Software Development Research Articles

Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing-Assisted Intelligent Systematic Literature Review System.

Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task. This study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data. We followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning-based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus-associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines. Our Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks). Cutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously.

Construction of a prototype of the basic module of a scalable robotic system for simulation of composite surfaces for dynamic tests

The authors of the article developed a mathematical model of Stewart platform management. The angles of rotation of the servo shafts were obtained when setting various laws of motion of the mobile platform, in matrix mode. The hardware part of the prototype of the basic module of a scalable robotics system for modeling composite surfaces for dynamic tests was developed. The results of calculations of the maximum permissible load on the shafts of servos are presented. The CubeIDE settings for programming the STM32 microcontroller are given. The results of software development for the control of this prototype were obtained, namely, a library for controlling 6 servo drives under driver control was written. The problems that may arise during the above-described developments by future developers are considered, solutions to these problems are obtained. Detailed design features of this prototype are described, the control code of the servo drive is given; steps for further development of the final robotic system are considered

Development and Validation of a Theoretical Model for Addressing Problems in Agile Meetings: A Systematic Literature Review and a Qualitative Study

Agile meetings are an essential part of modern software development, but they often face challenges such as lack of focus, communication problems, technological difficulties, and the like. Previous research has often addressed individual issues of agile meetings but has not provided sufficiently integrated or practical models to comprehensively address these challenges. This research fills this gap, developing a theoretical model that not only identifies key issues but systematically links them to provide a deeper understanding of their impact on meeting effectiveness.In this paper, we present a theoretical model that depicts intra-team problems in agile meetings developed through a systematic literature review (SLR). After initial development, the model was validated and improved through the qualitative analysis of semi-structured interviews with IT experts. This research aimed to confirm the coverage and relevance of the content of the preliminary model, as well as to identify the need for modification. The results of the interviews suggest that the presented model is adequate but that there is a need for potential improvement. IT experts agreed with the items in the model, and some of them suggested improvements. The model is promising because it illustrates the key issues that influence the effectiveness of agile meetings in a promising way. IT experts agreed on the items in the model, and some of them suggested improving the model.

Generative AI in Higher Education: Educators' Perspectives on Academic Learning and Integrity

Generative Artificial Intelligence (Gen AI), exemplified by models such as ChatGPT, has rapidly advanced, becoming a significant force in various sectors, including higher education. ChatGPT, a leading application of Gen AI, utilizes large language models (LLMs) to generate human-like text, providing capabilities that range from answering complex questions to facilitating software development. As these tools become increasingly integrated into academic environments, their impact on teaching and learning processes has become more under focus. However, there are not many studies focusing on the impact of such systems on students’ performance and the use of such systems. This study therefore examines the impact of GPT tools on higher education, aiming to address the following research questions: How does the use of GPTs influence the teaching and learning process in higher education? What are the perceived impacts of GPTs on educational practices from university educators’ perspective? The research employs a qualitative methodology, incorporating semi-structured interviews with educators from Swedish universities who have integrated GPT into their curricula. Data were transcribed and analysed using OpenAI's Whisper to extract key themes and insights. Data analysis was done through thematic analysis and categorizing the data using codes. The study uncovers a dual impact of GPT on education, while it offers substantial opportunities for enhancing productivity and personalized learning, it also raises significant concerns about academic integrity, over-reliance on AI, and the potential influences on students’ soft skills. These findings contribute to the discourse on digital learning by highlighting the need for instructional and constructive integration of AI technologies such as GenAI, in educational settings. In addition, the rise and integration of GPT technology is irreversible, and we must adapt to it rather than return to old ways. Embracing AI's potential while addressing its challenges is essential for progress and innovation in this new era. The study emphasizes the importance of developing clear policies and guidelines to ensure that the benefits of GPT are realized without compromising the integrity of the educational process. As such, this research provides valuable insights for educators, policymakers, and scholars interested in the ethical and effective implementation of AI in higher education

Improving bioinformatics software quality through teamwork.

Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers' work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

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.

Agency mode selection and software competition in supply chains

AbstractAt present, many software developers work with downstream service providers to sell software and services. For political or economic reasons, foreign developers may be not allowed to sell software and services directly in the country of providers, but they can authorize their software to the providers for sale. In this study, we examine two types of IT supply chain (ITSC), namely, “Agenting Domestic Software” (Mode 1) and “Agenting Foreign Software” (Mode 2), each with a domestic developer/a foreign developer, a service provider, and client enterprises. In both modes, clients can either pay a high price to purchase software with accessorial pre‐sale service from the provider, or firsthand acquire it from the domestic developer at a low price but without pre‐sale service. We observe that in Mode 1, the domestic developer's market scale and the competition intensity of software have no effect on the domestic developer's decisions and profit; however, they have influences on the provider's decisions and profit. In Mode 2, the profits of ITSC members may increase with the intensity of software competition. When the domestic developer's market scale is small (large), and the domestic developer has (loses) the advantage of low extended‐warranty‐service (EWS) cost, Mode 1 (2) is more beneficial to the domestic developer than Mode 2 (1); however, the provider is more willing to choose Mode 2 (Mode 1). In other cases, either Mode 1 or Mode 2 may result in a win‐win situation for the domestic developer and provider.

A survey on Cryptoagility and Agile Practices in the light of quantum resistance

Context:Crypto-agility, a name that stems from agile methodologies for software development, means the ability to modify quickly and securely cryptographic algorithms in the event of a compromise. The advent of quantum computing poses existential threats to current cryptography, having the power to breach current cryptography systems. Objective:We investigated whether and to what extent agile practices for software development are suited to support crypto-agility, or not. In particular, we discuss their usefulness in the context of substituting current algorithms with quantum-resistant ones. Method:First, we analyzed the literature to define a subset of 15 agile practices potentially relevant to cryptographic software development. Then, we developed a questionnaire to assess the suitability of agile practices for obtaining crypto-agility. We performed a Web search of relevant documents about crypto-agility and quantum resistance and sent their authors the questionnaire. We also sent the questionnaire to cybersecurity officers of four Italian firms. We analyzed and discussed the responses to 32 valid questionnaires. Results:The respondents’ affiliations are evenly distributed between researchers and developers. Most of them are active, or somehow active, in quantum-resistant cryptography and use agile methods. Most of the agile practices are deemed to be quite useful, or very useful to get crypto-agility, the most effective being Continuous Integration and Coding Standards; the least appreciated is Self-organizing Team. Conclusion:According to researchers and developers working in the field, the safe transition of cryptographic algorithms to quantum-resistant ones can benefit from the adoption of many agile practices. Further software engineering research is needed to integrate agile practices in more formal cryptographic software development processes.

Refining software defect prediction through attentive neural models for code understanding

Identifying defects through manual software testing is a resource-intensive task in software development. To alleviate this, software defect prediction identifies code segments likely to contain faults using data-driven methods. Traditional techniques rely on static code metrics, which often fail to reflect the deeper syntactic and semantic features of the code. This paper introduces a novel framework that utilizes transformer-based networks with attention mechanisms to predict software defects. The framework encodes input vectors to develop meaningful representations of software modules. A bidirectional transformer encoder is employed to model programming languages, followed by fine-tuning with labeled data to detect defects. The performance of the framework is assessed through experiments across various software projects and compared against baseline techniques. Additionally, statistical hypothesis testing and an ablation study are performed to assess the impact of different parameter choices. The empirical findings indicate that the proposed approach can increase classification accuracy by an average of 15.93% and improve the F1 score by up to 44.26% compared to traditional methods.

Bridging the gap in talent: A framework for interdisciplinary research on autism spectrum disorder persons in accounting and information systems

The accounting profession continues to face a talent shortage. In addition, the skills and demands of accountants continue to evolve as technology changes. At the same time, many autism spectrum disorder (ASD) persons remain unemployed or underemployed. Companies, including the Big 4, have begun hiring programs looking at a diverse set of individuals, including ASD persons. General anecdotes suggest ASD persons are thriving in terms of innovation, efficiency, and productivity. While neurotypical (NT) persons have been shown in multiple studies to rely on simple heuristics, which reduce judgment quality, we argue that there are neuropsychological differences in visual perception and attention that play a vital role in why ASD persons may be less susceptible to heuristics and biases; hence, increasing judgment quality. This paper presents an interdisciplinary research framework that adapts the human information processor model of cognition to bring together the judgment and decision-making, autism, and neuropsychological literatures to focus on areas where ASD persons may have judgment-related advantages over NT persons. We discuss the framework and present example research questions for two areas where ASD accountants are performing tasks: cybersecurity and software development. Our framework presents a guide for future empirical research where accounting academics can lead an interdisciplinary effort to support company inclusion programs of ASD persons and, more broadly, of neurodivergent persons.

AI Copilot for the Modern Developer : Leveraging GenAI in Software Development

This paper explores the transformative impact of AI copilots on software development, powered by Generative AI and Large Language Models. It examines how these tools enhance developer productivity, improve code quality, and democratize software development. The study delves into the core technologies behind AI copilots, including code synthesis and contextual learning, and discusses their integration with development environments. The paper also investigates the role of reinforcement learning in continuously improving AI copilots and analyzes their impact on software development practices. Additionally, it explores future prospects, including more sophisticated natural language understanding and improved cross-language support. Throughout, the paper emphasizes how AI copilots are reshaping the landscape of software engineering, potentially leading to more efficient, higher-quality, and innovative software solutions.

Terminology management in computer-assisted translation (CAT) tools MemoQ as a case study

Today, translation is the systematic thread that brings different civilizations and cultures together and brings them closer together. In the light of the considerable and rapid advances in technology, the responsibilities of the translator or of the translation companies increase on a daily basis. This led software developers to find tools to help the translator achieve those standards and enrich the translation market with new skills to handle them. Among these tools, perhaps most noteworthy is computer-assisted translation (CAT) Tools. The present paper sheds the light on one of the most important features in computer-assisted translation (CAT) Tools that is terminology management, furthermore, by taking MemoQ application as a case study, it shows a practical guide to create and use term base within this application.

Continuous Integration and Continuous Deployment (CI/CD) Optimization

The advent of Continuous Integration and Continuous Deployment (CI/CD) has fundamentally altered the landscape of software development, enabling teams to deliver updates with unprecedented speed and reliability. By automating the integration of code changes from multiple developers into a central repository, CI/CD practices ensure that software is continuously tested and deployed. This ongoing cycle not only facilitates quicker release cycles but also enhances collaboration among team members and fosters a culture of shared responsibility for code quality. Despite these advancements, organizations face significant challenges in optimizing their CI/CD pipelines. As software systems grow in complexity, the demand for swift and dependable deployments intensifies. This paper explores various techniques and strategies for optimizing CI/CD processes to minimize deployment times while maintaining system reliability. Key optimization methods discussed include: Parallelization of Build Processes: This technique involves breaking down the build process into smaller, independent tasks that can be executed concurrently. By leveraging distributed computing resources, organizations can significantly reduce build times, allowing for faster iterations and deployments. Dependency Caching: Caching dependencies can drastically decrease build times by reusing previously downloaded components. This approach not only speeds up the build process but also minimizes network load and enhances the overall efficiency of the CI/CD pipeline. Incremental Builds: Unlike full builds that compile the entire codebase, incremental builds focus on compiling only the changes made since the last build. This strategy reduces the amount of work needed for each build, accelerating the overall development process. The paper also delves into advanced rollback mechanisms such as blue-green deployments and canary releases. Blue-green deployments allow teams to maintain two identical production environments, enabling smooth transitions and quick rollbacks in case of issues. Canary deployments, on the other hand, introduce new features to a small subset of users before a full rollout, allowing teams to monitor the impact and catch potential failures early. Additionally, automated rollback mechanisms play a vital role in maintaining system reliability, ensuring that any failed deployments can be reverted swiftly to avoid downtime and user disruption. The impact of automation tools on deployment speed and error reduction is another critical aspect examined in this research. Automation frameworks can streamline various stages of the CI/CD process, from code integration to testing and deployment, minimizing human error and ensuring consistent, repeatable processes. By implementing robust automation strategies, organizations can not only accelerate their deployment cycles but also improve overall software quality.

Virtual Computer Laboratory Learning System Integrated With Social Media And Video Conference

The advancement of information and communication technology has driven innovation in various aspects of education, including computer laboratory learning. Traditional computer laboratories often face challenges in terms of time, cost, and accessibility. To address these challenges, this study developed a virtual computer laboratory learning system integrated with social media and video conferencing. This system is designed to provide flexible access, allowing students to learn and practice anytime and anywhere. The integration of social media aims to enhance interaction and collaboration between students and instructors, while video conferencing enables real-time learning sessions with direct interaction. The research methods used include system design, software development, and system testing. The results showed that the virtual computer laboratory learning system integrated with social media and video conferencing can facilitate learning and is expected to be an innovative solution in information technology education.

Dynamics and Control of a Novel Discrete Internet Rumor Propagation Model in a Multilingual Environment

In the Internet age, the development of intelligent software has broken the limits of multilingual communication. Recognizing that the data collected on rumor propagation are inherently discrete, this study introduces a novel SIR discrete Internet rumor propagation model with the general nonlinear propagation function in a multilingual environment. Then, the propagation threshold R0 is obtained by the next-generation matrix method. Besides, the criteria determining the spread or demise of rumors are obtained by the stability theory of difference equations. Furthermore, combined with optimal control theory, prevention and refutation mechanisms are proposed to curb rumors. Finally, the validity and applicability of the model are demonstrated by numerical simulations and a real bilingual rumor case study.

Software Change Request in Software Development Project : Factors and Methods (Scoping Review Methods)

Software development is dynamic by nature, often requiring changes in software requirements through Software Change Requests (SCRs). This research aims to identify and analyze the key factors that influence SCRs in software development projects and evaluate the methods used to effectively manage these changes. This research uses the Scoping Review Method to systematically review and synthesize existing literature on SCRs, aiming to provide a comprehensive understanding of current practices, challenges, and strategies for managing software change requests. The review includes an extensive search of academic databases to identify relevant studies that address SCR factors and management methods. Key factors that influence SCR include project size, complexity, stakeholder involvement, and clarity of initial requirements. Methods for managing SCR are categorized into formal approaches, such as structured change control processes and tools, and informal approaches, which rely on ad-hoc communication and collaboration. The results underscore the importance of a well-defined change management process to mitigate the risks associated with SCR. Effective SCR management can improve project outcomes by improving software quality, increasing stakeholder satisfaction, and minimizing project delays. This research has practical implications for software development professionals by providing insight into effective SCR management techniques. This research also identifies gaps in the literature, recommending future research on the impact of agile methodologies and the use of automated tools in facilitating change management. Understanding these factors and methods enables practitioners to better navigate the complexities of software change, ensuring successful project delivery and maintaining high software quality.

Sequence Flow: interactive web application for visualizing partial order alignments

BackgroundMultiple sequence alignment (MSA) has proven extremely useful in computational biology, especially in inferring evolutionary relationships via phylogenetic analysis and providing insight into protein structure and function. An alternative to the standard MSA model is partial order alignment (POA), in which aligned sequences are represented as paths in a graph rather than rows in a matrix. While the POA model has proven useful in several applications (e.g. sequencing reads assembly and pangenome structure exploration), we lack efficient visualization tools that could highlight its advantages.ResultsWe propose Sequence Flow – a web application designed to address the above problem. Sequence Flow presents the POA as a Sankey diagram, a kind of graph visualisation typically used for graphs representing flowcharts. Sequence Flow enables interactive alignment exploration, including fragment selection, highlighting a selected group of sequences, modification of the position of graph nodes, structure simplification etc. After adjustment, the visualization can be saved as a high-quality graphic file. Thanks to the use of SanKEY.js – a JavaScript library for creating Sankey diagrams, designed specifically to visualize POAs, Sequence Flow provides satisfactory performance even with large alignments.ConclusionsWe provide Sankey diagram-based POA visualization tools for both end users (Sequence Flow) and bioinformatic software developers (SanKEY.js). Sequence Flow webservice is available at https://sequenceflow.mimuw.edu.pl/. The source code for SanKEY.js is available at https://github.com/Krzysiekzd/SanKEY.js and for Sequence Flow at https://github.com/Krzysiekzd/SequenceFlow.

UK national survey on nuclear medicine in-house clinical software: the calm before the storm.

The use of in-house developed software as a medical device (IHD-SaMD) is core to many nuclear medicine (NM) services in the UK, including applications in nonimaging studies and image processing. Expected regulatory changes in 2025 could have significant implications due to a lack of resources and expertise in the implementation and maintenance of software Quality Management Systems (QMS) and associated standards. This survey investigated the national use of IHD-SaMD and the readiness of services to adapt to the upcoming regulatory changes. An online survey was used to investigate the current national usage of IHD-SaMD. Representatives of 64 UK NM physics services were invited to participate, with 43 responding. It was found that 98% of respondents use IHD-SaMD clinically. About 65% use IHD-SaMD that respondents felt was under-supported (e.g. legacy software). Approximately 60% of respondents use or support two or more pieces of IHD-SaMD. Around 66% of respondents use a QMS in their department, with about 48% using a software-specific QMS. Most respondents indicate understaffing, particularly with regard to IT/software skillsets. Almost all respondents indicate without an increase in the preparedness and understanding of the requirements, all dependent clinical services would be severely impacted or indeed stopped. This national survey shows that pending regulatory changes could significantly impact NM services, up to and including stopping clinical services. Additional resources would be required to support in-house software management under an appropriate QMS or move to European conformity marking (CE)-marked software where available. This must be urgently considered and addressed by all NM stakeholders.

Organizational Influence on Security Development in Open-Source Software Projects

Increasing technological complexity, intensified competition, and security requirements have driven open-source software (OSS) projects to become a crucial part of organizations' software development. This study focuses on the OSS project TensorFlow (TF) and uses a case study to examine how organizations and their associated developers collaborate to identify, fix and prevent security vulnerabilities. Social Network Analysis (SNA) of archived security data from software repositories is used to gain insight into security activities. The study examines the internal structure and evolution of security code collaboration, organizational networks, and top organizational contributors to TF. It also examines productivity, homophily, development diversity, and turnover rates among developers across various software releases. The in-depth insights from this research enhance our understanding of collaborative patterns in OSS communities within open software ecosystems, particularly in the security context.

VASARI-AUTO: PERFORMANT, EQUITABLE, EffiCIENT, ECONOMIC, AND SURVIVAL PREDICTIVE FEATURISATION OF GLIOMA MRI

Abstract AIMS The VASARI (Visually AcceSAble Rembrandt Images) MRI feature set is a quantitative system designed to standardize glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and therefore seldom used in clinical practice. This is however a problem that performant machine-learning software could plausibly automate. METHOD In 100 patients, two consultant neuroradiologists independently quantified VASARI features. In parallel, we developed VASARI-auto, an automated VASARI-labelling software applied to both open-source hand-segmented lesion masks and our openly available tumour segmentation model. We quantified: 1) agreement across neuroradiologists and VASARI-auto; 2) calibration of patient equity; 3) an economic workforce analysis; and 4) fidelity in predicting patient overall survival. Neuroradiologists were blinded to VASARI-auto software development and evaluations, and software developers were blinded to neuroradiologist labelling. RESULTS Segmentation and VASARI-auto were equally performant regardless of age or sex (mean segmentation Dice coefficient 0.947). A modest inter-rater variability between neuroradiologists (mean Cohen’s Kappa 0.49) was comparable between neuroradiologists and VASARI-auto (mean Cohen’s Kappa 0.41), with higher agreement across VASARI-auto methods (mean Cohen’s Kappa 0.94). The time taken for neuroradiologists to derive VASARI features was substantially higher than VASARI-auto (mean time per case 317 vs. 3 seconds, p<0.0001). A hospital workforce analysis of our centre forecast that three years of VASARI glioma featurisation would demand 771 consultant neuroradiologist workforce hours (£40,789) or 8.6 hours of computing time (£3.76 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features (R2 0.25), opposed to those derived by consultant neuroradiologists (R2 0.21). CONCLUSION VASARI-auto is a highly efficient automated labelling system for VASARI featurisation with equitable performance regardless of patient age or sex, a favourable economic profile if used as a decision support tool, and with non-inferior fidelity in downstream patient survival prediction. Future work should integrate such tools to enhance patient care.