Software Quality Research Articles

Catálogo de directrices de sostenibilidad para soportar el desarrollo y operación de software sostenible desde la ingeniería de requisitos

Sustainability constitutes one of the major challenges of today's society, in particular the sustainability of ICTs and more specifically of software. Requirements engineers need guidelines to successfully address sustainability in software projects. With this goal in mind, the CRETS4DevOps method has been designed to manage requirements in DevOps environments while promoting sustainability, especially technical sustainability. CRETS4DevOps encompasses a catalog of sustainability requirements, which helps in the reuse of expert knowledge in order to address the management of software quality attributes that result in sustainability, namely: modularity, scalability, flexibility, reusability, maintainability, reliability, security, interoperability and efficiency. This catalog is intended to assist practitioners both in software development and operation as well as in a sustainability audit. A tool to support CRETS4DevOps is currently being developed as a plug in for Microsoft Azure DevOps.

Integrating AI in testing automation: Enhancing test coverage and predictive analysis for improved software quality

This research explores the integration of Artificial Intelligence (AI) in testing automation, focusing on its ability to enhance test coverage and enable predictive analysis for improved software quality. As software systems become increasingly complex, traditional testing methods often struggle to meet quality demands. This study evaluates various AI techniques, including machine learning and natural language processing, and their applications in generating test cases, optimizing testing processes, and predicting defects. Through empirical case studies from diverse organizations, we demonstrate significant improvements in test coverage and defect detection rates following AI implementation. The findings highlight the efficiency gains and quality enhancements achieved through AI-driven testing, while also addressing challenges such as data dependency, complexity of implementation, and the need for skilled personnel. This research contributes to the understanding of AI's role in software testing and encourages organizations to adopt these technologies for better quality assurance and faster development cycles.

Assessment of digital literacy and user experience in patients with cochlear implants

Abstract Introduction The cochlear implant (CI) is the treatment of choice for people affected by deafness or profound hearing loss. Patients with cochlear implants (PwCIs) can enhance their hearing outcomes using digital hearing training programs (dHTPs). Current technological trends leans towards personalized, AI-driven dHTPs, aiming to optimize user experience (UX) and adherence. This study aims to investigate the digital literacy skills and UX of PwCI before and after testing a prototype of a dHTP with an AI-driven design (TraiCI). Methods Two research-specific questionnaires developed by the Hearing Center Düsseldorf (QDL and QUX) were used to assess digital literacy skills and user experience in TraiCI. Additionally, the User Experience Questionnaire (UEQ) was employed to evaluate general user experience. The UEQ assesses software quality across dimensions such as transparency and stimulation using paired opposites. Results 22 outpatient PwCIs the Hearing Center Düsseldorf with unilateral or bilateral CI (mean age 54 years; SD = 15) were included. In QDL, PmCI demonstrated high levels of digital literacy skills. The majority of PmCI (80.6%) reported prior experience with dHTP. Usage of these programs occurred daily to multiple times per week within the first six months after CI treatment, with session durations predominantly ranging from 10 to 30 minutes. QUX data indicated a smooth initiation process (70.8%) and seamless login experience (95.5%). The UEQ addressed various aspects of user experience, with ‘transparency’ receiving the highest scores (MW = 1.9; SD = 0.6), while ‘stimulation’ received the least positive ratings (MW = 0.0; SD = 2.1). Conclusions The results show a high degree of digital literacy among PwCIs. They encountered minimal challenges in operating hardware and software components. Further refinement is necessary to align with PwCI’s expectations and maximize benefits. Early and iterative involvement of PwCIs in future projects could optimize outcomes. Key messages • The study highlights the proficient digital literacy skills of cochlear implant patients (PwCIs). • Further refinement of dHTPs is needed to better align with PwCIs’ expectations and maximize benefits, emphasizing the importance of iterative involvement for optimized outcomes.

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.

Test Case Automation: Transforming Software Testing in the Digital Era

Purpose: This research aims to explore the limitations of manual software testing, the effectiveness of test case automation in addressing these challenges, and the strategies for successful implementation of automation. It also examines the broader impact of adopting test automation on the quality, speed, and efficiency of software delivery. Methodology: The study investigates the challenges of manual testing in agile and DevOps environments and explores the role of automation as a solution. By analyzing various automation tools, frameworks, and best practices, the research provides insights into successful implementation strategies for test case automation. Additionally, it reviews the associated benefits and potential challenges organizations may face when transitioning to automated testing. Findings: Test case automation significantly mitigates the shortcomings of manual testing, particularly in fast-paced development cycles. The findings reveal that automation not only enhances the quality of software by catching defects earlier but also accelerates the testing process, improving overall efficiency. However, the study also identifies challenges, such as the initial investment in tools and skills required for automation, as well as the ongoing need for maintenance of automated test cases. Unique Contribution to Theory, Policy and Practice: This research contributes to the theoretical understanding of software testing (Tom Badgett, Corey Sandler & Glenford J. Myers, 2011) by highlighting the essential role of automation in modern software development methodologies like agile and DevOps. It bridges the gap between traditional manual testing and contemporary automated practices, providing a framework for understanding how automation fits within these rapid development paradigms. In practice, the study encourages organizations to adopt test case automation as a critical policy for improving the quality and speed of software delivery. It offers practical insights into the implementation of automation, including tool selection, training, and process integration, which can guide companies in transitioning to automated testing. Additionally, it highlights the long-term benefits of automation, making it a valuable policy consideration for organizations aiming to optimize their development and testing processes.

Construction of the Curriculum System of Software Engineering Based on the Agile Development Method

Under the background of “new engineering” construction, software engineering teaching pays more attention to cultivating students’ engineering practice and innovation ability. In view of the inconsistency between development and demand design, team division of labor, difficult measurement of individual contribution, single assessment method, and other problems in traditional practice teaching, this paper proposes that under the guidance of agile development methods, software engineering courses should adopt Scrum framework to organize course project practice, use agile collaboration platform to implement individual work, follow up experiment progress, and ensure effective project advancement. The statistical data of curriculum “diversity” assessment show that there is an obvious improvement effect on students’ software engineering ability and quality.

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.

Exploring the role of project status information in effective code smell detection

Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection.

Model-driven development for functional correctness of avionics systems: a verification framework for SysML specifications

AbstractCurrently, the most widespread software quality assurance methods in the avionics domain are semi-automated reviews and testing. However, their effort grows disproportionately to the size of the system under development. Also, these methods cannot achieve exhaustive coverage due to the complexity of today’s avionics systems and their potentially infinite set of combinations of possible inputs and system states. Furthermore, the later software issues are detected in the development process, the more expensive it is to fix them. To overcome these issues, a model-driven verification approach for modeling and analyzing avionics systems in early phases of the development is presented. To this end, semantics is given to SysML v2 models by a mapping to a theorem prover encoding. The development of a dedicated SysML v2 profile supporting event-driven data flow specifications, the encoding of corresponding structures in the theorem prover Isabelle, and a generator creating theorems from SysML v2 models are presented. The approach is evaluated by formally proving a representative liveness property of a hierarchical system model from the avionics domain. Since liveness properties can be negated only by infinite data sequences and thus cannot be covered exhaustively by testing, this case study demonstrates the added value for meeting typical safety requirements in the avionics domain. The results can be transferred from avionics to other domains, as well.

SAM: A Modern System Code for Advanced Non-LWR Safety Analysis

The System Analysis Module (SAM), developed at Argonne National Laboratory and by collaborators at other organizations, is for advanced non–light water reactor safety analysis. SAM aims to provide fast-running, modest-fidelity, whole-plant transient analysis capabilities that are essential for fast-turnaround design scoping and engineering analyses of advanced reactor concepts. To facilitate code development, SAM utilizes the MOOSE object-oriented application framework, its underlying finite element library, and linear and nonlinear solvers to leverage modern advanced software environments and numerical methods. SAM aims to solve tightly coupled physical phenomena, including fission reaction, heat transfer, fluid dynamics, and thermal-mechanical responses in advanced reactor structures, systems, and components with high accuracy and efficiency. This paper gives an overview of the SAM code development, including goals and functional requirements, physical models, current capabilities, verification and validation, software quality assurance, and examples of simulations for advanced nuclear reactor applications.

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.

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.

Evaluation of Information Quality Using ISO/IEC 25010:2011 (Case Research: Menu Harianku Application)

This research aims to determine the product quality of the Menu Harianku application and recommendimproving the quality of the application based on the ISO/IEC 25010:2011 model with 6characteristics in product quality dimensions namely Functional Suitability, Performance Efficiency,Usability, Reliability, Security, and Portability. ISO/IEC 25010:2011 standard was chosen because ofits very suitable characteristics for measuring software quality. Starts with identifying the problem,Design quality, Testing performance quality, and making application improvement recommendations.According to technical characteristics of Functional Suitability, of 97 test cases, there were 11 failed.Performance efficiency of Mobile and Desktop devices scores 86% of the improvement criteria.Reliability Hosting is only accessible in Asian (Japan) and European locations (all) for no more than20 VUs. Security with Medium-level vulnerabilities. The portability of the application runs well on 6Desktop & Mobile browsers. According to users the characteristics of Functional Suitability with apercentage of 84.1%, Performance efficiency at 84%, and Usability at 83.5%. As for the applicationrecommendations related to the results of application quality testing that can be used in improving thequality of the Menu Harianku application.

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.

Improving reuse during the development process for web systems

Software reuse has emerged as a crucial practice in the software industry, offering significant benefits in time-to-market, and resources management. This is particularly pertinent in web systems development, where the integration of diverse technologies and the varied backgrounds of technical teams pose substantial challenges. The rapid expansion of web systems underscores the urgent need to adopt best practices and methodologies for web system reuse to streamline the development process, reducing effort, cost, and time. This paper aims to identify the key challenges of web system reuse in the context of small and medium-sized software companies in Egypt and Saudi Arabia. Using qualitative research methods, including interviews, focus groups, and participant observations, an empirical study was conducted to examine current reuse practices and understand the root causes of common challenges. Based on the results of the empirical study, a systematic approach was developed to enhance web system reuse during the development process in the context of small and medium-sized software companies in Egypt and Saudi Arabia. The proposed approach addresses critical gaps in current practices, offering practical guidelines to improve efficiency, reduce development time, and enhance overall software quality. This research contributes to the broader discourse on software reuse by providing context-specific insights and adaptable solutions that are relevant to similar markets worldwide.

A Data-Driven Approach for Game Evaluation Using Latent Dirichlet Allocation Method Based on Players’ Reviews

Garena is a global game developer and publisher. Garena provides users with access to popular and engaging online games for mobile and PC, developed, curated, and localized for each market. The Battle Royale genre is relatively new, and this research will evaluate Free Fire, one of the games in this genre made by Garena. Analyzing end-user reviews is considered important for evaluating software quality. Researchers need to understand which aspects need to be evaluated based on player reviews on Google Play and how the model's performance is generated using feedback from players who have played Free Fire. In this study, researchers use the Latent Dirichlet Allocation (LDA) method to model topics and generate clusters in discussions for each topic. LDA is a generative probabilistic model of a corpus. This research on topic modeling using Google Play reviews and LDA has identified the topics users are most concerned with. The research shows three main aspects: bugs, graphics and performance, and game rules/punishment policy, as aspects that need to be evaluated based on player reviews on Google Play.

Empirical Analysis of Widely Used Website Automated Testing Tools

In today's software development, achieving product quality while minimising cost and time is critical. Automated testing is crucial to attaining these goals by lowering inspection efforts and discovering faults more effectively. This paper compares widely used automated testing tools, such as Selenium, Appium, Java Unit (JUnit), Test Next Generation (TestNG), Jenkins, Cucumber, LoadRunner, Katalon Studio, Simple Object Access Protocol User Interface (SoapUI), and TestComplete, based on functionality, ease of use, platform compatibility, and integration capabilities. Our findings show that no single tool is inherently superior, with each excelling in certain areas such as online, mobile, Application Programming Interface (API), or performance testing. While Selenium and Appium are the dominant online and mobile testing frameworks, TestComplete and Katalon Studio offer complete, user-friendly cross-platform testing solutions. Despite the benefits of automation, obstacles such as tool maintenance, scalability, and cost issues remain. The report finishes with advice for picking the best tool for the project and offers potential approaches for enhancing testing frameworks, such as AI-driven optimisation, cloud-based testing, and greater Continuous Integration/ Continuous Deployment (CI/CD) integration. This study offers useful information for developers and testers looking to optimise their testing methods and increase software quality.

A Generalized approach to the operationalization of Software Quality Models

Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.

Development of a virtual teaching module for advanced semiconductor fabrication and its learning effectiveness analysis

AbstractSemiconductor fabrication is the process of manufacturing semiconductor devices, typically integrated circuits (ICs) such as microprocessors and memory. This involves transferring circuit diagrams onto a silicon wafer using photomasks and photoresists. After a series of fabrication processes, ICs are created on the wafer surface and then diced into individual chips, which are packaged and tested with quality control procedures to become the final products. Virtual reality (VR) simulates imaginary experiences or environments difficult to achieve in the real world through human senses and immersive equipment, allowing users to interact in a virtual 3D space in real time, making it well suited for applications in science education and industrial training. This study transforms the essential knowledge of advanced semiconductor manufacturing processes into an easily understandable virtual teaching module, thereby creating educational resources for high school and college students. The objective is to enhance their scientific and technological literacy, yielding substantial benefits for the general public. This study utilized VR technology to simplify and clarify the knowledge about the semiconductor manufacturing process, making it more engaging for learners. The virtual teaching module's learning content includes an overview of wafer preparation, semiconductor fabrication, chip packaging, and IC testing. Users can interact with the virtual teaching module and conduct virtual experiments to enhance their understanding by trial and error. Experimental results show that it can improve students' learning achievement and learning motivation. Therefore, the virtual teaching module is suitable for high‐school students and the general public to understand semiconductor technology and its applications. The effectiveness of the virtual teaching module is heavily dependent on the availability and quality of VR hardware and software. Limited access to advanced VR equipment or technical issues could have affected the learning experience, thereby influencing the learning outcomes.

A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (SVM-PSO).

Leukemia, a hematological disease affecting the bone marrow and white blood cells (WBCs), ranks among the top ten causes of mortality worldwide. Delays in decision-making often hinder the timely application of suitable medical treatments. Acute lymphoblastic leukemia (ALL) is one of the primary forms, constituting approximately 25% of childhood cancer cases. However, automated ALL diagnosis is challenging. Recently, machine learning (ML) has emerged as an important tool for building detection models. In this study, we present a hybrid detection model that improves the accuracy of the detection process by combining support vector machine (SVM) and particle swarm optimization (PSO) approaches to automatically identify ALL. We use SVM to represent a two-dimensional image and complete the classification process. PSO is employed to enhance the performance of the SVM model, reducing error rates and enhancing result accuracy. The input images are obtained from two public datasets (ALL-IDB1 and ALL-IDB2), and public online datasets are utilized for training and testing the proposed model. The results indicate that our hybrid SVM-PSO model has high accuracy, outperforming stand-alone algorithms and demonstrating superior performance, an enhanced confusion matrix, and a higher detection rate. This advancement holds promise for enhancing the quality of technical software in the medical field using machine learning.