Software Quality Management 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.

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.

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.

Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration. This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes. Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis. Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis. The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

Investigating the Role of Software Quality Managers and Software Developers in Creating Code Smells

Software code smells are regarded as signs of potential problems in code design or implementation, they might not affect functionality but impact future maintenance and readability. However, refactoring is a process of code transformation that improves code structure without changing code behavior. This process occurs in the stage of software maintenance where software developers and software quality managers/engineers must work closely in order to identify code smells and plan for refactoring. This research aims to investigate the role of quality managers and software developers in creating code smells.

ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)

Software stands out as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity, which leads to challenges in quality assurance. Software defect prediction (SDP) has emerged as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. The methodologies of machine learning (ML) and one of its branches, deep learning (DL), exhibit superior accuracy and adaptability compared to traditional statistical approaches, catalyzing active research in this domain. However, it makes it hard to generalize, not only because of the disparity between open-source projects and commercial projects but also due to the differences in each industrial sector. Consequently, further research utilizing datasets sourced from diverse real-world sectors has become imperative to bolster the applicability of these findings. For this study, we utilized embedded software for use with the telecommunication systems of Samsung Electronics, supplemented by the introduction of nine novel features to train the model, and a subsequent analysis of the results ensued. The experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolstering the application of SDP methodologies within analogous industrial sectors.

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

Using Software Agents to improve functionality of the Power Quality Management Software System

Power quality management is a complex problem that involves rapid changes in knowledge management. That’s why hardware and software systems have to continuously adapt the dynamic requirements of the customer and to process increasing quantities of data. Software Agents could be of help in developing a self adapting dynamic system.

SOFTWARE QUALITY MANAGEMENT

The work examines the state of research and practice in the field of software quality management and gives an assessment of the extent to which existing approaches meet the requirements of new software-intensive systems. The main categories on which software quality management is based, approaches to software quality management are considered. It is shown that in all the studied models, none included the aspect of communication as one of the quality factors. In the field of software quality, a large number of metrics have been proposed for measurement. But these indicators face problems that also arise from the lack of structure in qualitative models. One problem is that, despite the definition of metrics, quality models do not provide a detailed description of the impact of specific indicators on software quality. Nowadays, there is a need for quality components for communications at all levels and especially in complex systems where this becomes a critical factor over the Internet.

Research on Aerospace Software Quality Improvement Based on Software Test Data Analysis

This paper addresses the issue of underutilized software test data in current aerospace software quality management. Through analyzing the current third-party software evaluation process, the paper proposes to optimize software evaluation process and management to enhance software development quality and software test quality. The paper proposes an aerospace software quality improvement framework based on software test data analysis, which provides a reference scheme for aerospace software quality management. Overall, the paper emphasizes the importance of software test data analysis in improving aerospace software quality and provides a comprehensive solution for addressing problems identified in aerospace software quality management.

The Role and Importance of Software Testing in Software Quality Management

Software testing plays a crucial role in software quality management. This paper provides an overview of the basic concepts and principles of software testing, including definitions, principles such as early defect detection, equivalence class division, boundary value testing, and wrong speculation. The classification of software testing is also discussed, covering functional testing, performance testing, safety testing, and compatibility testing. The software testing lifecycle is outlined, including the planning, design, execution, and evaluation stages. Additionally, the paper explores the role of software testing in different phases of the software development process, such as demand, design, encoding, and integration. The specific applications of software testing in quality planning, assessment, and defect management are discussed as well. The future development trends of software testing, including automation, integration with DevOps, and integrity verification, are highlighted. In conclusion, software testing is essential in ensuring software quality, and advancements in automation and integration are expected to shape its future.

Analyzing of Water Quality Management using SPSS Method

In order to maintain water quality, it is typically essential to authorise the release of hazardous compounds, for which surface water waste discharge must be monitored. To make sure that the water is safe to consume for people, animals, and other marine life, it is crucial to monitor water quality. It's crucial for ports to measure water quality in order to comprehend environmental effects and protect marine life. Non-point resource pollution, streams of pollution both direct and indirect, management of watersheds, erosion, and sediments, non-point sources of pollution, and integrated management of water resources,contamination of groundwater and waste water management. Depending on whether or not the water is suitable for drinking or swimming, water quality refers to the state of the water, including its chemical, physical, and biological characteristics. The quality of water can be assessed by taking groundwater samples to undergo lab analysis or by utilising probes that can continuously or intermittently record data. Information on water quality is used by the water sector to manage water resources. Understanding what is happening beneath, where and how quickly water is moving, which geochemical processes are taking place, and recognising distinct sources of water are all made easier by the quality of the water. The necessity to upgrade civilization's plumbing has an impact on water quality as well. One of the natural resources that is present in appropriate amounts is water. It is a crucial component of life on Earth. It is frequently used for drinking, cleaning, cooking, bathing, irrigation, and other residential and commercial purposes. For water utilities dealing with rising energy costs, a lack of water supplies, and strict regulatory requirements, water as well as energy resources are crucial.Early in the 1990s, the idea of an interwoven Water and Energy Quality Management Software (EWQMS) was established as a framework for operational optimization for simultaneously addressing issues with water quality, water supply, and energy management. In order to build an EWQMS, about twenty water utilities interfaced existing control systems with external or internal software optimization packages. Due to increased utilisation of cheaper tariff times and improved operating efficiencies, utilities with an installed EWQMS have reported operating cost savings of 8e15%, which has resulted in a decrease in energy consumption of 6e9%. This paper presents the current state of understanding on the usual structural features and operational tactics of EWQMS, and both advantages and disadvantages are considered. The analysis also identifies the gaping holes that should spur new research initiatives and outlines the difficulties faced during the installation and deployment of EWQMS. Ratio studies are statistical analyses of data from appraisals and property valuations. Nearly all states utilize them to produce quantitative measure of the proportion of current market price about which individually estimated taxable property is appraised as well as to offer assessment performance indicators. Energy and water quality management, system, Water utilities, Energy efficiency, Water quality and Demand forecasting. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Characteristics of sisal fiber the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

Software Methodology for Data Control and Collection for Autonomous Monitoring Systems with a Large Amount of Generated Information on the Example of Software for a Hydrological Radiolocation System

Introduction . The current data monitoring and collection systems produce a growing amount of generated information. Such factors, as the increasing sampling rate of ADCs and the increasing speed of systems for primary processing, receiving, and transmitting information, etc., make the systems operate almost at the bandwidth limit of data transmission interfaces. In some applications, such a flow is redundant and can be optimized through the use of various algorithms for the primary processing of information. However, in some applications, reducing the data flow is impossible, since the received information is processed with a delay. Therefore, the development of a software methodology for controlling and collecting data in the system of automatic monitoring of the sea surface by a hydrological radiolocation system seems a relevant research task. Aim . To synthesize a methodology and to develop software for controlling the information system of radar monitoring of the sea surface. Materials and methods . System approach, software architectural and algorithmic design, software quality management methods, system analysis, Qt framework, C++ programming language. Results . A working methodology for designing software for controlling information and measurement technologies with a large amount of generated data was obtained. The effectiveness of the methodology and software quality were confirmed by control tests. A 3-month autonomous testing of the stability and reliability of the system was carried out. Detection of data loss in the system comprised less than 0.002 %, thus not exceeding the specified critical level of 0.5 %. Conclusion . The developed methodology can be used in designing software for controlling information and measuring systems generating a large amount of data. The approaches used to build a multithread software architecture with asynchronous data flow control has shown their high efficiency.

On measuring coupling between microservices

In software quality management, the selection strategy for proper metrics varies depending on the application scenarios and measurement objectives. MicroService Architecture (MSA), despite being commonly employed nowadays, still cannot be reliably measured and compared if the microservices in a system are independent. Software managers and architects need to understand whether their microservices are “decoupled enough”, if not, which ones are over-coupled, and by how much. In this paper, we contribute a novel set of metrics – Microservice Coupling Index (MCI) – derived from the relative measurement theory. Instead of measuring coupling evidence with simple counts, we measure how dependent and coupled the microservices are relative to the possible couplings between them. We measured the MCI metrics for 15 open source projects that involve 113 distinct microservices. Empirical investigation confirmed that MCIs differ quite significantly from existing coupling measures and that they are more discriminative than existing ones for separating high and low degrees of microservice couplings and thus more useful in comparing design alternatives. A series of experimental studies were conducted, showing that the larger the MCIs, the less likely the bugs and changes can be localized and separated, and the less likely that the individual microservices in a system can be independently developed and evolved.

Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

Analyzing Current Approaches to Assessing the Quality of Software Products

Information technologies are the fastest developing industry today both in global economy and economy of Russia. Serious attention is paid to methodological aspects of quality management. Raising quality of software products is an objective of implementing the national project ‘Digital Economy of the Russian Federation’. Processes and stages of software development are modified with regard to new requirements to end product in accordance with adopted home standards. Thus the goal of improving the quality of software products is really topical. The quality of software product is influenced by various determinate and casual external and internal factors. Home experience of managing software quality refers to both western experience and works by Russian scientists, such as G. G. Azgal’dov, Yu. P. Adler, I. G. Venetskiy, V. V. Lipaev, S. D. Il’enkova, I. F. Shishkin, P. L. Chenyshev, V. M. Mishin. The article studies methods of managing software product quality that are used in home IT companies and carries out their comparative analysis. The author formulated proposals aiming at identification of drawbacks by using methods of cognitive modeling.

Assignment of Motor Mechanics at the Tire Palace Using the Hungarian Method and Testing Software Quality Management (QM)

Measurement of working time (time study) is basically an attempt to determine the placement of mechanical work from the length of work time required by a mechanic to complete a job. This study aims to place the optimal mechanical work in the context of service operational time efficiency and obtain optimal time savings that can be achieved by mechanics in working on motorcycle servicing so as to maximize income using the Hungarian Method. The purpose of this research is to optimize employee assignments by looking at the mechanical working time. The problems that occur at ISTANA BAN are the ineffectiveness of the work process time and the swelling of operational costs, especially in the work of Matic Kaburator, Injection Matic, Injection Duck, Kaburator Duck and Sport with 5 workers. The results of the Assignment Application with the Hungarian Method and Software Quality Management (QM) Testing, Evan mechanics serviced the Duck Kaburator motorbike with a service time of 25 minutes, Minus mechanic serviced the Injection Duck motorbike with a service time of 24 minutes, Hermon mechanic serviced the Sport motorbike with a service time of 27 minutes, mechanic Anton servicing the Matic Injection motorbike with a service time of 25 minutes, mechanic Zola servicing the Matic Kaburator motorbike with a service time of 26 minutes. After analyzing the possibilities, it can be concluded that all mechanics can service each type of motor if the assigned mechanic has worked on the specified type of motor. By minimizing mechanical service time, it will have an impact on Tire Zone revenue because the number of motorbikes being serviced is increasing. Total Tire Zone revenue can be increased from pre-implementation revenue using the Hungarian Method.

Application of the Hungarian Method and Software Quality Management (QM) Testing in Determining Optimal Wage Costs at OneTop Frozen Food Stores

Linear programming that can be found in life around is an assignment problem. Common assignment problems include n tasks that must be assigned to m workers assuming each worker has different competencies in completing each task. One of the methods in solving this problem is the Hungarian method. The purpose of this research is to optimize the assignment of employees by looking at the cost of daily wages. The problems that occur at the OneTop Frozen Store are the ineffectiveness of the work process and the swelling of operational costs, especially at work, Order Sorting, Packing, Labeling, Delivery with 4 workers. The application of the Hungarian method and testing of Software Quality Management obtains optimal wage costs so that operational costs can be reduced to a minimum without reducing the quality of service to consumers. Based on data processing in the Hungarian method, an assignment that is in accordance with the work in preparing Frozen Food orders at the OneTop Store can cost a daily wage of Rp.130,000 (one hundred and thirty thousand) to be the optimal wage cost per day. when compared with the previous calculation without using the Hungarian method by paying wages of Rp. 175,000 (one hundred and seventy five) per day. Based on the application of the Hungarian method, it is effective in determining an assignment and placement of workers so that they can work more effectively on a better and optimal Frozen Food ordering process.

579 Outcomes of an Independent Burn Advanced Practice Provider Peer Review Process

IntroductionBurn peer review (PR) is an important process for quality assurance and process improvement. Our division consists of five burn surgeons, seven advanced practice providers (APPs) and four night house officer (NHO) APPs. As the APPs are intimately integrated within the burn service - both on the inpatient (IP) and outpatient (OP) settings, a subsequent need to create an independent peer review process for the APPs was identified. We describe our process and evaluate the results after two years of a monthly APP PR meeting.MethodsAll data is entered into a quality management software. Triggers for APP PR include complex discharge, inappropriate OP referral, delay in treatment, OP treatment greater than three weeks without surgical intervention, pressure wound, patient discharge with greater than 30 morphine equivalent dose (MED) of narcotic, readmission or unexpected admission from OP clinic, substance abuse, and self referral. Cases with individual improvement opportunities result in real time direct feedback from the director, lead APP, and peers. These may be escalated to the provider’s ongoing professional performance evaluation (OPPE) or trigger a focused professional performance evaluation (FPPE). Systemic issues identified are referred for multidisciplinary discussion in our Performance Improvement/Quality Assurance (PI/QA) process. Complicated issues involving multiple disciplines may trigger a root cause analysis. If an individual or system issue is not identified, a group discussion about process improvement and standardization of practice ensues.ResultsFrom initiation of the Burn APP PR process in May of 2019, to September 2021, there were 37 cases reviewed. Twelve were for indicated triggers and 25 for self-referrals. Breakdown of these cases is as follows: outpatient treatment greater than three weeks without surgical intervention (3), readmission or unexpected admission from outpatient clinic (4), delay in treatment impacting clinic course (5). Self referred cases included graft loss (8), cellulitis (3), venous thromboembolic event (2), and wound dressing discussion (2). There were three cases where clinical issues were identified of which one was referred for root cause analysis, and the other two led to real-time education and feedback from the director.ConclusionsPR is important to ensure that high quality patient care standards are met. Autonomous APP practice is important for the function of a team, quality of care delivered, professional satisfaction of the APP, and enables financial optimization for burn centers. Creating an independent burn APP peer review process allows for time-sensitive feedback to patient care related events, improves collaboration and practice standardization, and provides an important educational opportunity for the team.

Application of PDCA Cycle in Software Quality Management

Application of PDCA Cycle in Software Quality Management

Integrated Automotive Software Quality Management System in compliance with Automotive SPICE, ISO 26262, ISO 21448 and ISO 21434 Standards

Integrated Automotive Software Quality Management System in compliance with Automotive SPICE, ISO 26262, ISO 21448 and ISO 21434 Standards