Future Software Research Articles

Evaluation of Automatic Analysis Software for 1H NMR Spectra Quantification. Impact of Signal Broadening and EDTA Addition.

NMR is a well-established analytical technique that enables the identification and quantification of several compounds in complex mixtures. The implementation of automated analysis software enhances this process by comparing sample spectra with a library of standard reference spectra. One of the key parameters in libraries is the signal line width; however, this may result in a concentration bias for compounds with broad signals, particularly those that are complexing. This study aims to evaluate the quantification of seven compounds commonly present in wine with complexing activity. Four automatic programs and manual deconvolution were used to quantify mixtures of these compounds at known concentrations, both in the presence of cations and with the addition of ethylenediamine-tetraacetate (EDTA) as a chelating agent. Additionally, malate was quantified in a wine sample using the standard addition method, both with and without EDTA. The findings illustrate that three of the programs, which employ the signal width value, underestimate compound concentrations in the presence of cations due to signal broadening. This error was mitigated by the EDTA addition. In contrast, the remaining software, which did not utilize signal line width, obtained similar concentrations in both cases. Future investigations involving the automatic analysis of compounds with complexing activity should take care of sample preparation and/or algorithm selection. Furthermore, future software development should consider allowing flexible adjustments of signal line width in their workflow.

The Pervasive Impact of Software Engineering and Architecture: A Multi-Industry Analysis

This article presents a comprehensive analysis of the ubiquitous role of software engineering and architecture across diverse industries in the modern digital landscape. Through a combination of literature review, industry case studies, and expert interviews, we examine how core software engineering principles—such as scalability, security, user experience, and data management—are applied and adapted in sectors ranging from e-commerce and fintech to healthcare, transportation, media, manufacturing, and government services. Our findings reveal both common challenges and industry-specific innovations in areas such as blockchain technology, telemedicine, autonomous vehicles, and industrial IoT. We also explore emerging trends, including the integration of artificial intelligence, edge computing, and quantum computing, and their potential impact on future software architectures. Furthermore, this article addresses critical ethical considerations and societal implications, including data privacy, job displacement, and the digital divide. By synthesizing these multifaceted perspectives, we provide valuable insights for both industry practitioners and academic researchers, highlighting the transformative power of software engineering in shaping the technological foundations of contemporary society and identifying key areas for future research and development.

From Code to Care: The Impact of Software Engineering and QA on Healthcare Delivery Systems

The rapid digitalization of healthcare has led to an unprecedented reliance on software systems to improve patient care, streamline operations, and ensure regulatory compliance. This article presents a comprehensive analysis of the critical role that software engineering and quality assurance (QA) play in three key areas of modern healthcare: Electronic Health Records (EHRs), telemedicine platforms, and medical device software. Through an examination of current practices, challenges, and case studies, we demonstrate how robust software engineering methodologies and rigorous QA processes contribute to the development of reliable, secure, and efficient healthcare applications. Our findings highlight the importance of data integrity, interoperability, user experience, and regulatory compliance in healthcare software development. Furthermore, we explore emerging trends such as artificial intelligence, blockchain, and the Internet of Medical Things (IoMT), discussing their implications for future software engineering and QA practices in healthcare. This article provides valuable insights for healthcare organizations, software developers, and policymakers, offering recommendations to enhance the quality and safety of software-driven healthcare solutions.

Requirements Engineering at a Turning Point: Key Research Trends Shaping the Future of Systems and Software Integration

Requirements Engineering (RE) is experiencing a significant transformation that requires urgent attention and creativity from industry experts to address the challenges of future systems and software development. At the core of this evolution is Requirements Engineering (RE), an essential process that defines, records, and maintains the fundamental aspects of software and systems design. With the rapid increase in complexity and interconnectedness across different industries, the issues linked to requirements engineering are becoming more pressing, significantly affecting costs, timelines, and overall performance. This article outlines current research trends in requirements engineering, focusing on emerging challenges and future pathways. We investigate advancements in RE techniques, methodologies, and tools, assessing their effects on building complex systems. Furthermore, we emphasize the growing significance of stakeholder engagement and agile practices in shaping requirements, highlighting their contributions to improving collaboration and adaptability. By evaluating the present state of practice and pinpointing critical areas of research concern, this article offers a guide for future requirements engineering studies, enabling researchers and practitioners to address the changing needs of software and systems engineering.

Empowering the biomedical research community: Innovative SAS deployment on the All of Us Researcher Workbench.

The All of Us Research Program is a precision medicine initiative aimed at establishing a vast, diverse biomedical database accessible through a cloud-based data analysis platform, the Researcher Workbench (RW). Our goal was to empower the research community by co-designing the implementation of SAS in the RW alongside researchers to enable broader use of All of Us data. Researchers from various fields and with different SAS experience levels participated in co-designing the SAS implementation through user experience interviews. Feedback and lessons learned from user testing informed the final design of the SAS application. The co-design approach is critical for reducing technical barriers, broadening All of Us data use, and enhancing the user experience for data analysis on the RW. Our co-design approach successfully tailored the implementation of the SAS application to researchers' needs. This approach may inform future software implementations on the RW.

Aerospace Software and AI Sustainability: A Software Developer Capabilities Approach

Artificial intelligence (AI) mediated approaches towards generating or refining digital software are influencing the aerospace industry and its emerging, developing, and innovative operational systems. Such firm and/or industry inclusions can enhance entrepreneurial effectiveness and ongoing sustainability. A nationwide Qualtrics survey delivers semi-structured interview summations of registered software-house developers (‘experts) showing nodal thematic data can NVivo project map that stagewise models towards a suite of sustainability outcomes. Respondent software developers consider software house digital and AI new capacities, plus their knowledge creation frame across two capabilities areas that relationally link into an entrepreneurial capabilities suite and a strategic AI product trending capabilities suite. This system likely coalesces, as a digital AI-supported collective, towards enhancements into digital and AI globally sustainable outcomes. The aerospace industry. and their engaged software houses can use this operational approach when strategically planning future ongoing software and AI development inclusions across their globally located corporate aerospace entities, by considering how best to adjust ongoing digital and AI competencies and entrepreneurial capabilities towards delivering enhanced sustainable outcomes. This study suggests a stagewise and measurement model approach can likely be derived to introduce chosen software and AI levers, but these should focus on delivering ongoing digital and sustainable aerospace industry outcomes.

Challenges of software verification: the past, the present, the future

Software verification aims to prove that a program satisfies some given properties for all its possible executions. Software evolved incredibly fast during the last century, exposing several challenges to this scientific discipline. The goal of the “Challenges of Software Verification Symposium” is to monitor the state-of-the-art in this field. In this article, we will present the evolution of software from its inception in the 1940s to today’s applications, how this exposed new challenges to software verification, and what this discipline achieved. We will then discuss how this chapter covers most of the current open challenges, the possible future software developments, and what challenges this will raise in software verification.

SOFTWARE ARCHITECTURE SYSTEM DESIGN FOR THE MASS SERVICE SYSTEMS MODELING TO BE IMPLEMENTED IN GO PROGRAMMING LANGUAGE

The subject of the article is the methods and approaches to organizing the architecture of software implementation designed for modeling the behavior of mass service systems. The goal of the work is to design a software architecture for implementation in Go language, intended to replicate the behavior of various types of mass service systems, without considering the failure of individual service channels, using parallel computing. The article addresses the following tasks: consider the basis for designing the architecture and conclude its appropriateness; develop requirements for the future software product for more effective resource use and clear definition of successful completion; analyze the approaches to organizing software architecture and make a justified decision on the application of one of them; design a general algorithm scheme taking into account all requirements; identify the components of the modeled system and their interactions; build process diagrams considering the specifics of the Go programming language; define the method and contracts of interaction with the software. The research will utilize the following methods: Go programming language, concurrency, architectural UML diagrams, C4 diagrams, process diagrams. The following results were obtained: the requirements for the software for modeling mass service operations (SMO) were defined; common approaches to organizing architecture were considered and a comparative analysis was conducted; the structure of the future program was developed at the necessary levels of abstraction; for the first time, an architecture of the software product for modeling various mass service systems using parallel computing and the concurrency approach under the implementation in the Go programming language was proposed.

Gamification strategy to promote social and human factors in the training of software engineers: A case study

AbstractSoftware Engineering is a discipline frequently reflected in training strategies. This discipline requires certain levels of abstraction to achieve the competencies and skills necessary for the professional development of future software developers. The software industry increasingly demands that professionals in this discipline have social and human skills to achieve highly productive teams. Therefore, these teams should respond to such demands in a world with increasing dependence on technology and the development of software products. Traditional pedagogical strategies often need help adapting to the new generations of software engineers and responding in a limited way to the demands of teaching processes related to this discipline. This article evaluates a gamification‐based strategy designed for the Software Engineering course at a Latin American higher education institution. This course addressed software project management as a training objective. Such a strategy was designed with a gamification‐based model to influence the productivity of software development teams. The results of using the model show its efficiency and usefulness as a guide for implementing new strategies based on gamification that considers social and human factors (SHFs) to intervene in the productivity of software development teams. The challenges designed in the proposal presented managed to promote SHFs in the participants, according to the analysis of the prepared case study. According to these results, the factors considered relate to skills and experience in managing software development projects, motivation, and communication. The activities executed by the participants in the context of the case study strengthened the human side of the team and allowed its growth to achieve its objectives.

Polyenergetic reconstruction mitigates streak artifacts by dual source imaging in chest photon counting detector computed tomography

ObjectiveThis study aims to assess the efficacy of polyenergetic reconstruction methods in reducing streak artifacts caused by dual source imaging in Photon Counting Detector Computed Tomography (PCD-CT) imaging, thereby improving image quality and diagnostic accuracy. MethodsA retrospective cohort study was conducted, involving 50 patients who underwent chest Computed Tomography Angiography with PCD-CT, focusing on those with streak artifacts. Quantitative and qualitative analyses were performed on images reconstructed using monoenergetic and polyenergetic techniques. Quantitative evaluations measured the attenuation of tracheal air density in regions affected by streak artifacts, while qualitative assessments employed a modified Likert scale to rate image quality. Statistical analyses included Wilcoxon's signed-rank tests and Spearman's correlation, alongside assessments of inter-rater reliability. ResultsThere was significantly lower attenuation of tracheal air density on the polyenergetic reconstructions (Median − 1010 ± 62 HU vs −930 ± 110 HU; P < 0.001), and significantly decreased variation on the polyenergetic reconstructions (Median 65.2 ± 79.5 HU vs 38.8 ± 33.9 HU; P < 0.001). The median modified-Likert scale were significantly better for the polyenergetic reconstructions (median modified-Likert 4 ± 0.5 vs 2.5 ± 1; P < 0.001). The inter-rater agreement was substantial and not significantly different between reconstructions (Gwet's ACPolyenergetic = 0.78 vs Gwet's ACVMI = 0.775). ConclusionPolyenergetic reconstruction significantly mitigates streak artifacts in PCD-CT imaging, enhancing quantitative and qualitative image quality. This advancement addresses a known limitation of current PCD-CT reconstruction techniques, offering a promising approach to improving diagnostic reliability and accuracy in clinical practice. We demonstrate that future software implementations can resolve this artifact.

Impact of Incorporating Community Smells with Object-Oriented Metrics to Predict Change-Prone Classes

Change-proneness is an important quality attribute that can help developers to maintain source code more effectively. Change-prone classes refer to those classes which are more likely to change across releases. Existing approaches have used object-oriented metrics to predict change-prone classes. However, community smells related information, which represents the communication issues among developers’ community, has not yet been considered to predict change-prone classes. This study investigates the impact of including community smells-related information with existing Object Oriented (OO) metrics to predict a class to be change-prone in future software releases. It aims to understand the extent to which the addition of community smell-related information can contribute to the performance of change prediction models. To perform analysis, 317 releases of 14 open-source Java projects were selected from GitHub. The resulting dataset is used to predict change-prone classes with five different popular and widely used machine learning algorithms, namely K-nearest neighbor (KNN), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP). To evaluate the performance of the classifiers, accuracy, and F1-measure are used. The experimental results suggest that including community smell metrics along with the existing OO metrics gives better performance in predicting change-prone classes. Moreover, MLP and LR algorithms, which are both parametric models, are found to predict change-prone classes better when Community Smell(CMS) related features are incorporated, in comparison with other non-parametric models. By including CMS along with OO metrics, 1.51% increase in average accuracy and 3.52% increase in average F1 score for Logistic Regression, and 1.69% increase in average accuracy and 3.26% increase in average F1 score for Multi-layer Perceptron have been found. DUJASE Vol. 8 (1) 32-41, 2023 (January)

Can software-defined vehicles never roll over: A perspective of active structural transformation

The revolution of physical structure is highly significant for future software defined vehicles (SDV). Active structural transformation is a promising feature of the next generation of vehicle physical structure. It can enhance the dynamic performance of vehicles, thus providing safer and more comfortable ride experiences, such as the ability to avoid rollover in critical situations. Based on the active structural transformation technology, this study proposes a novel approach to improve the dynamic performance of a vehicle. The first analytical motion model of a vehicle with active structural transformation capability is established. Then, a multi-objective optimization problem with the adjustable parameters as design variables is abstracted and solved with an innovative scenario specific optimization method. Simulation results under different driving scenarios revealed that the active transformable vehicle applying the proposed method could significantly improve the handling stability without sacrificing the ride comfort, compared with a conventional vehicle with a fixed structure. The proposed method pipeline is defined by the software and supported by the hardware. It fully embodies the characteristics of SDV, and inspires the improvement of multiple types of vehicle performance based on the concept of “being defined by software” and the revolution of the physical structure.

Product managers in software startups: A grounded theory

Context:Defining and designing a software product is not merely a technical endeavor, but also a socio-technical journey. As such, its success is associated with human-related aspects, such as the value users perceive. To handle this issue, the product manager role has become more evident in software-intensive companies. A unique, challenging context for these professionals is constituted by software startups, emerging companies developing novel solutions looking for sustainable and scalable business models. Objective:This study aims to describe the role of product managers in the context of software startups. Method:We performed a Socio-Technical Grounded Theory study using data from blog posts and interviews. Results:The results describe the product manager as a multidisciplinary, general role, not only guiding the product by developing its vision but also as a connector that emerges in a growing company, enabling communication of software development with other areas, mainly business and user experience. The professional performing this role has a background in one of these areas but a broad knowledge and understanding of key concepts of the other areas is needed. We also describe how differences of this role to other lead roles are perceived in practice. Conclusions:Our findings represent several implications for research, such as better understanding of the role transformation in growing software startups, practice, e.g., identifying the points to which a professional migrating to this role should pay attention, and the education of future software developers, by suggesting the inclusion of related topics in the education and training of future software engineers.

APPROVAL OF FORMATION OF PROFESSIONAL COMPETENCE OF FUTURE OPERATORS IN INFORMATION PROCESSING AND SOFTWARE

The article is devoted to the problem of researching the effectiveness of the process of forming the professional competence of future information processing and software operators in the process of professional training. It was found that the system of training future specialists in the process of professional training is aimed at ensuring their readiness for professional activity, taking into account all components of the educational process of the educational institution. It was clarified that the important factors of the problem of the formation of professional competence of future specialists in the process of professional training are inadequate readiness for professional activity and the appropriate level of their professional competence for such activity. It was revealed that the process of formation of professional competence of individuals for professional activity is influenced by the following factors: transformation of the content of training of future operators in information processing and software in professional (vocational and technical) education institutions; the prestige of the profession and its social significance; professional development and development of the personality of future specialists; acquisition of practical experience by students of education regarding the implementation of innovative technologies and project activities; purposeful mastery of professional skill and specialty; the formation of a culture of mastery and the readiness of qualified specialists for professional activity. It is specified that the formation of professional competence of future specialists in the process of professional training is oriented towards: purposeful formation of a complete system of professional competences in the process of professional training; development of motivational potential; enrichment of the development of individual and professional qualities, formation of self-assertion, improvement of personal and professional assets, acquisition of one's own style of work; accumulation of experience in professional skills for effective performance of professional activities, improvement of professional abilities and skills for further self-actualization, self-improvement of professional activities, development of quick adaptation to full-fledged future activities; improvement of assets of creative and scientific research, enabling professional self-development in professional activity. The study of the continuity of the development of future specialists to professional activity requires further development.

Software Bug Prediction Using Machine Learning

Software bug prediction is a crucial aspect of software development and maintenance as it directly impacts the overall success of the software. By identifying potential bugs early on, software quality, reliability, and efficiency can be improved while also helping to reduce costs. Creating a reliable bug prediction model is a complex task, with various techniques proposed in the literature. In this paper, a bug prediction model utilizing machine learning algorithms is introduced. Three supervised machine learning algorithms have been incorporated to forecast future software faults using historical data. The evaluation process revealed that Machine Learning algorithms, specifically Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Networks (ANNs), can be effectively utilized with a high level of accuracy. Additionally, a comparison measure was implemented to assess the proposed prediction model against other methods. The findings indicated that the Machine Learning approach outperformed other techniques in terms of performance. Key Words: Bug Prediction, Reliability, Efficiency, Quality, Machine Learning, Performance Metrics

What’s the safest way home? Mapping safe transport routes in South Africa with (and without) crime data

Safety is arguably the single biggest daily concern for commuters. Popular navigation software systems such as Google Maps, TomTom and Waze do not however currently give the same weight of consideration to crime risk when directing road users to the safest and shortest routes as they do existing traffic knowledge. In this study we propose three simple methods to delineate the safest and shortest route between any two locations in South Africa considering crime risk. The first method uses historical crime data to delineate the safest and shortest route; the second method, uses only the location of so-called ‘risky facilities’ to determine the safest and shortest route; while the third method uses a combination of both historical crime data and facility data to delineate the optimal route. We contrast the resultant routes delineating by these three methods and speculate on the policy implications of integrating this knowledge into future navigation software systems.

Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning

Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning (ECFML) algorithm. The lightweight MobileViT network is used as the meta-learner of the Model Agnostic Meta-Learning (MAML) algorithm. By learning common knowledge on the local data of multiple clients, and then fine-tuning the model, the number of unnecessary iterations is reduced, and communication efficiency is improved while reducing the number of parameters. The gradient information model is encrypted using the differential privacy of the Laplace mechanism, and the optimal privacy budget is determined through experiments. Experiments on three public datasets (AEEEM, NASA, and Relink) verified the effectiveness of ECFML in terms of parameter quantity, convergence, and model performance of cross-project software defect prediction.

Future of software development with generative AI

Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be used for many purposes, from creative dimensions to replacing repetitive and manual tasks. The number of opportunities increases with the capabilities of large-language models (LLMs). This has raised concerns about ethics, education, regulation, intellectual property, and even criminal activities. We analyzed the potential of generative AI and LLM technologies for future software development paths. We propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations. The motivation for this research is clear: the software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it.

Trust, artificial intelligence and software practitioners: an interdisciplinary agenda

AbstractTrust and trustworthiness are central concepts in contemporary discussions about the ethics of and qualities associated with artificial intelligence (AI) and the relationships between people, organisations and AI. In this article we develop an interdisciplinary approach, using socio-technical software engineering and design anthropological approaches, to investigate how trust and trustworthiness concepts are articulated and performed by AI software practitioners. We examine how trust and trustworthiness are defined in relation to AI across these disciplines, and investigate how AI, trust and trustworthiness are conceptualised and experienced through an ethnographic study of the work practices of nine practitioners in the software industry. We present key implications of our findings for the generation of trust and trustworthiness and for the training and education of future software practitioners.

Research on mine ventilation network solution and collaborative control technology

With the development of intelligent ventilation system in domestic coal mines, the demand for ventilation network solving software is becoming more and more intense. Aiming at the problem that the traditional ventilation network solution algorithm is difficult to meet the increasingly complex ventilation system solution needs of domestic mines, and the lack of functions of the collaborative control of ventilation equipment and facilities, this paper uses theoretical analysis combined with mine reality to study the relevant theories and algorithms in the field of ventilation network solution and collaborative control at home and abroad, and optimizes the network solution and collaborative control related algorithms such as iterative calculation of ventilation network, identification and stability analysis of angle-linked wind network, natural wind pressure calculation, local fan collaborative control, and collaborative control of damper and wind window. Provide theoretical guidance for future network solution software development.