Major Software Research Articles

Navigating the Software Engineering Landscape: A Career Mentorship Program for Academic-to-Industry Transition

In the swiftly evolving field of software engineering, the transition from academia to industry presents unique challenges for students. At Thompson Rivers University (TRU), the mandatory co-operative education (co-op) program highlights the significance of this shift. While returning co-op students often praise the experience, the pressure to secure a position during the third year adds significant stress. Many students face challenges due to limited exposure to major software engineering courses in their initial years, coupled with inadequate preparation for the co-op application process. To address these challenges, we implemented a career mentorship program to equip third-year software engineering students with the skills, knowledge, and confidence necessary for successful co-op placements and future professional endeavors. The mentorship series, facilitated by industry professionals, covered diverse topics including career exploration, networking, technical interview preparation, and professional engineering licensure. Pre- and post-program surveys revealed significant improvements in students' confidence, motivation, and preparedness for co-op opportunities.

MolAR: Memory-Safe Library for Analysis of MD Simulations Written in Rust.

Transition to the memory safe natively compiled programming languages is a major software development trend in recent years, which eliminates memory-related security exploits, enables a fearless concurrency and parallelization, and drastically improves ergonomics and speed of software development. Modern memory-safe programing languages, such as Rust, are currently not used for developing molecular modeling and simulation software despite such obvious benefits as faster development cycle, better performance and smaller amount of bugs. This work introduces MolAR-the first memory-safe library for analysis of MD simulations written in Rust. MolAR is intended to explore the advantages and challenges of implementing molecular analysis software in the memory-safe natively compiled language and to develop specific memory-safe abstractions for this kind of software. MolAR demonstrates an excellent performance in benchmarks outperforming popular molecular analysis libraries and tools, which makes it attractive for implementing computationally intensive analysis tasks. MolAR is freely available under Artistic License 2.0 at https://github.com/yesint/molar.

Simulation Design and Research of Flame Generation in Virtual Fire Scene Based on Unity3D

Since the 21st century, with the continuous development and progress of science and technology, the possibility of fire danger is also rising, the requirements of public fire safety management have also been improved accordingly, and the problems in its fire safety management work make it difficult to meet the management requirements. The selection of firefighters has also become the top priority of management, good psychological quality of firefighters can keep calm in the face of fire explosions and various emergencies, and can make the most correct choice to complete the rescue and fire fighting work. In this paper, a building in a certain location is selected as the location to simulate the scene of fire. Through human-computer interaction to judge the psychological quality of firefighters. First use major modeling software such as 3ds Max, Adobe Fusecc, or find material in the Unity3D store to create models of fire scenes and people and import them into Unity3D. Using Unity3D's own particle system component to build flame models, adding collision body components to all models to detect their collision detection mechanism and the mechanism of flame spreading combustion. A Scenario-based Communication Ability Evaluation System Based on Traditional Cognitive Behavior Measurement and Advanced Eye Movement, EEG and NIR Acquisition Techniques.

Quality Assurance of Point and 2D Shear Wave Elastography through the Establishment of Baseline Data Using Phantoms.

Ultrasound elastography has been available on most modern systems; however, the implementation of quality processes tends to be ad hoc. It is essential for a medical physicist to benchmark elastography measurements on each system and track them over time, especially after major software upgrades or repairs. This study aims to establish baseline data using phantoms and monitor them for quality assurance in elastography. In this paper, we utilized two phantoms: a set of cylinders, each with a composite material with varying Young's moduli, and an anthropomorphic abdominal phantom containing a liver modeled to represent early-stage fibrosis. These phantoms were imaged using three ultrasound manufacturers' elastography functions with either point or 2D elastography. The abdominal phantom was also imaged using magnetic resonance elastography (MRE) as it is recognized as the non-invasive gold standard for staging liver fibrosis. The scaling factor was determined based on the data acquired using MR and US elastography from the same vendor. The ultrasound elastography measurements showed inconsistency between different manufacturers, but within the same manufacturer, the measurements showed high repeatability. In conclusion, we have established baseline data for quality assurance procedures and specified the criteria for the acceptable range in liver fibrosis phantoms during routine testing.

How project knowledge management develops volatile organizational memory

PurposeThis paper discusses the effects of enabling conditions of project knowledge management in building volatile organizational memory. The theoretical rationale underlies a recursive relationship among enabling conditions of project knowledge management, organizational learning and memory.Design/methodology/approachThis research employs a qualitative descriptive single case study approach to examine a mobile application development project undertaken by a major software company in Brazil. The analysis focuses on the project execution using an abductive analytical framework. The study data were collected through in-depth interviews and company documents.FindingsBased on the research findings, the factors that facilitate behavior and strategy in managing project knowledge pose a challenge when it comes to fostering organizational learning. While both these factors play a role in organizational learning, the exchange of information from previous experience could be strengthened, and the feedback from the learning process could be improved. These shortcomings arise from emotional tensions that stem from power struggles within knowledge hierarchies.Practical implicationsBased on the research, it is recommended that project-structured organizations should prioritize an individual’s professional experience to promote organizational learning. Organizations with well-defined connections between their projects and strategies can better establish interconnections among knowledge creation, sharing and coding.Originality/valueThe primary contribution is to provide a comprehensive view that incorporates the conditions required to manage project knowledge, organizational learning and memory. The findings lead to four propositions that relate to volatile memory, intuitive knowledge, learning and knowledge encoding.

The Entrainment of Task Allocation and Release Cycles in Open Source Software Development

In this study we identify a process of “entrainment” around open source software (OSS) development release cycles to capture patterns of self-organized task allocation among developers. We conducted an abductive, computationally intensive study of eight OSS projects, using relational event modeling to analyze 1,169,489 actions covering 93 major software releases. The process of entrainment that we identify involves three task allocation mechanisms: (1) developer-issue inertia, (2) developer contribution frequency, and (3) issue popularity. Our analysis demonstrates that these mechanisms and the phases of the release cycle entrain each other. Before a major release, developers engage in a concentrated mobilization phase, whereby they democratize development activity and increasingly allocate community contributions to the set of issues related to the release. After a major release, the extended cleanup phase garners a greater share of development work from recently highly active developers and dilutes the activity of these developers across a wider range of issues. Our theorizing suggests that major releases constitute important events around which OSS communities self-organize and we characterize how this occurs. Our research contributes to theorizing on organizing in OSS communities by explaining how self-organizing task allocation interacts with release cycles through the mechanism of entrainment. We also contribute to the literature on entrainment by showing how it may unfold in the context of online peer production communities such as OSS.

Recursive autoencoder network for prediction of CAD model parameters from STEP files

Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many potential applications in improving efficiency within design processes. This paper presents a novel asymmetric autoencoder model, consisting of a recursive encoder network and fully-connected decoder network, for the reproduction of CAD models through prediction of the parameters necessary to generate a 3D part design. Inputs to the autoencoder are STEP (standard for the exchange of product data) files, an ISO standard CAD model format, compatible with all major CAD software. A complete 3D model can be accurately reproduced using a STEP file, meaning that all geometric information can be used to contribute to the final encoded vector, with no loss of small detail.In a CAD model of overall size 10 × 10 × 10 units, for 90% of models, the class of an added feature is estimated with maximum error of 0.6 units, feature size with maximum error of 0.4 units and coordinate values representing position with maximum error of 0.3 units. These results demonstrate the successful encoding of complex geometric information, beyond merely the shape of the 3D object, with potential application in the design of search engine functionality.

Design of Face Detection and Recognition System to Enhance Security of Safe Locker

In recent years, safe lockers have been spread in public places to secure valuable belongings. The people are concerned about losing safe locker keys or use of the spare key by others and will remain worried about their things. To solve the forenamed matter, this paper proposed a system that depends on biometrics to secure valuable things and can minimize their concerns and worries. The proposed system consists of two major parts, software, and hardware. In the hardware part, A microcontroller with a camera and an electronic lock will be used to securely open or close the door of the safe locker. In the software part, the images that are pictured by the camera will be prepared by an image processing algorithm and then Support Vector Machines (SVM) will be trained in the images of a person. The images and information of the person will be saved until the belongings come out and after that deletes everything to prepare for another one. Index Terms— Safe Locker, Face Detection, Face Recognition, Machine Learning, Cyber Security.

Fast and Robust Optimization of Full Trajectory from Entry Through Powered Descent

A high-mass Mars entry, descent, and landing (EDL) mission for cargo delivery or human exploration faces the challenge of a high propellant mass fraction requirement for powered descent. This work develops a novel method and the associated algorithm that utilize existing entry and propellant-optimal powered descent guidance algorithms for fast and robust optimization of the end-to-end EDL trajectory for achieving the overall optimized propellant efficiency. A bilevel optimization formulation aided by a predictive logic based on the optimal powered descent algorithm to determine the near-optimal transition from entry to powered descent allows the end-to-end trajectory to be optimized in a relatively simple manner. No new major software or algorithms are required other than the existing guidance algorithms. A solution to the bilevel optimization problem is shown to exist, and the convergence of the bilevel optimization algorithm is guaranteed under certain mild assumptions. The algorithm developed in this paper is able to find consistently an end-to-end near-optimal EDL trajectory in just over 10 s on a desktop computer, while general-purpose modern trajectory optimization software can take thousands of seconds. The effectiveness and robustness of the algorithm are demonstrated by successfully optimizing thousands of complete EDL trajectories efficiently and reliably from dispersed initial entry conditions.

IoT forensics: Exploiting log records from the DAHUA technology CCTV systems.

CCTV surveillance systems are ubiquitous IoT appliances. Their forensic examination has proven critical for investigating crimes. DAHUA Technology is a well-known manufacturer of such products. Despite its global market share, research regarding digital forensics of DAHUA Technology CCTV systems is scarce and currently limited to extracting their video footage, overlooking the potential presence of valuable artifacts within their log records. These pieces of evidence remain unexploited by major commercial forensic software, yet they can hide vital information for an investigation. For instance, these log records document user actions, such as formatting the CCTV system's hard drive or disabling camera recording. This information can assist in attributing nefarious actions to specific users and hence can be invaluable for understanding the sequence of events related to incidents. Therefore, in this paper, several DAHUA Technology CCTV systems are thoroughly analyzed for these unexplored pieces of evidence, and their forensic value is presented.

Порівняльний аналіз систем програмування мікроконтролерів серії STM32 у виробах авіаційного застосування

The subject of the research is the software development environment for STM32 series microcontrollers, which play an important role in the electronic support of modern aviation and are an integral part of the development process of aircraft systems. Goal. The selection of the most current STM32 series microcontroller programming system for aviation products. Tasks. Overview of major popular software development environments for STM32 microcontrollers such as STM32CubeIDE, VisualGDB, IAR Embedded Workbench for ARM, Keil MDK, Arm Development Studio, and Simulink. Various aspects of programming systems are considered, including software development, feature availability, and integration with development tools and environments. Research methods. Carrying out a comparative analysis; separation of the characteristic features of the use of programming environments in avionic devices using the example of JSC "Element". In view of the use of environments in the field of avionics, special attention was paid to certification and reliability using examples of safety standards, verification, and testing tools. The results. It was found that code debugging, memory analysis, code optimization, and other basic functions are inherent to all considered development environments. Distinguishing features inherent in individual programming environments are identified, including microcontroller robot simulation, high performance and compilation speed, open source code, optimization, support from the manufacturer, and a wealth of documentation, including user manuals, code examples, support forums, training courses, webinars, and articles from development companies. Conclusions (scientific and practical novelty). The results of the analysis provide important conclusions regarding the better choice of STM32 microcontroller programming systems in aviation products. The comparisons and analyses made will help software developers choose the most suitable programming system for their specific needs. A conclusion was made about the feasibility of using development environments such as IAR Embedded Workbench for ARM and STM32CubelIDE for developing aviation software based on STM32.

IoT forensics: Exploiting unexplored log records from the HIKVISION file system.

CCTV surveillance systems are IoT products that can be found almost everywhere. Their digital forensic analysis often plays a key role in solving crimes. However, it is common for these devices to use proprietary file systems, which frequently hinders a complete examination. HIKVISION is a well-known manufacturer of such devices that typically ships its products with its proprietary file system. The HIKVISION file system has been analyzed before but that research has focused on the recovery of video footage. In this paper, the HIKVISION file system is being revisited regarding the log records it stores. More specifically, these log records are thoroughly examined to uncover both their structure and meaning. These unexplored pieces of evidence remain unexploited by major commercial forensic software, yet they can contain critical information for an investigation. To further assist digital forensic examiners with their analysis, a Python utility, namely the Hikvision Log Analyzer, was developed as part of this study that can automate part of the process.

Revisit security in the era of DevOps: An evidence‐based inquiry into DevSecOps industry

AbstractBy adopting agile and lean practices, DevOps aims to achieve rapid value delivery by speeding up development and deployment cycles, which however lead to more security concerns that cannot be fully addressed by an isolated security role only in the final stage of development.DevSecOpspromotes security as a shared responsibility integrated into the DevOps process that seamlessly intertwines development, operations, and security from the start throughout to the end of cycles. While some companies have already begun to embrace this new strategy, both industry and academia are still seeking a common understanding of the DevSecOps movement. The goal of this study is to report the state‐of‐the‐practice of DevSecOps, including the impact of DevOps on security, practitioners' understanding of DevSecOps, and the practices associated with DevSecOps as well as the challenges of implementing DevSecOps. The authors used a mixed‐methods approach for this research. The authors carried out a grey literature review on DevSecOps, and surveyed the practitioners of DevSecOps in industry of China. The status quo of DevSecOps in industry is summarized. Three major software security risks are identified with DevOps, where the establishment of DevOps pipeline provides opportunities for security‐related activities. The authors classify the interpretations of DevSecOps into three core aspects of DevSecOps capabilities, cultural enablers, and technological enablers. To materialise the interpretations into daily software production activities, the recommended DevSecOps practices from three perspectives—people, process, and technology. Although a preliminary consensus is that DevSecOps is regarded as an extension of DevOps, there is a debate on whether DevSecOps is a superfluous term. While DevSecOps is attracting an increasing attention by industry, it is still in its infancy and more effort needs to be invested to promote it in both research and industry communities.

Characterization of continuous experimentation in software engineering: Expressions, models, and strategies

Continuous Experimentation (CE) has become increasingly popular across industry and academic communities. Major software organizations use CE to increase their revenue by adding value to end-users, and researchers are investigating the CE adoption process and usage to expand its success. Given this rapid evolution, observing a shared understanding of CE definitions, processes, and experiment strategies is difficult, potentially jeopardizing new implementations and focused research efforts. To characterize CE from the perspective of its definitions, processes, and strategies for experimentation available in the technical literature and to evolve the understanding perspectives for “continuous experimentation” and “data-driven development” definitions. To select and analyze sources of information in the technical literature dealing with different aspects of continuous experimentation through a Literature Study using an ad hoc search improved with snowballing (backward and forward). Organize the findings into new perspectives for CE definitions, processes, and experiment strategies. It was possible to identify many different definitions, processes, and experimental strategies used to describe CE in the 72 analyzed empirical papers, making it difficult to decide on their combination to be applied in a real software development project. Therefore, it has been proposed to evolve the CE understanding perspective, to categorize its experiment strategies, and to offer a combined development process for CE combining parts of other processes. Besides, conjectural requirements have been identified, which can contribute to better differentiating requirements and hypotheses in the CE context. Likely, a better understanding of CE is still missing. It can contribute towards organizing a common taxonomy to facilitate the possible choices for the experiment strategies. Therefore, there is space for more investigations on its applicability and value in different categories of software systems, despite all the advancements of CE and its promotion in developing modern software systems.

Exploring the Impact of Experiments on Knowledge Workers' Productivity in the Pakistani Software Industry

The world has been changed into global apartment by the innovation of ICT. The concepts of time and distance have been dashed down with the magical clicks of technology. All these supersonic changes are caused by the highest level of competency and latest knowledge of the workforce. The structure of the economy is dynamic and it continues to change with the changes of tide and space; where once organizations were dependent on the productivity of a manual workforce, they are now increasingly depending on the productivity of knowledge workers. Today, knowledge workers account for more than two-thirds of the workforce, and thus should be the centre of focus of strategic plans to improve productivity. The aim of this experimental research study was to explore the impact of suggested factors on knowledge worker productivity. The researcher used the survey method for collection of data with instrument of questionnaire to achieve the objectives of the study. The sample size was consisted of 100 respondents, which were selected randomly from major software firms of Pakistan. Results of descriptive statistics and structural equations modelling (SEM) revealed that independent variables, e.g. on-job-training, workplace, motivation, education and work experience and well-being have a positive effect on knowledge worker productivity. In order to increase the knowledge worker productivity, management requires to betterly understand how to make knowledge workers more productive by examining the factors that affect productivity and by analysing any relationship between the factors and roles.

The impact of online tax community advice on individual taxpayer decision making

In this study, we examine the impact of advice shared on an online tax community and taxpayer decision making. Online tax communities are linked to major tax preparation software and provide a way for taxpayers to ask unique questions and receive responses. While online communities are intended to facilitate the transmission of unbiased advice between individual taxpayers, the quality, content, and source of responses can greatly vary. Drawing on the predictions of expectancy violations theory (EVT), we investigate two facets of provided advice: response provider expertise and response language concreteness. Our results indicate that taxpayers report more conservatively (more aggressively) when presented with advice from a deemed tax expert if concrete language (abstract language) is used. Further, we find that taxpayers' perceived usefulness of the response mediates this relationship. Collectively, we contribute to EVT and provide evidence on the recognition and use of online tax community responses.

Learning environments in software testing education: An overview

AbstractNowadays, we heavily depend on software, as we utilize it daily. Bugs in the software can cause significant damage, loss of private data and money, and even loss of human lives. Software testing and quality assurance discipline, which belongs to the software engineering domain, aims to ensure that the software is good enough before its release to the market. Therefore, it is vital to ensure that software engineers are highly trained in this domain. Unfortunately, this discipline is frequently neglected throughout software engineering courses. Students are mostly taught just the theoretical principles of software testing and gain little practical experience. Additionally, the software testing lectures are considered to be dull, especially if they are not followed by practical tasks where the students could apply learned techniques and gain hands‐on experience. Several tools, games, and collaborative learning environments have been proposed to make the lectures more interesting and increase student engagement. The main goal of this article is to provide a systematic survey of such environments, where each environment was evaluated in terms of major software testing topics covered and the approach used to engage the students.

Analytical integration of the heater and sensor 3ω signals of anisotropic bulk materials and thin films

We derive and analytically integrate the models for the heater and sensor 3ω signals of the temperature field of anisotropic bulk materials and thin films. This integration is done by using the Fourier transform and expressing the frequency dependence of temperature in terms of the modified Bessel and Struve functions, which are well-implemented in major computation software. The effects of the radiative losses and interface thermal resistance are also evaluated for different frequency regimes. Further, by fitting the 3ω model integrated over the heater and sensor widths to experimental data recorded up to 31 kHz, the thermal conductivity and thermal diffusivity of a quartz glass wafer are determined for temperatures ranging from 300 to 800 K. The obtained results show that the usual log-linear approximation can induce an uncertainty of about 5% on the thermal conductivity values. The exact integrated models are thus expected to facilitate the accurate determination of the thermal conductivity and thermal diffusivity of anisotropic materials through a wide spectrum of modulation frequencies and without time-consuming numerical integration.

The influence of project initiators’ person-to-person followership on project popularity in open source communities: The role of reach and importance

Person-to-person (P2P) followership is an important aspect of major open source software (OSS) development platforms. In this age of social media platforms, P2P followership has significantly shaped the way users engage in OSS development by facilitating the establishment of connections among OSS users. Despite the prevalence of P2P followership, less is known about the impact of OSS project initiators’ P2P followership nodes on their project popularity. This is a particularly important gap considering the low rate of OSS project success. We posit that OSS project initiators derive information and influence benefits from the quantity and connectivity of their P2P followership nodes, explaining the popularity of the projects initiated. We determine the connectivity of OSS project initiators’ P2P followership nodes based on the nodes’ reach and importance. To test the hypotheses, we use a large panel dataset collected over 24 months from GitHub. The findings indicate that the quantity of OSS project initiators’ P2P followership nodes including followers and followees has a positive effect on their project popularity. Moreover, such an effect is mostly dependent on the connectivity of the OSS project initiators’ P2P followership nodes in such a way that highly connected P2P followership nodes do not impact influence benefits but they increase information benefits. We discuss the theoretical and practical implications of our findings.

Data-Resource Profile: United Kingdom Optimum Patient Care Research Database.

Electronic medical records (EMRs) maintained in primary care in the UK and collected and stored in EMR databases offer a world-leading resource for observational clinical research. We aimed to profile one such database: the Optimum Patient Care Research Database (OPCRD). The OPCRD, incepted in 2010, is a growing primary care EMR database collecting data from 992 general practices within the UK. It covers over 16.6 million patients across all four countries within the UK, and is broadly representative of the UK population in terms of age, sex, ethnicity and socio-economic status. Patients have a mean duration of 11.7 years' follow-up (SD 17.50), with a majority having key summary data from birth to last data entry. Data for the OPCRD are collected incrementally monthly and extracted from all of the major clinical software systems used within the UK and across all four coding systems (Read version 2, Read CTV3, SNOMED DM+D and SNOMED CT codes). Via quality-improvement programmes provided to GP surgeries, the OPCRD also includes patient-reported outcomes from a range of disease-specific validated questionnaires, with over 66,000 patient responses on asthma, COPD, and COVID-19. Further, bespoke data collection is possible by working with GPs to collect new research via patient-reported questionnaires. The OPCRD has contributed to over 96 peer-reviewed research publications since its inception encompassing a broad range of medical conditions, including COVID-19. The OPCRD represents a unique resource with great potential to support epidemiological research, from retrospective observational studies through to embedded cluster-randomised trials. Advantages of the OPCRD over other EMR databases are its large size, UK-wide geographical coverage, the availability of up-to-date patient data from all major GP software systems, and the unique collection of patient-reported information on respiratory health.