Source Software Research Articles

The ESCAPE Open-source Software and Service Repository

Purpose: The purpose of the ESCAPE Open-source Software and Service Repository (OSSR) is to provide a central location for the dissemination and use of trusted open-source software in the fields of astronomy, astroparticle physics, and particle physics. The repository allows users to easily access and download tools and services developed within the community, and to contribute their own tools and services. Methods: The ESCAPE project has set up a curated repository of software that provides tools and an environment to make it easy for users to find and download the software and services that they need. The repository is regularly updated and is maintained by a curation board, ensuring that the software and services are reliable and up-to-date. The curation and onboarding process makes the OSSR a trustworthy source of software that can be used for scientific analysis. The software included in the repository must include documentation and instructions and follow a set of modern best practices in software development. Training is provided to students and researchers to help them provide high-quality scientific software following modern software development practices. Outcome: The OSSR currently contains a wide range of software and services, including those for data management, data analysis, and machine learning. These tools and services are used by researchers and other users around the world. The OSSR has proven to be an effective means for disseminating and providing open-source software and services developed by the ESCAPE project partners and welcomes contributions from the entire community.

SPAYK: An environment for spiking neural network simulation

In research areas such as mobile robotics and computer vision, energy and computational efficiency have become critical. This has greatly increased interest in high-efficiency neuromorphic hardware and spiking neural networks. Because neuromorphic hardware is not yet widely available, spiking neural network studies are conducted by simulations. There are numerous simulators available today, each designed for a specific purpose. In this paper, a novel and open source package (SPAYK) for simulating spiking neural networks is presented. SPAYK has been proposed to speed up spiking neural network research. In the majority of simulators, networks are expressed with differential equations and require advanced neuroscience knowledge since such simulators are generally designed for brain and neuroscience research. SPAYK, on the other hand, is specifically designed as a framework to easily design spiking neural networks for practical problems. SPAYK is an easy-to-use Python package. There are three fundamental classes in the core: the model class for creating neuron groups, the organization class for simulating tissues, and the learning class for synaptic plasticity. While developing and testing the SPAYK environment, various experiments were carried out. This study includes three of these experiments. In the first experiment, we investigated the behavior of a group of Izhikevich neurons for visual stimuli. Also, a single Izhikevich neuron has been trained to respond to a particular label in a supervised manner with synaptic plasticity. In the second experiment, a well-known experiment was repeated to validate SPAYK. In this experiment, a neuron trained by synaptic plasticity can recognize repetitive patterns in a spike train. In the third experiment, a similar neuron was simulated with stimuli with multiple labels adapted from the MNIST dataset. It has been shown that the neuron can classify a particular label by synaptic plasticity. All these experiments and the SPAYK environment are presented as open-source tools.

What makes a readable code? A causal analysis method

AbstractContextCode readability is one of the most important quality attributes for software source code. To investigate which features affect code readability, most existing studies rely on correlation‐based methods. However, spurious correlations (a mathematical relationship wherein two variables appear to be causal but are not) involved in correlation‐based methods may affect research conclusions.ObjectiveIn order to remove spurious correlations and obtain conclusions from the perspective of causation as to what makes a readable code, we propose a causal theory‐based approach to analyze the relationship between code features and code readability scores.MethodFirst, we adopt the PC algorithm and additive noise models to construct the causal graph on the basis of the selected code features. Then, we use the linear regression algorithm based on the back‐door criterion to obtain the causal effect of different features on code readability.ResultWe conduct a set of experiments on readability data labeled by human annotators. The experimental results show that the average number of comments positively impacts code readability, with each additional unit increasing the code readability score by 0.799 points. Whereas the average number of assignments, identifiers, and periods have a negative impact, with each additional unit decreasing the code readability score by 0.528, 0.281, and 0.170 points respectively.ConclusionWe believe that our findings will provide developers with a better understanding of the patterns behind code readability, and guide developers to optimize their code as the ultimate goal.

Program Source-Code Re-Modularization Using a Discretized and Modified Sand Cat Swarm Optimization Algorithm

One of expensive stages of the software lifecycle is its maintenance. Software maintenance will be much simpler if its structural models are available. Software module clustering is thought to be a practical reverse engineering method for building software structural models from source code. The most crucial goals in software module clustering are to minimize connections between created clusters, maximize internal connections within clusters, and maximize clustering quality. It is thought that finding the best software clustering model is an NP-complete task. The key shortcomings of the earlier techniques are their low success rates, low stability, and insufficient modularization quality. In this paper, for effective clustering of software source code, a discretized sand cat swarm optimization (SCSO) algorithm has been proposed. The proposed method takes the dependency graph of the source code and generates the best clusters for it. Ten standard and real-world benchmarks were used to assess the performance of the suggested approach. The outcomes show that the quality of clustering is improved when a discretized SCSO algorithm was used to address the software module clustering issue. The suggested method beats the previous heuristic approaches in terms of modularization quality, convergence speed, and success rate.

Probabilistic Prediction of Oceanographic Velocities with Multivariate Gaussian Natural Gradient Boosting

Abstract Many single-output regression problems require estimates of uncertainty along with the point predictions. For this purpose, there exists a class of regression algorithms that predict a conditional distribution rather than a point estimate. The off-the-shelf options are much more limited, however, when the prediction output is multivariate and a joint measure of uncertainty is required. In this paper, we predict a distribution around a multivariate random vector of dimension P, such that the joint uncertainty would quantify the probability of any vector in P-dimensional space. This is more expressive than providing separate uncertainties in each dimension. To enable joint probabilistic regression, we propose a natural gradient boosting approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution, where we focus on the multivariate Gaussian distribution. Our method is robust, can be easily trained without extensive tuning, and performs competitively in comparison to existing approaches. The motivating application of our methodology is to predict two-dimensional oceanographic currents measured by freely floating Global Drifter Program drifters using remotely sensed data. We also demonstrate the method’s performance on simulated data. We find this method excels when strong correlation between output dimensions is present. As part of this work, we have added the model to the open source package at github.com/stanfordmlgroup/ngboost.

Dataset Characteristics for Reliable Code Authorship Attribution

Code authorship attribution aims to identify the author of software source code according to the author’s unique coding style characteristics. The lack of benchmark data in the field, forced researchers to employ various resources that often did not reflect real programming practices. Throughout the years, research studies have used textbook examples, students’ programming assignments, faculty code samples, code from programming competitions and files retrieved from open-source repositories as research objects. The diversity of the data raised concerns about the feasibility of capturing the appropriate data characteristics to reliably evaluate code attribution. In this paper, we investigate these concerns and analyze the effect of the dataset characteristics and feature elimination techniques on the accuracy of code attribution. Unlike the majority of the work done in this field, which mainly concentrates on designing new features, we explore the nature of the data used in previous studies and assess the factors that influence the attribution task. Within this analysis, we investigate the robustness of three feature sets regarded as reliable benchmarks in the attribution research. Based on our findings, we define a process for deriving a reduced set of features for accurate and predictable attribution and make recommendations on the dataset characteristics.

Continuous Integration for a development process of the information technology of remote monitoring and control

The current state of using CI/CD in commercial and open-source projects is analyzed. CI/CD pipelines are shown to be essential to the modern software development process, where they are used to configure workflows. Centralized and distributed approaches to building a software source code version control system are analyzed. For the web-oriented SCADA project, the choice and use of the version control system are justified. We provide concrete examples of using CI/CD that will allow ordinary developers, not DevOps specialists, to take the first steps to configure workflows for their projects and provide basic steps for configuring task execution using the example of the build and test phase of the source code of a real project that uses several programming languages. Figs: 4. Refs. 20 titles.
 Keywords: CI, CD, VCS, git, workflow, actions, building, testing, Agile.

Numerical Investigation on a Diffuser-Augmented Horizontal Axis Tidal Stream Turbine with the Entropy Production Theory

An implication of a turbine current is the development of a wake, a reduced speed flow, thus affecting the performance of an adjoined turbine. The aim of this study is to examine the turbine wake properties to offer a basic framework for the exploration of efficient turbine arrangements through the OpenFOAM source package and the entropy production theory. The results indicate that the diffuser inlet produces the largest entropy rate; however, this dissipates quickly after the rotor plane. In terms of vorticity, the Q and λ2-criterion results are sensitive to the isosurface thresholds. In general, the Ω-Rortex method proves a convenient and accurate solution for vortex visualization and identification. For the overall mean wake structure, the velocity profile follows a tadpole-shape, whilst the velocity deficits above 100% are observed around the nacelle and throat (diffuser) and behind the tower. The concentration of maximum turbulent intensities appears behind the throat of the diffuser and at the top and bottom of the tower. Owing to the swirling effect after rotor, we proposed recommended values of b0 = 10−5 for the hydrodynamic investigation of tidal stream turbines. The present findings extend our knowledge on the flow disruption due to shrouded turbines and are particularly relevant for farm project advisors.

RPM Packaging for Ansible Automation Configuration Management in Linux

Ansible automation is not a new configuration management method but is a widely used and accepted DevOps tool to manage Linux as well as non-Linux servers across the networks. It not only helps to manage systems from the control host but also enables remote administration tasks such as package management, system evaluation, security scans, user management as well as service configuration implementations. This paper utilises the same concept for systems management and enables the configuration files as well as ansible yaml files packages and shipped through fully secured and signed rpm package which guarantees that neither the rpm package is tampered nor the shipped ansible yaml playbooks are modified. The rpm package can be built on Red Hat Enterprise Linux and can also be signed to make sure that the package doesn't get any replaced files in between the time it is shipped by the vendor till it reaches the end user/customer. This paper will also talk about the rpm building best practices including spec files, public-private key pairs, rpm macros as well as yum metadata to host the package over the internet. The deployed package will make sure that it adheres to the complete lifecycle of the rpm package management, appropriate dependencies and suitable changelog as per the Open Source and Red Hat Package Management standards. Key Words: rpm, ansible, private-public keypair, rpm-macros- spec file, security.

Combining text mining with clinical decision support in clinical practice: a scoping review.

Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation. A search was conducted in PubMed, EMBASE, and Cochrane Library databases to identify full-text English language studies published before January 2022 with TM-CDS combination in clinical practice. Of 714 identified and screened unique publications, 39 were included. The majority of the included studies are related to diagnosis (n = 26) or prognosis (n = 11) and used a method that was developed for a specific clinical domain, document type, or application. Most of the studies selected text containing parts of the electronic health record (EHR), such as reports (41%, n = 16) and free-text narratives (36%, n = 14), and 23 studies utilized a tool that had software "developed for the study". In 15 studies, the software source was openly available. In 79% of studies, the tool was not implemented in clinical practice. Barriers to implement these tools included the complexity of natural language, EHR incompleteness, validation and performance of the tool, lack of input from an expert team, and the adoption rate among professionals. The available evidence indicates that the TM-CDS combination may improve diagnostic and therapeutic processes, contributing to increased patient safety. However, further research is needed to identify barriers to implementation and the impact of such tools in clinical practice.

What are the characteristics of highly-selected packages? A case study on the npm ecosystem

With the popularity of software ecosystems, the number of open source components (known as packages) has grown rapidly. Identifying high-quality and well-maintained packages from a large pool of packages to depend on is a basic and important problem, as it is beneficial for various applications, such as package recommendation and package search. However, no systematic and comprehensive work focuses on addressing this problem except in online discussions or informal literature and interviews. To fill this gap, in this paper, we conducted a mixed qualitative and quantitative analysis to understand how developers identify and select relevant open source packages. In particular, we started by surveying 118 JavaScript developers from the npm ecosystem to qualitatively understand the factors that make a package to be highly-selected within the npm ecosystem. The survey results showed that JavaScript developers believe that highly-selected packages are well-documented, receive a high number of stars on GitHub, have a large number of downloads, and do not suffer from vulnerabilities. Then, we conducted an experiment to quantitatively validate the developers’ perception of the factors that make a highly-selected package. In this analysis, we collected and mined historical data from 2,527 packages divided into highly-selected and not highly-selected packages. For each package in the dataset, we collected quantitative data to present the factors studied in the developers’ survey. Next, we used regression analysis to quantitatively investigate which of the studied factors are the most important. Our regression analysis complements our survey results about highly-selected packages. In particular, the results showed that highly-selected packages tend to be correlated by the number of downloads, stars, and how large the package’s readme file is.

Experimental and numerical study of Taylor flow in a square minichannel

A numerical and experimental study of Taylor bubbles in a square minichannel with a side of 1 mm has been carried out. A three-dimensional numerical simulation was performed using the volume of fluid method in the open source package OpenFOAM. An experimental study was performed using a high-speed shadow method and automatic processing. The characteristic flow regimes are investigated, with the main attention being paid to the Taylor regime. In the course of the work, the calculated and experimental data were compared, and their good agreement was shown. The distribution of velocities in a liquid and gas, as well as the distribution of the liquid film thickness in a bubble, is studied. The thickness of the liquid film in the corner and the center of the channel is compared with the corresponding well-known correlations. A dependence that describes the thickness of a liquid film in a square channel is proposed. Investigations of the streamline both in the liquid near the bubble and in the bubble itself. It is shown that in the square channel in front of the bubble there are four stable vortexes in the direction of the channel corners. Inside the bubble there is a specific flow from the tail to the nose of the bubble. There is a swirling of the gas in the transverse direction in the bubble.

Research of Obfuscated Malware with a Capsule Neural Network

The paper presents the results of a research of using transfer training of the capsule neural network to detect malware. The research was carried out on the basis of the source code of malware using the context-triggered piecewise hashing method. The source codes of malware were obtained from public sources of software. Verification of the capsule neural network learning results was carried out using a trained convolutional neural network, and publicly available sources of test to malware. The research was conducted on six types of malware. Software source code, part of capsule neural network training datasets, pre-trained capsule neural network, and full research are publicly available at https://github.com/T-JN.

Holographic PIV/PTV for nano flow rates-A study in the 70 to 200nL/min range.

Accurately measuring flow rates is a key requirement in many medical applications such as infusion and drug delivery systems. A major drawback of current systems is the low resolution of the sensors in the low flow rate regime. In this article, we present a method based on Holographic PIV/PTV that has been used for the first time to measure flow rates in the range of a few nL/min. Our method requires a very simple setup that combines lensless holography with particle velocimetry. For flow rates in the 70 to 200nL/min range, the highest uncertainty was 5.6% (coverage factor k=2). This is an open-source project; the CAD designs and software source code are available at https://github.com/gui-miotto/holovel.

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data.

Determining the correct localization of post-translational modifications (PTMs) on peptides aids in interpreting their effect on protein function. While most algorithms for this task are available as standalone applications or incorporated into software suites, improving their versatility through access from popular scripting languages facilitates experimentation and incorporation into novel workflows. Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. Access to internal objects for scoring and working with modified peptides adds to the toolbox for working with PTMs in Python. pyAscore is available as an open source package for Python 3.6+ on all major operating systems and can be found at pyascore.readthedocs.io.

An open source package to perform basic and advanced statistical analysis of turbulence data and other complex systems

We present an open-source package that helps the user to perform a basic statistical analysis of a given turbulence dataset. We believe that this package is useful to the entire turbulence community. It can be used to estimate statistical quantities of turbulence such as the spectral density, turbulence intensity, integral length scale, Taylor microscale, Kolmogorov scale, and dissipation rate as well as velocity increments, structure functions, and their scaling exponents. Different well-known methods available in the literature were selected so that they can be compared. This package also performs an advanced analysis that includes the scale-dependent statistical description of turbulent cascade using the Fokker–Planck equation, consequently leading to the assessment of integral fluctuation theorems. As a precondition for the stochastic process approach, Markovian properties of the turbulent cascade in scale are tested. This is utilized to estimate the drift and diffusion coefficients of the Fokker–Planck equation. The total entropy production for each independent cascade trajectory can be determined using a Fokker–Planck equation. Estimating total entropy production admits a rigorous law of non-equilibrium stochastic thermodynamics, namely, the integral fluctuation theorem, which must be valid if Markov properties hold and the Fokker–Planck equation is correct. This approach to the turbulent cascade process has the potential for a new way to link the statistical description of turbulence, non-equilibrium stochastic thermodynamics, and local turbulent flow structures. Finally, we emphasize that the presented package can be used for the analysis of other data with turbulent-like complexity as well.

Approach to Predict Software Vulnerability Based on Multiple-Level N-gram Feature Extraction and Heterogeneous Ensemble Learning

Software vulnerabilities are one of the roots of computer security problems. The traditional static analysis and dynamic analysis methods based on software source code mainly have some deficiencies, such as high false positive rate, high false negative rate and insufficient semantic information captured. Nevertheless, the application of machine learning, Natural Language Processing and other technologies in software vulnerability prediction can effectively mitigate such issues. This paper proposed a vulnerability prediction method based on multiple-level N-gram feature extraction and heterogeneous ensemble learning. First, by code intermediate representation and constructing a multiple-level N-gram feature generation model, two kinds of N-gram semantic features with different window size and different granularity at word and char level were extracted to retain the semantic and structural information of code. Second, TF–IDF was used to construct the vector space model as the input of prediction model. As a single classifier was prone to overfitting and poor generalization, this paper conducted benchmark testing on five classical machine learning algorithms (NB, SVM, DT, LR, RF), and then combined four (SVM, DT, LR, RF) among them, which had better performance as the base classifiers to form the stacking heterogeneous ensemble method to build the vulnerability prediction model. Finally, the proposed method was verified on buffer overflow vulnerability and resource management vulnerability datasets, with a lowest false positive rate and false negative rate which can reach 1.58% and 4.06%, respectively.

An Experience Report on Producing Verifiable Builds for Large-Scale Commercial Systems

Build verifiability is a safety property for a software system which can be used to check against various security-related issues during the build process. In summary, a verifiable build generates equivalent build artifacts for every build instance, allowing independent auditors to verify that the generated artifacts correspond to their source code. Producing a verifiable build is a very challenging problem, as non-equivalences in the build artifacts can be caused by non-determinsm from the build environment, the build toolchain, or the system implementation. Existing research and practices on build verifiability mainly focus on remediating sources of non-determinism. However, such a process does not work well with large-scale commercial systems (LSCSs) due to their stringent security requirements, complex third party dependencies, and large volumes of code changes. In this paper, we present an experience report on using a unified process and a toolkit to produce verifiable builds for LSCSs. A unified process contrasts with the existing practices in which recommendations to mitigate sources of non-determinism are proposed on a case-by-case basis and are not codified in a comprehensive tool. Our approach supports the following three strategies to systematically mitigate non-equivalences in the build artifacts: remediation, controlling, and interpretation. Case study on three LSCSs within <inline-formula><tex-math notation="LaTeX">${{\sf Huawei}}$</tex-math></inline-formula> shows that our approach is able to increase the proportion of verified build artifacts from less than 50 to 100 percent. To cross-validate our approach, we successfully applied our approach to build 2,218 open source packages distributed under <inline-formula><tex-math notation="LaTeX">${{\sf CentOS}}$</tex-math></inline-formula> 7.8, increasing the proportion of verified build artifacts from 85 to 99 percent with minimal human intervention. We also provide an overview of our mitigation guideline, which describes the recommended strategies to mitigate various non-equivalences. Finally, we present some discussions and open research problems in this area based on our experience and lessons learned in the past few years of applying our approach within the company. This paper will be useful for practitioners and software engineering researchers who are interested in build verifiability.

Düzen: generating the structural model from the software source code using shuffled frog leaping algorithm

Düzen: generating the structural model from the software source code using shuffled frog leaping algorithm

End-to-End Deep Fault-Tolerant Control

PUBLISHED ON IEEE/ASME TRANSACTIONS ON MECHATRONICS, DOI: 10.1109/TMECH.2021.3100150. Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-loop control of mechatronic systems. As a consequence, sensor faults will prevent the system from working correctly, unless a fault-tolerant control (FTC) architecture is adopted. As model-based FTC algorithms for nonlinear systems are often challenging to design, this paper focuses on a new method for FTC in the presence of sensor faults, based on deep learning. The considered approach replaces the phases of fault detection and isolation and controller design with a single recurrent neural network, which has the value of past sensor measurements in a given time window as input, and the current values of the control variables as output. This end-to-end deep FTC method is applied to a mechatronic system composed of a spherical inverted pendulum, whose configuration is changed via reaction wheels, in turn actuated by electric motors. The simulation and experimental results show that the proposed method can handle abrupt faults occurring in link position/velocity sensors. The provided supplementary material includes a video of real-world experiments and the software source code.