Scalable Software Research Articles

Requirements Engineering for Software Systems: A Systematic Literature Review

This article contains a systematic literature review of requirements engineering for software systems. The literature published within last 5 years was included into the review. A research question was defined as requirements development process of large scale software system (with thousands of requirements) and an interaction problem during this process (communication, coordination and control). The problem is caused by the fact that large-scale software system requirements process is a cross-disciplinary task and it involves multiple parties — stakeholders, domain experts, and suppliers with own goals and constrains, and thus, the interaction between them seriously slows down the overall requirements development process than writing the requirements specification itself. The research papers were classified by several research directions: Natural Language Processing for Requirements Engineering (NLP4RE), Requirement Prioritization, Requirements Traceability, Quality of Software Requirements, Non-functional Requirements and Requirements Elicitation. Motivation and intensity of each direction was described. Each direction was structured and represented with the key references. A contribution of each research direction into the research question was analyzed and summarized including potential further steps. It was identified that some researchers had met a part of the described problem in different forms during their researches. At the end, other researches were described additionally in a short overview. To approach the research question further potential direction was described.

A scalable software solution for anonymizing high-dimensional biomedical data.

BackgroundData anonymization is an important building block for ensuring privacy and fosters the reuse of data. However, transforming the data in a way that preserves the privacy of subjects while maintaining a high degree of data quality is challenging and particularly difficult when processing complex datasets that contain a high number of attributes. In this article we present how we extended the open source software ARX to improve its support for high-dimensional, biomedical datasets.FindingsFor improving ARX's capability to find optimal transformations when processing high-dimensional data, we implement 2 novel search algorithms. The first is a greedy top-down approach and is oriented on a formally implemented bottom-up search. The second is based on a genetic algorithm. We evaluated the algorithms with different datasets, transformation methods, and privacy models. The novel algorithms mostly outperformed the previously implemented bottom-up search. In addition, we extended the GUI to provide a high degree of usability and performance when working with high-dimensional datasets.ConclusionWith our additions we have significantly enhanced ARX's ability to handle high-dimensional data in terms of processing performance as well as usability and thus can further facilitate data sharing.

Optical modulation using digital image symbol patterns and grating light valve technology in a hybrid multiplexed superchannel

AbstractThe latest research exploits to increase optical channel bandwidth are termed polarization division multiplexing (PDM) and few mode fiber (FMF), which rely upon a complex multiplexed architecture together with customized electro‐optic modulated hardware elements, inhibited by cost implications and technical limitations thereof. The research pursued herein presents an alternate digital image pattern modulated (DIPM) approach to achieve higher bandwidth gains, spectral efficiency (SE), and transmission reach improvement. The solution focuses on reducing implementation cost with a scalable software approach and simplified conceptual hardware design using grating light valve (GLV) technology to address the drawbacks of PDM, FMF, and other conventional optical transmission technologies. Simulation test results for a DIPM‐GLV‐based fiber optic (FO) transmission system and comparisons against conventional and other novel techniques are graphically shown and interpreted, suggesting that DIPM using GLV is a viable high performance and cost efficient solution.

Site reliability engineering for IOS mobile application in small-medium scale industries

Software reliability is one and only the standard used to calculate the excellence of a software system. In this situation, we suggest a method to forecast software reliability in the primary creation of any process growth phases since from the designing stage only. Software mobile application has remained a significant tool in our lifetime, and several small-medium scale software progress companies capitalize the substantial resources to mark accessible prospects. The landscape of mobile atmospheres varies as of server environments owing to various issues, by means of the net, liveliness, battery-operated, and compatibility. Estimating and forecasting mobile application reliability remain the actual contests as of the variety of the mobile situations in which the applications are used, and the absence of widely available fault data. In addition, error information is optionally given in to the end-users. Hence, in what way to estimate reliability of mobile software application has remained more and more significant. In this paper, site reliability engineering (SRE) for mobile application is proposed for the reliability of software and is taken as a case study. Their failure is copied back to the exact features of mobile application is useful and powerful.

Parallel Anisotropic Unstructured Grid Adaptation

Computational fluid dynamics (CFD) has become critical to the design and analysis of aerospace vehicles. Parallel grid adaptation that resolves multiple scales with anisotropy is identified as one of the challenges in the CFD Vision 2030 Study to increase the capacity and capability of CFD simulation. The study also cautions that computer architectures are undergoing a radical change, and dramatic increases in algorithm concurrency will be required to exploit full performance. This paper reviews four different methods to parallel anisotropic grid adaptation. They cover both ends of the spectrum: 1) using existing state-of-the-art software optimized for a single core and modifying it for parallel platforms, and 2) designing and implementing scalable software with incomplete but rapidly maturing functionality. A brief overview for each grid adaptation system is presented in the context of a telescopic approach for multilevel concurrency. These methods employ different approaches to enable parallel execution, which provides a unique opportunity to illustrate the relative behavior of each approach. Qualitative and quantitative metric evaluations are used to draw lessons for future developments in this critical area for parallel CFD simulation.

An Open-Source Many-Scenario Approach for Power System Dynamic Simulation on HPC Clusters

In this paper we introduce an approach to accelerate many-scenario (i.e., hundreds to thousands) power system simulations which is based on a highly scalable and flexible open-source software environment. In this approach, the parallel execution of simulations follows the single program, multiple data (SPMD) paradigm, where the dynamic simulation program is executed in parallel and takes different inputs to generate different scenarios. The power system is modeled using an existing Modelica library and compiled to a simulation executable using the OpenModelica Compiler. Furthermore, the parallel simulation is performed with the aid of a message-passing interface (MPI) and the approach includes dynamic workload balancing. Finally, benchmarks with the simulation environment are performed on high-performance computing (HPC) clusters with four test cases. The results show high scalability and a considerable parallel speedup of the proposed approach in the simulation of all scenarios.

Аналіз впливу вигоряння ядерного палива реактора ВВЕР-1000 на швидкість утворення 16N в теплоносії першого контуру

The developed model of the WWER-1000 reactor using MCNP6.2 (Monte Carlo N-Particle Transport Code) includes the detailed core taking into account the design of the fuel assemblies, as well as the baffle, the lower plenum, the fuel support columns, the core barrel, a downcomer, and the reactor pressure vessel. It allows implementing multifunctional calculations such as recriticality with various fuel configurations, the critical concentration of boric acid, determination of the axial and radial peaking factor in the reactor core, etc. For obtaining the more precise result of the cumulation nitrogen-16 formation rate, the contribution from different water volumes was taken into account: in the core, above the fuel and the top nozzle, in the top nozzle of the fuel assembly, in the bottom nozzle, between the fuel and the bottom nozzle, in the axial channels of the baffle, in the reflector. In order to obtain the realistic boundary conditions, the change of the isotopic composition in the fuel assemblies during one fuel cycle was calculated using the ORIGEN-ARP of SCALE software. Therefore, the influence of the nuclear fuel depletion of fuel assemblies in the WWER-1000 reactor on the change of the basic neutron-physical characteristics was determined such as the distribution of the neutron flux density with the energies necessary to initiate the 16O(n,p)16N reaction, the average number of neutrons per fission, the neutron spectrum and average fission energy. As a result, the dependence of the nitrogen-16 formation rate in the primary coolant system on the nuclear fuel burnup is obtained.

Robust, flexible, and scalable tests for Hardy-Weinberg equilibrium across diverse ancestries.

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

A Study on the Direction of Modeling Techniques for the Development of Large Scale Software

A Study on the Direction of Modeling Techniques for the Development of Large Scale Software

A cost-effective adaptive random testing algorithm for object-oriented software testing

The rapid development of object oriented programming (OOP) technology has made it one of the mainstream programming technologies that has been widely used in the design and development of object oriented software (OOS). The inheritance, encapsulation and polymorphism properties of object-oriented language can improve the reusability, scalability and interoperability of software while increasing the difficulty of testing OOS. Researchers have proposed a variety of testing methods to test OOS among which random testing (RT) has been widely used due to its simplicity and ease of use. An OMISS-ARTsum algorithm is proposed in this paper that uses improved OMISS random test FSCS-ART with max-sum standard, which is an implementation version of fixed-sized-candidate-set ART. The OMISS-ARTsum algorithm calculates the total distance between a candidate test case and the executed test case set before the next test case is selected from the set of candidate test cases. Unlike the traditional max-sum based FSCS-ART algorithm, OMISS-ARTsum does not calculate the distance between each executed test case and the candidate case and then sum up the total distance, but uses the method of summing up all the executed test cases and the candidate cases. The information of executing test cases is saved as a whole and the distance between the executed test case set and candidate cases is calculated at the same time. Experiment shows that compared to the OMISS-ART algorithm, the proposed OMISS-ARTsum algorithm can reduce the time overhead.

Survey on Software Changes: Reasons and Remedies

Software systems play a key role in most businesses nowadays. Building robust, reliable and scalable software systems require going through a software production cycle (or process). However, it has been noticed that software systems are subjected to changes, whether those amendments are important or not. Those changes to software systems are viewed as a considerable issue in software engineering; they are considered as a burden and cost a lot, especially in cases such as enterprises and large-scale software systems. This study aims to identify the reasons that cause software changes and suggest remedies for those reasons. We survey the opinions of experts such as technical managers, team leaders, and senior developers. We collected 81 responses to our questionnaire, which aimed to establish common software development practices in the local industry. We also conducted 16 semi-structured interviews targeting the most senior experts, in which we directly discussed the reasons and remedies for software changes. Our results highlight the most influential reasons that cause changes to software systems, such as changes to user requirements, requests for new features, software development methodology, solving bugs, refactoring, and weak user experience design. Most importantly, the study solicited solutions that can reduce the need for software changes

An FPGA Scalable Software-Defined Radio Platform for UAS Communications Research

In the framework of modern Unmanned Aerial System (UAS) ground-board communications, a data-link system should provide with the following features [1]: multiband and adaptive modulations for responding to channel conditions changes and multi-standard interoperability, interferences resilience with a secure physical layer, incorporation of an air-to-air link complementary to the classical air-to-ground links. Varying the available communication functions to provide the above features without the need to substitute on-board components is a desired target. For this purpose, a Field Programmable Gate Aray (FPGA) scalable Software Defined Radio hardware Platform (SDRP) and its control and baseband signal processing architecture have been developed. The platform is composed by means of three boards which provide respectively the power supply, an FPGA based processing core and the radio frequency front-end. The control and baseband signal processing architecture, implemented on the FPGA, is designed with an application-independent section, working as a base reference design, and a reconfigurable section that implements communication functions and algorithms. The overall platform, at the board and FPGA architecture level, has been designed considering scalability and modularity as key features. Thanks to this platform a data-link which responds to the above target can be easily implemented. As a case study a reconfigurable data-link between a UAS and a Ground Control Station (GCS), designed to establish reliable communication in all the phases of a flight (parking, taxiing, taking off, cruising and landing), is presented.

Teaching performance testing

Performance testing plays a vital role in the verification of large scale software systems. It is used for testing the speed, responsiveness, capacity and stability of the investigated system. However, despite the significance of this topic, the effort invested in teaching performance testing in Computer Science is insufficient. The current paper shows, how the fundamentals of performance testing can be demonstrated to students both from a theoretical and a practical viewpoint through step-by-step practical examples that are used in the industry. It is also discussed how a basic toolchain can be set up for performance tests using only free tools. With the presented examples, the reader will be able to take first steps in the performance testing area.
 Subject Classification: 68M15

OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data.

Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

NGPINT: a next-generation protein-protein interaction software.

Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate complementary DNA (cDNA) libraries represent an alternative approach that optimizes scale, cost and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can process interaction data from any biosystem with an available genome or transcriptome reference, thus facilitating the discovery of protein-protein interactions in model and non-model organisms.

Deep mining of open source software bug repositories

Large scale software projects adopt bug tracking systems such as Bugzilla and Jira to manage the bugs’ fixes and store their information. Mining bug repositories is essential to automate some maintenance phase activities like bug triage. Recently, embedding and deep learning models have emerged as a breakthrough for text classification applications. This paper proposes utilizing word embedding and deep learning models for mining bug repositories, in order to identify severity classes of newly reported bugs. Embedding models were utilized to represent bug reports, due to their ability to acquire semantic relations among words and sentences. Five effective deep learning models (CNN, LSTM, GRU, hybrid CNN-LSTM/GRU) were trained to classify the bug reports to their severity classes. We attempted these models as each of them has different capabilities; CNN can extract key features of an input and reduce the feature space size. While, LSTM and GRU can represent sequential words (sentences) effectively. Experimental results on two open large-scale bug repositories, Eclipse and Mozilla, demonstrated that CNN model is the superior. However, it was found that the micro-average classification performance of the CNN could be achieved by some traditional classifiers, e.g. SVM and KNN, trained using the embedding vectors instead of the bag-of-words-model.

A Scalable Computational Approach for Simulating Complexes of Multiple Chromosomes

Significant efforts have been recently made to obtain the three-dimensional (3D) structure of the genome with the goal of understanding how structures may affect gene regulation and expression. Chromosome conformational capture techniques such as Hi-C, have been key in uncovering the quantitative information needed to determine chromatin organization. Complementing these experimental tools, co-polymers theoretical methods are necessary to determine the ensemble of three-dimensional structures associated to the experimental data provided by Hi-C maps. Going beyond just structural information, these theoretical advances also start to provide an understanding of the underlying mechanisms governing genome assembly and function. Recent theoretical work, however, has been focused on single chromosome structures, missing the fact that, in the full nucleus, interactions between chromosomes play a central role in their organization. To overcome this limitation, MiChroM (Minimal Chromatin Model) has been modified to become capable of performing these multi-chromosome simulations. It has been upgraded into a fast and scalable software version, which is able to perform chromosome simulations using GPUs via OpenMM Python API, called Open-MiChroM. To validate the efficiency of this new version, analyses for GM12878 individual autosomes were performed and compared to earlier studies. This validation was followed by multi-chain simulations including the four largest human chromosomes (C1-C4). These simulations demonstrated the full power of this new approach. Comparison to Hi-C data shows that these multiple chromosome interactions are essential for a more accurate agreement with experimental results. Without any changes to the original MiChroM potential, it is now possible to predict experimentally observed inter-chromosome contacts. This scalability of Open-MiChroM allow for more audacious investigations, looking at interactions of multiple chains as well as moving towards higher resolution chromosomes models.

Big Data Systems

Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.

Denoising COVID-19 computed tomography scans with scalable open source software

Denoising COVID-19 computed tomography scans with scalable open source software