configuration parameters by analyzing their associated code blocks. We design customized control- and data-flow analysis to derive a specification of configurations. Additionally, we conduct reachability analysis to eliminate spurious rules to reduce false positives. Upon evaluation on five real-world datasets across three widely-used systems, Apache, vsftpd, and PostgreSQL, ConfigX detected more than 2200 silent misconfigurations. We additionally conducted a user study where we ran ConfigX on misconfigurations reported on user forums by real-world users. ConfigX easily detected issues and suggested repairs for those misconfigurations. Our solutions were accepted and confirmed in the interaction with the users, who originally posted the problems.