Source Code Of System Research Articles

Innovative solutions: Design and implementation of an advanced national service training program (NSTP) portal for state universities and colleges in the Philippines

Technology is continually evolving, leading to unexpected breakthroughs, especially evident during the pandemic. The modern era has enhanced the quality of life for many people, and computer programs have transformed how individuals connect with each other and the world. This study aims to develop a National Service Training Program (NSTP) Portal System for the Department of State Universities and Colleges (SUCs) in the region. The researcher used developmental research methodologies, which involve examining, describing, and assessing the product development process. The system's source code was written in PHP, JavaScript, and jQuery, following the Agile Process. MySQL was used to create the system's database, storing all data, records, and information. The study employed developmental research methods to analyze and describe the product development process and evaluate the final product. Additionally, a descriptive research approach was used to gather information through survey questionnaires to assess the system's validity in terms of its features. This evaluation was conducted using a survey instrument based on the ISO/IEC 25010 Software Quality Standards. Department heads, academics, and students were consulted during the data collection for the portal development. The system's creation was successful.

Real-Time Prototype Electricity Monitoring and Forecasting System based on Wemos D1 R1 ESP8266 and IoT

Background - Technology helps people do things. IoT is a buzzword, but some need explanation. It means internet-connected devices. IoT seeks continuous internet benefits. Lighting, fans, automated doors. These things have internet-connected sensors. IoT technology allows internet-controlled household appliances. Internet-connected computers or smartphones monitor this tool.
 
 Purpose - The purpose of this research is to create a system that can monitor the electric power in the room of the house in real-time through the Blynk application using a smartphone and make it easier for consumers to estimate the cost of electricity for a month.
 
 Design/methodology/approach - Systematics simplify and summarize this study. This study will use the Blynk smartphone app to monitor room electric power in real-time and anticipate monthly electricity expenses. Literature review expands research theories. Analysis and design, explaining the analysis based on the background and literature review, then constructing tools and designing the source code and workflow of the Wemos D1 R1 ESP8266 Microcontroller system. Implementation and testing discuss experimental results and how to test new technology. Study report analysis and development ideas conclude this section.
 
 Findings – This study produced a guide for designing IoT-based power monitoring device applications. Allow users to estimate their household electricity costs quickly and keep this option available.
 
 Research limitations - This research is limited by the Wemos ESP8266 D1 R1 microcontroller, INA219 current sensor, and led for room lights. Wemos D1 R1 ESP8266 and IOT will be used to create a monitoring system tool and estimate electric power in real time using room fans. Future accomplishment. This method requires many components.
 
 Originality/value - This research may reveal how to lower electricity bills by monitoring and cutting usage online.

ISO/TS 21564:2019- based Evaluation of a Semantic Map between Variables in the ISARIC Freestanding Follow Up Survey and ORCHESTRA Studies

The COVID-19 pandemic has led to tremendous investment in clinical studies to generate much-needed knowledge on the prevention, diagnosis, treatment and long-term effects of the disease. Case report forms, comprised of questions and answers (variables), are commonly used to collect data in clinical trials. Maximizing the value of study data depends on data quality and on the ability to easily pool and share data from several sources. ISARIC, in collaboration with the WHO, has created a case report form that is available for use by the scientific community to collect COVID-19 trial data. One of such research initiatives collecting and analyzing multi-country and multi-cohort COVID-19 study data is the Horizon 2020 project ORCHESTRA. Following the ISO/TS 21564:2019 standard, a mapping between five ORCHESTRA studies’ variables and the ISARIC Freestanding Follow-Up Survey elements was created. Measures of correspondence of shared semantic domain of 0 (perfect match), 1 (fully inclusive match), 2 (partial match), 4 (transformation required) or 4* (not present in ORCHESTRA) as compared to the target code system, ORCHESTRA study variables, were assigned to each of the elements in the ISARIC FUP case report form (CRF) which was considered the source code system. Of the ISARIC FUP CRF’s variables, around 34% were found to show an exact match with corresponding variables in ORCHESTRA studies and about 33% showed a non-inclusive overlap. Matching variables provided information on patient demographics, COVID-19 testing, hospital admission and symptoms. More in-depth details are covered in ORCHESTRA variables with regards to treatment and comorbidities. ORCHESTRA’s Long-Term Sequelae and Fragile population studies’ CRFs include 32 and 27 variables respectively which were evaluated as a perfect match to variables in the ISARIC FUP CRF. Our study serves as an example of the kind of maps between case report form variables from different research projects needed to link ongoing COVID-19 research efforts and facilitate collaboration and data sharing. To enable data aggregation across two data systems, the information they contain needs to be connected through a map to determine compatibility and transformation needs. Combining data from various clinical studies can increase the power of analytical insights.

Turnstile File Transfer: A Unidirectional System for Medium-Security Isolated Clusters

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. The isolation prevents harm to networked systems. However, there are times when the data is required to move in such quantities or speeds that it makes downloading onto an intermediate device untenable. This study proposes a novel turnstile model, a mechanism for one-way file transfer from one enterprise system to another without allowing data leakage. This system protects data integrity and security by connecting the isolated environment to the external network via a locked-down interconnection. Using medium-security isolated clusters, the researchers successfully developed a unidirectional file transfer system that acts as a one-way “turnstile” for secure file transfer between systems not connected to the internet or other external networks. The Turnstile system (source code available at github.com/monnin/turnstile) provides unidirectional file transfer between two computer systems. The solution enabled data to be transferred from a source system to a destination system without allowing any data to be transferred back in the opposite direction. The researchers found an automated process of transferring external files to isolated clusters optimized the transfer speed of external files to isolated clusters using Linux distributions and commands.

Tailor-made Virtualization Monitor Design for CPU Virtualization on LEON Processors

In recent decades, mixed-criticality systems have been widely adopted to reduce the complexity and development times of real-time critical applications. In these systems, applications run on a separation kernel hypervisor, a software element that controls the execution of the different operating systems, providing a virtualized environment and ensuring the necessary spatial and temporal isolation. The guest code can run unmodified and unaware of the hypervisor or be explicitly modified to have a tight coupling with the hypervisor. The former is known as full virtualization, while the latter is known as para-virtualization. Full virtualization offers better compatibility and flexibility than para-virtualization at the cost of a performance penalty. LEON is a processor family that implements the SPARC V8 architecture and whose use is widespread in the field of space systems. To the best of our knowledge, all separation kernel hypervisors designed to support the development of mixed-criticality systems for LEON employ para-virtualization, which hinders the adaptation of real-time operating systems. This article presents the design of a Virtualization Monitor that allows guest real-time operating systems to run virtualized on LEON-based systems without needing to modify their source code. It is designed as a stand-alone component within a hypervisor and incorporates a set of techniques such as static binary rewriting, automatic code generation, and the use of operating system profiles. To validate the proposed solution, tests and benchmarks have been implemented for three guest systems, RTEMS, FreeRTOS, and Zephyr, analyzing the overhead introduced in certain situations characteristic of real-time applications. Finally, the same benchmarks have been run on AIR, one of the hypervisors that uses para-virtualization. The results obtained show that the use of the proposed techniques allows us to obtain similar results to those obtained using para-virtualization without the need to modify the source code of the guest real-time operating systems.

Requirements Traceability: Recovering and Visualizing Traceability Links Between Requirements and Source Code of Object-oriented Software Systems

Requirements Traceability: Recovering and Visualizing Traceability Links Between Requirements and Source Code of Object-oriented Software Systems

Requirements Traceability: Recovering and Visualizing Traceability Links Between Requirements and Source Code of Object-oriented Software Systems

Requirements Traceability: Recovering and Visualizing Traceability Links Between Requirements and Source Code of Object-oriented Software Systems

EMF-Syncer: scalable maintenance of view models over heterogeneous data-centric software systems at run time

With the increasing presence of cyber-physical systems (CPSs), like autonomous vehicle systems and digital twins, the future of software engineering is predicated on the importance of designing and developing data-centric software systems that can adapt intelligently at run time. CPSs consist of complex heterogeneous software components. Model-driven engineering advocates using software models to tame such complexity, capturing the relevant design concerns of such systems at different levels of abstraction. Yet most of the existing CPSs are engineered without considering MDE practices and tools, facing fundamental challenges when working with data: monitoring the program data at run time, syncing updates between program and model, dealing with heterogeneous data sources, and representing such observed data at run time to facilitate automated analysis. In this work, we introduce the notion of view models to explicitly represent parts of the domain knowledge implicitly embedded in the source code of a software system. This notion is equipped with a scalable bidirectional syncing mechanism that extracts view model instances from program snapshots at run time. The syncing mechanism is proposed from a conceptual point of view, independently of specific implementations and supports incremental view model update and view model maintenance. We show how this syncing mechanism is flexible enough to facilitate the non-intrusive adoption of MDE technology over existing MDE-agnostic heterogeneous data-centric systems. We study the run-time cost implied by the EMF-Syncer , the tool implementing this syncing mechanism for Java applications and view models atop the eclipse modeling framework (EMF) when executing data analytic and transformation tasks over large volumes of data in the presence of data updates at run time. An empirical evaluation of the EMF-Syncer has been conducted with an industry-targeted benchmark for decision support systems, analyzing performance and scalability. The novel syncing mechanism enables new opportunities to adopt MDE technology in heterogeneous data-centric systems.

Identifying refactoring opportunities for large packages by analyzing maintainability characteristics in Java OSS

The source code of a Java-based software system is often structured into packages. When packages are large, they often carry maintainability quality issues. In the literature, there is a lack of empirical evidence on the specific maintainability issues that occur when packages become too large. Our study fills this gap by performing relationship analysis of package size with respect to internal maintainability characteristics (coupling, cohesion, and complexity) using package-level metrics collected from 111 open-source Java projects provided in Qualitas Corpus. Our results show significantly higher maintainability issues in large packages as indicated by the maintainability metrics. We also report strong relationships of package size with cohesion (represented by the number of connected components in a package) and complexity (measured by the number of internal relationships in a package). Based on these strong associations with package size, we show that these cohesion and complexity metrics can be used to identify large package refactoring opportunities. Furthermore, we also discuss why some maintainability metrics (e.g., coupling metrics) may not be useful for refactoring large packages.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

A security vulnerability predictor based on source code metrics

Detecting security vulnerabilities in the source code of software systems is one of the most important challenges in the field of software security. We need an effective solution to discover and patch vulnerabilities before our valuable information is compromised. Security testing is a type of software testing that checks whether software is vulnerable to cyber attacks. This study aimed to pursue three main objectives: (1) The first goal is to identify the vulnerable functions of a C/C++ software program based on code metrics. This can reduce the cost of software security testing and also redirect the related activities to identified vulnerable functions rather than to the entire software, (2) The second goal is to identify the type of attack related to the vulnerability function, and (3) Finally, the ultimate goal is to analyze the relationship between code metrics and the vulnerabilities. This goal can help us understand which code structure is most likely to contain vulnerable code. This paper first aimed to create a comprehensive view of the source code of the target software using graph concepts. Second, a set of source code metrics and calculated by crawling on the related graph using the static analysis approach. Finally, the vulnerability prediction model presented in this paper is based on machine learning technique applied on metrics extracted from program source code. Compared to previous work, new achievements have been made in this paper. One of the most important ones is the very high accuracy detection of the proposed model in detecting the type of vulnerability. Moreover, 15 code metrics are used to predict vulnerabilities. Our analysis on feature importance indicates that what structure the software program code has, most likely, it will be vulnerable. Experimental results in 10 real projects (OpenSSL, SQLite, FreeType, LibTiff, Libxslt, Binutils, FFmpeg, ImageMagick, OpenSC, and rdesktop) indicated that the security testing predictor proposed in this paper could predict on average 89% of the really vulnerable functions of the source code and 86% of the vulnerability type of the detected functions correctly.

Utilizing source code syntax patterns to detect bug inducing commits using machine learning models

Detecting Bug Inducing Commit (BIC) or Just in Time (JIT) defect prediction using Machine Learning (ML) based models requires tabulated feature values extracted from the source code or historical maintenance data of a software system. Existing studies have utilized meta-data from source code repositories (we named them GitHub Statistics or GS), n-gram-based source code text processing, and developer’s information (e.g., the experience of a developer) as the feature values in ML-based bug detection models. However, these feature values do not represent the source code syntax styles or patterns that a developer might prefer over available valid alternatives provided by programming languages. This investigation proposed a method to extract features from its source code syntax patterns to represent software commits and investigate whether they are helpful in detecting bug proneness in software systems. We utilize six manually and two automatically labeled datasets from eight open-source software projects written in Java, C++, and Python programming languages. Our datasets contain 642 manually labeled and 4014 automatically labeled buggy and non-buggy commits from six and two subject systems, respectively. The subject systems contain a diverse number of revisions, and they are from various application domains. Our investigation shows the inclusion of the proposed features increases the performance of detecting buggy and non-buggy software commits using five different machine learning classification models. Our proposed features also perform better in detecting buggy commits using the Deep Belief Network generated features and classification model. This investigation also implemented a state-of-the-art tool to compare the explainability of predicted buggy commits using our proposed and traditional features and found that our proposed features provide better reasoning about buggy commit detection compared to the traditional features. The continuation of this study can lead us to enhance software effectiveness by identifying, minimizing, and fixing software bugs during its maintenance and evolution.

MedLatinEpi and MedLatinLit : Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts

We present and make available MedLatinEpi and MedLatinLit , two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatinEpi and MedLatinLit consist of 294 and 30 curated texts, respectively, labelled by author; MedLatinEpi texts are of epistolary nature, while MedLatinLit texts consist of literary comments and treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis tasks, such as authorship attribution, authorship verification, or same-author verification. Along with the datasets, we provide experimental results, obtained on these datasets, for the authorship verification task, i.e., the task of predicting whether a text of unknown authorship was written by a candidate author. We also make available the source code of the authorship verification system we have used, thus allowing our experiments to be reproduced, and to be used as baselines, by other researchers. We also describe the application of the above authorship verification system, using these datasets as training data, for investigating the authorship of two medieval epistles whose authorship has been disputed by scholars.

Research on Design Pattern Detection Method Based on UML Model with Extended Image Information and Deep Learning

Detecting relevant design patterns from system design or source code helps software developers and maintainers understand the ideas behind the design of large-scale, highly complicated software systems, thereby improving the quality of software systems. Currently, design pattern detection based on machine learning has become a hot research direction. Scholars have proposed many design pattern detection methods based on machine learning. However, most of the existing literature only reports the utilization of traditional machine learning algorithms such as KNN, decision trees, ANN, SVM, etc., which require manual feature extraction and feature selection. It is very difficult to find suitable and effective features for the detection of design patterns. In the previous research, we have initially explored a design pattern detection method based on graph theory and ANN. Based on the research work done, we speculate that if we can realize the end-to-end design pattern detection from system design or source code to design pattern with the help of the powerful automatic feature extraction and other advantages of deep learning, the detection effect can be further improved. This paper intends to first explore a UML model that extends image information, called colored UML, so as to transform the design pattern detection problem into an image classification problem; on this basis, the positive and negative sample sets and the system to be recognized are all expressed in the form of colored UML models, the convolutional neural network VGGNet is used to train the data set to extract features, and the extracted features are trained by the SVM for binary classification to judge the pattern instances. Experiments were carried out on three open-source projects. We used three non-machine learning design pattern detection methods and five design pattern detection methods based on traditional machine learning algorithms, as well as the method in this paper. In general, the method proposed in this paper achieved higher precision and recall, and for different programs and their patterns, the precision and recall were stable at more than 85% in most cases. The experimental results demonstrate that this paper can achieve a better effect in recognizing design patterns. The research is, therefore, of both theoretical significance and application value.

Topological Structure Analysis of Software Using Complex Network Theory

Due to people’s increasing dependence on software, the emergence of software defects will lead to serious consequences. And the essential cause of software defects is the increasing complexity of software. The premise of reducing software defects is to understand the software topology to ensure software quality. The software topology refers to the connection between the internal elements of the software, and it has become an important factor affecting the quality of the software. In this paper, we use complex network theory as a tool to analyze the software topology. Firstly, we extract the software structural information from the source code of the software system and abstract the extracted software structural information with software network theory. Secondly, the metrics widely used in complex networks are introduced to analyze the built software network. When tracking the values of these metrics in the software system, we have a deeper understanding of the software topology. These results provide a different dimension to understanding the software topology, which has important guiding significance for the subsequent understanding of the software and is also very useful for reducing software defects and ensuring software quality.

A measurable refinement method of design and verification for micro-kernel operating systems in communication network

A secure operating system in the communication network can provide the stable working environment, which ensures that the user information is not stolen. The micro-kernel operating system in the communication network retains the core functions in the kernel, and unnecessary tasks are implemented by calling external processes. Due to the small amount of code, the micro-kernel architecture has high reliability and scalability. Taking the micro-kernel operating system in the communication network prototype VSOS as an example, we employ the objdump tool to disassemble the system source code and get the assembly layer code. On this basis, we apply the Isabelle/HOL, a formal verification tool, to model the system prototype. By referring to the mathematical model of finite automata and taking the process scheduling module as an example, the security verification based on the assembly language layer is developed. Based on the Hoare logic theory, each assembly statement of the module is verified in turn. The verification results show that the scheduling module of VSOS has good functional security, and also show the feasibility of the refinement framework.

Forecast system source code, data and all figures for the paper "Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method".

Forecast system source code, data and all figures for the paper "Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method".

Improving performance and determinism of multitasking systems on the LEON architecture

Real-time systems are characterised by the fact that they have to meet a set of both functional and temporal requirements. Processor architectures have a significant impact on the predictability of software execution times and can add different sources of indeterminism depending on the features provided. The LEON processor family is the reference platform for space missions of the European Space Agency, with open-source implementations that are written in VHDL language. All versions of the LEON processors conform to the SPARC architecture Version 8. This architecture groups the general-purpose registers into windows to reduce memory transfer overhead in function calls. Unfortunately, this mechanism introduces indeterminism in software execution times at various levels. In this paper, we propose an extension to the original architecture that provides determinism for a configurable subset of tasks and interrupt service routines and eliminates the concurrency-related jitter, all this with a minimum cost in terms of FPGA resource utilisation. For the validation of the proposed solution, we have implemented the extension into the VHDL code of the LEON3 processor and modified the source code of the RTEMS operating system to make use of the new functionality.

On the energy self-sustainability of IoT via distributed compressed sensing

This paper advocates the use of the distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for energy self-sustainability. We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation. We provide theoretical analysis on the performance of both the classical compressive sensing (CS) approach and the proposed distributed CS (DCS)-based approach to data acquisition for EH IoT. Moreover, we perform an in-depth comparison of the proposed DCS- based approach against the distributed source coding (DSC) system. These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach, and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

A graph-based clustering algorithm for software systems modularization

Clustering algorithms , as a modularization technique, are used to modularize a program aiming to understand large software systems as well as software refactoring. These algorithms partition the source code of the software system into smaller and easy-to-manage modules (clusters). The resulting decomposition is called the software system structure (or software architecture). Due to the NP-hardness of the modularization problem, evolutionary clustering approaches such as the genetic algorithm have been used to solve this problem. These methods do not make much use of the information and knowledge available in the artifact dependency graph which is extracted from the source code. To overcome the limitations of the existing modularization techniques, this paper presents a new modularization technique named GMA (Graph-based Modularization Algorithm). In this paper, a new graph-based clustering algorithm is presented for software modularization. To this end, the depth of relationships is used to compute the similarity between artifacts, as well as seven new criteria are proposed to evaluate the quality of a modularization. The similarity presented in this paper enables the algorithm to use graph-theoretic information. To demonstrate the applicability of the proposed algorithm, ten folders of Mozilla Firefox with different domains and functions, along with four other applications, are selected. The experimental results demonstrate that the proposed algorithm produces modularization closer to the human expert’s decomposition (i.e., directory structure) than the other existing algorithms. The proposed algorithm is expected to help a software designer in the software reverse engineering process to extract easy-to-manage and understandable modules from source code.

Benchmark on Indexing Algorithms for Accelerating Molecular Similarity Search.

Structurally similar analogues of given query compounds can be rapidly retrieved from chemical databases by the molecular similarity search approaches. However, the computational cost associated with the exhaustive similarity search of a large compound database will be quite high. Although the latest indexing algorithms can greatly speed up the search process, they cannot be readily applicable to molecular similarity search problems due to the lack of Tanimoto similarity metric implementation. In this paper, we first implement Python or C++ codes to enable the Tanimoto similarity search via several recent indexing algorithms, such as Hnsw and Onng. Moreover, there are increasing interests in computational communities to develop robust benchmarking systems to access the performance of various computational algorithms. Here, we provide a benchmark to evaluate the molecular similarity searching performance of these recent indexing algorithms. To avoid the potential package dependency issues, two separate benchmarks are built based on currently popular container technologies, Docker and Singularity. The Singularity container is a rather new container framework specifically designed for the high-performance computing (HPC) platform and does not need the privileged permissions or the separated daemon process. Both benchmarking methods are extensible to incorporate other new indexing algorithms, benchmarking data sets, and different customized parameter settings. Our results demonstrate that the graph-based methods, such as Hnsw and Onng, consistently achieve the best trade-off between searching effectiveness and searching efficiencies. The source code of the entire benchmark systems can be downloaded from https://github.uconn.edu/mldrugdiscovery/MssBenchmark.