Software Process Research Articles

Mobile Application to Improve the Follow-up and Control Process in Patients with Tuberculosis

Tuberculosis is a severe and life-threatening illness that affects numerous individuals worldwide every day. The key objective of this study was to create a system that could enhance the monitoring and management of tuberculosis patients. To achieve this goal, the Mobile D methodology was utilized because of its effectiveness in project management. This methodology emphasizes test-driven development, continuous integration, and optimization to enhance software processes. The outcome of this research was a prototype of a mobile application specifically designed for individuals with tuberculosis. Professionals and people affected by the disease assessed the quality of the prototype. They evaluated its effectiveness, user-friendliness, design, and functionality and gave ratings of 4.77 and 4.69 on a Likert scale, respectively. These figures indicate that the prototype meets high-quality criteria. In conclusion, this research successfully created an efficient prototype that enhances the monitoring and control of tuberculosis patients. The prototype includes features such as real-time consultations for immediate interaction between physicians and patients, clinical history visualization, and medication reminders, all of which improve the user’s experience.

Analysis of Silica-Hexadecyltrimethoxysilane Nanosol Hybrids through optimization of Silica Nanosol Concentration and determining fabric types on Hydrophobicity

An analysis of the silica nanosol-hexadecyltrimethoxysilane hybrid has been carried out through optimizing the concentration of silica nanosol and determining the type of fabric regarding hydrophobicity. This research began with the synthesis of silica nanosol via the sol-gel method, where TEOS as a precursor was dissolved in a mixture of ethanol-distilled water with a base catalyst with a contact time of 2 hours. The concentration of silica nanosol was varied by 0.025, 0.05, and 0.075 mol. The dip-coating method was used to coat the fabric with nanosol, with a tensile rate of 3 cm per second. Then the fabric was re-coated with silica nanosol in HDTMS solution (4% wt/wt ethanol) and dried in a 120°C oven for 10 minutes. This research tested calico, cotton and mori fabrics. A Canon DSLR camera with a Thamron 100mm lens was used to measure the contact angle of the fabric surface. ImageJ software processes images to obtain contact angle values on the fabric surface. The fabric was then characterized using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) and the silica nanosol was characterized using a Transmission Electron Microscope (TEM). The optimum concentration of silica-hdtms nanosol was obtained at a concentration of 0.075 mol with surface contact angles of cotton, mori and calico fabrics of 134, 132 and 135°. The flow speed of water with a slope of 100 on cotton, mori, and calico fabric produces flow speeds of 3.3, 6, and 5 m/s.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

How do we achieve blinding in modern electronic and paper medical records during the conduct of transfusion trials?

Regulatory aspects of transfusion medicine add complexity in blinded transfusion trials when considering various electronic record keeping software and blood administration processes. The aim of this study is to explore strategies when blinding transfusion components and products in paper and electronic medical records. Surveys were collected and interviews were conducted for 18 sites across various jurisdictions in North America to determine solutions applied in previous transfusion randomized control trials. Sixteen responses were collected of which 11 had previously participated in a transfusion randomized control trial. Various solutions were reported which were specific to the laboratory information system (LIS) and electronic medical record (EMR) combinations although solutions could be grouped into four categories which included the creation of a study product code in the LIS, preventing the transmission of data from the LIS to the EMR, utilizing specialized stickers and labels to conceal product containers and documents in the paper records, and modified bedside procedures and documentation. LIS and EMR combinations varied across sites, so it was not possible to determine combination-specific solutions. The study was able to highlight solutions that may be emphasized in future iterations of LIS and EMR software as well as procedural changes that may minimize the risk of unblinding.

Risk Management in the LPG Refrigerated Green Terminal Project for Sustainable Development Goals

The LPG Refrigerated Green Terminal Project for Sustainable Development Goals seeks to enhance national stock resilience and reduce reliance on leased refrigerated LPG floating storage by a State-Owned Oil and Gas Company. Various challenges were encountered throughout the project, including resident demonstrations causing schedule disruptions, delays in project design due to client approval processes, and disparities in land data during tendering and implementation phases, among others. This research aims to identify the most critical risk by assessing the number of priority risks and risk categories. Effective risk management is pivotal in achieving project objectives. The methodology employed encompasses Fault Mode and Effect Analysis (FMEA), a 3D Risk Matrix, and a Pareto Diagram, supplemented by Analytical Hierarchy Process (AHP) software for risk weight determination. The study reveals a minimum Risk Priority Number (RPN) of 6, signifying low-risk severity, infrequent risk occurrence, and the company’s ability to detect these risks. Conversely, the highest RPN is recorded at 36, indicating significant risk severity, frequent risk occurrence, and challenges in risk detection by the company.

Exploring the trend of recognizing apple leaf disease detection through machine learning: a comprehensive analysis using bibliometric techniques

This study’s foremost objectives were to scrutinize how unexpected weather affects agricultural output and to assess how well AI-based machine learning and deep leaning algorithms work for spotting apple leaf diseases. The researchers carried out a bibliometric study to obtain understanding of the current research trends, citation patterns, ownership and partnership arrangements, publishing patterns, and other parameters related to early identification of apple illnesses. Comprehensive interdisciplinary scientific maps are limited because syndrome recognition is not restricted to any solitary arena of research, despite the fact that there have been many studies on the identification of apple diseases. By employing a scientometric technique and 109 publications from the Scopus database published between 2011 and 2022, this study attempted to assess the condition of the research area and combine knowledge frameworks. To find important journals, authors, nations, articles, and topics, the study used the automated processes of VOSviewer and Biblioshiny software. Patterns and trends were discovered using citation counts, social network analysis, and citation and co-citation studies.

An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs

In the digital era of increasing software complexity, improving the development efficiency of safety-critical software is a challenging task faced by academia and industry in domains such as nuclear energy, aviation, the automotive industry, and rail transportation. Recently, people have been excited about using pre-trained large language models (LLMs) such as ChatGPT and GPT-4 to generate code. Professionals in the safety-critical software field are intrigued by the code generation capabilities of LLMs. However, there is currently a lack of systematic case studies in this area. Aiming at the need for automated code generation in safety-critical domains such as nuclear energy and the automotive industry, this paper conducts a case study on generating safety-critical software code using GPT-4 as the tool. Practical engineering cases from the industrial domain are employed. We explore different approaches, including code generation based on overall requirements, specific requirements, and augmented prompts. We propose a novel prompt engineering method called Prompt-FDC that integrates basic functional requirements, domain feature generalization, and domain constraints. This method improves code completeness from achieving 30% functions to 100% functions, increases the code comment rate to 26.3%, and yields better results in terms of code compliance, readability, and maintainability. The code generation approach based on LLMs also introduces a new software development process and V-model lifecycle for safety-critical software. Through systematic case studies, we demonstrate that, with appropriate prompt methods, LLMs can auto-generate safety-critical software code that meets practical engineering application requirements. It is foreseeable that LLMs can be applied to various engineering domains to improve software safety and development efficiency.

An Open-Source Software Reliability Model Considering Learning Factors and Stochastically Introduced Faults

In recent years, software development models have undergone changes. In order to meet user needs and functional changes, open-source software continuously improves its software quality through successive releases. Due to the iterative development process of open-source software, open-source software testing also requires continuous learning to understand the changes in the software. Therefore, the fault detection process of open-source software involves a learning process. Additionally, the complexity and uncertainty of the open-source software development process also lead to stochastically introduced faults when troubleshooting in the open-source software debugging process. Considering the phenomenon of learning factors and the random introduction of faults during the testing process of open-source software, this paper proposes a reliability modeling method for open-source software that considers learning factors and the random introduction of faults. Least square estimation and maximal likelihood estimation are used to determine the model parameters. Four fault data sets from Apache open-source software projects are used to compare the model performances. Experimental results indicate that the proposed model is superior to other models. The proposed model can accurately predict the number of remaining faults in the open-source software and be used for actual open-source software reliability evaluation.

Self-esteem, entrepreneurial mindset, and entrepreneurial intention: A moderated mediation model

Entrepreneurial intention has been extensively debated; however, research on factors influencing entrepreneurial intention is still in development. This study examines the relationship between self-esteem, entrepreneurial mindset, and self-efficacy with entrepreneurial intention connections among students from five prominent public Iranian universities. It proposes a moderator role for entrepreneurial self-efficacy in the interaction between self-esteem, mindset, and entrepreneurial intention. Data were collected through a survey questionnaire, and 406 students were collected from five universities in Iran. The SPSS MACRO Process and PLS-SEM software examined the analysis data. The results indicated that the impact of self-esteem and entrepreneurial mindset on entrepreneurial intention was supported, but the influence of self-esteem on entrepreneurial mindset was not supported. However, self-esteem positively affects entrepreneurial intention through mindset only when self-efficacy is low and medium. Furthermore, the direct effect of self-esteem and mindset on intention decreases as self-efficacy increases; this means that when self-efficacy is medium and high, it becomes the most important predictor of intention. Finally, this study provides evidence to families, educational institutions, and policymakers at educational, cultural, and support levels in Iran as a developing country that strengthens self-esteem, self-efficacy, and the entrepreneurial mindset to evolve entrepreneurship. The implications of our findings were discussed.

A survey of energy concerns for software engineering

There is growing attention to energy efficiency in the software engineering field. This has been driven by modern technologies, for example, Internet of Things (IoT), Social Networking Services (SNS) and quantum computing. In addition to this, recent trends and concerns such as Environment, Social, and Governance (ESG) and human/societal/environmental well-being for responsible Artificial Intelligence (AI) have accelerated the use of energy efficient software. Despite this, energy concerns in this field have been less explored and studied. This limitation results in falling short to address and overcome greenability issues at the software level, and leaving critical challenges to be solved in this space. This study aims to address this limitation and fill the gap between previous studies. We survey green in software engineering framed by the ten knowledge areas of software engineering to not only cover the entire development life-cycle but also widen the scope of discussion to software process, method, and model management. Based on our comprehensive investigation, we discuss open challenges, trade-offs and implications of this study for both researchers and practitioners.

PENGARUH POLA ASUH ORANG TUA PERMISIF TERHADAP KECANDUAN PERMAINAN DARING YANG DIMEDIASI OLEH KESEJAHTERAAN PSIKOLOGIS

Online game addiction among adolescents is currently a major concern for parents in Indonesia. The parenting style implemented by parents can be one of the factors that trigger online game addiction in adolescents, one of which is the permissive parenting style. The main aim of this study is to investigate the effect of permissive parenting style on online game addiction mediated by psychological well-being, referring to the I-PACE theory. The quantitative method was employed in the study and respondents were selected using the purposive sampling technique. Participants in this study were adolescents aged 16-20 years throughout Indonesia who were collected by distributing questionnaires via social media. Data were collected from 269 respondents who were analyzed using the mediation model regression technique by Hayes with PROCESS software on the IBM SPSS version 25 for Windows application. The results of this study indicated that psychological well-being did not significantly mediate the effect of a permissive parenting style on online game addiction. However, psychological well-being significantly predicted online game addiction directly. Meanwhile, a permissive parenting style significantly predicted online game addiction. Permissive parenting style also significantly predicted psychological well-being. The permissive parenting style of fathers has a stronger effect on online gaming addiction.

Ferramenta digital para primeiros socorros na infância: aplicativo para profissionais e cuidadores

Abstract Objective To describe the process of developing an app for children first aid support for professionals and caregivers. Methods Technological production study, developed in southern Brazil, between March/2022 and May/2023, based on the functionalities of the scrum method. Designed in cooperation with an interprofessional team, the software was developed based on a set of light and agile practices, the framework called Flutter and the Dart programming language. Results The app, developed in a systematic and participatory way with the participation of health and education professionals and caregivers, demonstrated plausible functionality, accessibility, feasibility and reliability in providing agile and safe information in events such as children’s first aid. Called SOS Kids, the app has an attractive interface, quick access to visual information and various commands on a single screen. The graphic elements are interactive, in order to favor visualization and prospective decision-making. Conclusion The development process of the child first aid support software for professionals and caregivers, based on a method that relies on light and agile practices, proved to be functional and has the potential to support, in an effective and safe way, health professionals, education professionals and caregivers in general.

Building Execution Plan as an effective document for Building Information Modelling

The use of Building Information Modelling (BIM) technology is recently becoming an increasingly influential factor in the successful completion of construction projects. Providing a wide range of technical enhancements to the design process (for example software for 3D modelling), BIM also offers a wide range of tools for managing construction projects. The goal of Building Information Modelling is to eliminate gaps in data transmission between different stages of the life cycle of a construction project. In this context, BEP, or BIM Execution Plan, is an essential document in the BIM concept. It functions as a tool for managing a project, both from the human resources perspective, such as meeting deadlines, and from the perspective of preserving and transferring graphical and non-graphical data of the project. Furthermore, it provides a detailed plan for building design, construction, and facility management, helping to ensure that all stakeholders are in line with the project’s goals. This article examines BEP as it evolves throughout various life cycle stages, from initial design and planning to construction and final delivery, exploring its potential in facility management.

РАЗРАБОТКА ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ МОДЕЛИРОВАНИЯ ПРОЦЕССОВ ТЕПЛОМАССОПЕРЕНОСА ПРИ МНОГОФАЗНОМ ПОТОКЕ

Mathematical models simulating the process of heat and mass transfer in a multiphase flow and taking into account the thermal conductivity of the working fluid, taking into account its location in space, physical properties and other characteristics, are difficult to implement in the form of software. Usually such models are represented by nonlinear equations, which makes it difficult to implement them correctly using numerical methods. When developing such mathematical models, hydrodynamics, heat transfer of a moving working fluid and the dependence of viscosity on temperature and velocity gradient are not taken into account. (Research purpose) The research purpose is developing a mathematical model of the functioning of the heat supply system as a set of cylindrical pipelines with a heat source through which the working fluid moves. Additional conditions are introduced to achieve high accuracy of calculations at boundary points. The possibility of using analytical equations to calculate the internal heat dissipation during the transportation of the working fluid inside the pipeline is studied. (Materials and methods) Using the two-phase delay theory (DPL-model) and the finite element method (FEM) as flow balancing methods, we obtain a model of the heat exchange of the working fluid motion in the pipeline with the possibility of taking into account relaxation properties. (Results and discussion) The developed algorithm is implemented using the high-level interpreted programming language MATLAB and its simulation environment for complex technical systems Simulink. (Conclusions) Equations modeling the real motion of a multiphase working fluid in locally nonequilibrium systems, including heat supply systems, are obtained. The results obtained make it possible to compile an algorithm and implement it using the high-level interpreted programming language MATLAB, defining a working version of the software for modeling heat and mass transfer processes in a multiphase flow.

Software Testing Using Cuckoo Search Algorithm with Machine Learning Techniques

Software testing are any errors, flaws, bugs, mistakes, failures in a piece of software that might cause the programme to produce incorrect or unexpected results. Testing in software almost always increase both the time and money needed to finish a project. And finding bugs and fixing them is a laborious and expensive software process in and of itself. While it's unrealistic to expect to completely eradicate all testing from a project, their severity may be mitigated. It is possible to predict where bugs may appear in software using a method known as software defect prediction (SDP). The goal of each software development project should be to provide a bug-free product. Predicting where bugs may appear in code, often known as software defect prediction (SDP), is an important part of fixing software. Software of a high calibre should have few bugs. A software metric is a quantitative or qualitative evaluation of some aspect of the programme or its requirements. One of the more recent population-based algorithms, Cuckoo Search (CS) was inspired by the flight patterns of some cuckoo species as well as the Lévy flying patterns of other birds and fruit flies. The needs for international convergence are met by CS. KNN is a significant non-parameter supervised learning technique. This paper presents an overview of Stochastic Diffusion Search (SDS) in the form of a social metaphor to illustrate the processes by which SDS allots resources. The best-fit pattern identification and matching difficulties were addressed by SDS using a novel probabilistic method. As a multiagent population-based global search and optimization method, SDS is a distributed model of computing that makes use of interaction amongst basic agents. The behaviour of SDS is described by studying its resource allocation, convergence to global optimum, resilience, minimum convergence criterion, and linear time complexity within a rigorous mathematical framework, setting it apart from many nature-inspired search algorithms. This paper proposes a hybrid optimization strategy based on CS-SDS techniques. By using the global search strategy solution of the SDS algorithm, this hybridization idea aims to enhance the cuckoo bird's search strategy for the optimum host nest. To that end, the SDS method would be used to place the cuckoo egg in the most advantageous location. When compared to other classifiers, PC2's improved performance may be attributed to its higher recall values. When compared to the Naive Bayes and Radial Bias Neural Network classifiers, the KNN performs 7.64% and 2.20% better, respectively.

An Organizational Modeling for Developing Smart Contracts on Blockchain-based Supply Chain Finance Systems

Managing smart contracts in an Agile software development based on blockchain technology enables project managers to offer rules for software processes to meet the needs and expectations of stakeholders. On the one hand, smart contracts’ faster cycle time, lower fraud, and lower fees and charges have ensured that the business logic satisfies the stakeholder’s criteria. Agile approaches, on the other hand, are development processes that use incremental and iterative engineering methodologies to drive the system life cycle. Such methodologies are typically well-suited for incorporating and adapting management ideas early in the development life cycle. To reconcile both viewpoints, this work provides an organization modeling for constructing smart contracts on the blockchain utilizing organizational modeling techniques that allow all processes as well as the smart contracts management discipline in supply chain finance system development to be illustrated.

Activity-Based Detection of (Anti-)Patterns: An Embedded Case Study of the Fire Drill

Background: Nowadays, expensive, error-prone, expert-based evaluations are needed to identify and assess software process anti-patterns. Process artifacts cannot be automatically used to quantitatively analyze and train prediction models without exact ground truth. Aim: Develop a replicable methodology for organizational learning from process (anti-)patterns, demonstrating the mining of reliable ground truth and exploitation of process artifacts. Method: We conduct an embedded case study to find manifestations of the Fire Drill anti-pattern in n=15 projects. To ensure quality, three human experts agree. Their evaluation and the process’ artifacts are utilized to establish a quantitative understanding and train a prediction model. Results: Qualitative review shows many project issues. (i) Expert assessments consistently provide credible ground truth. (ii) Fire Drill phenomenological descriptions match project activity time (for example, development). (iii) Regression models trained on approx. 12–25 examples are sufficiently stable. Conclusion: The approach is data source-independent (source code or issue-tracking). It allows leveraging process artifacts for establishing additional phenomenon knowledge and training robust predictive models. The results indicate the aptness of the methodology for the identification of the Fire Drill and similar anti-pattern instances modeled using activities. Such identification could be used in post mortem process analysis supporting organizational learning for improving processes.

Exploring the Effects of Team Learning Capabilities and Team Climates in Software Process Tailoring

Exploring the Effects of Team Learning Capabilities and Team Climates in Software Process Tailoring

Enhancing Software Process Assessment with an Ontology-Based Tool: Integrating CMMI, SPICE, and TMMI Models

Software process reference models like Capability Maturity Model Integration (CMMI) and Software Process Improvement and Capability Determination (SPICE) have played a crucial role in evaluating processes for several decades. Their adoption by institutions has become essential for consistently delivering software projects to customers on time and with expected quality. However, conducting software process assessments demands considerable time, effort, and skilled personnel. This research introduces an ontology-based tool that facilitates software process assessment for organizations by integrating widely used process reference models, namely CMMI, SPICE, and Test Maturity Model Integration (TMMI). The development of this tool involves multiple stages: creating ontologies for each process reference model, integrating them into the tool, enabling querying capabilities, and visualizing the ontologies. Through a validation study in a selected organization, two sets of processes were assessed using the Ontology-Based Software Process Assessment Tool (OSPAT). The results demonstrated that organizations can benefit from OSPAT in evaluating their software development processes across diverse reference models, thereby enhancing overall process efficiency and quality.

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

This article outlines the design and development process of an automatic product categorization software intended for use in e-commerce and online marketplace platforms. The project aims to tackle the urgent issue of effectively classifying a wide range of products within digital markets. By utilizing artificial intelligence (AI) and machine learning methodologies, the software effectively analyzes product descriptions, enabling users to seamlessly incorporate products through automated categorization. The significance of the project is rooted in its ability to guarantee the accuracy and consistency of category hierarchies, automate the process of categorizing, and improve the overall user experience. The main goals involve the process of project planning, data collecting, and preparation. Machine learning models have been built and subsequently incorporated to facilitate the study of product descriptions. Through strict evaluation and optimization processes, a high level of accuracy and efficiency is achieved, resulting in several anticipated benefits. These benefits cover automated product categorization, enhanced user experience, and the potential for online platforms to gain a competitive edge. The key elements of innovation involve AI-driven textual analysis, learning methodologies grounded in data, and the ability to adapt to diverse industry contexts. Precautions and backup strategies are implemented to tackle technical issues, including the selection of machine learning libraries and algorithms, ensuring data quality, and integrating with various platforms. The success criteria encompass the objective of achieving a minimum prediction accuracy rate of 90%, optimizing business efficiency, enhancing user pleasure, and ensuring smooth system functioning. This project is a significant contribution to the field of product categorization inside the digital marketplace, as it provides automation, accuracy, and efficiency, ultimately resulting in an enhanced user experience.