Software Changes Research Articles

Research on the Application of Computer Aided Design Software SU in Landscape Design

SketchUp has simplified, three-dimensional and intuitive effects on landscape design optimization. The software changes from traditional manual drawing to computer-aided drawing, which promotes the improvement of landscape design and BIM modeling and analysis level. SketchUp enables real-time communication with customers. This paper is based on two-dimensional data accurately drawn by AutoCAD and uses the SketchUp ESRI plug-in between GIS and SketchUp to convert shape files into SketchUp recognizable. Then, Google SketchUp intelligent software was used to rapidly batch process GIS data. Finally, the 3D model is output into Multipitch (*.mdb) format data. The software can realize roaming, query, data management and spatial analysis, and achieve the purpose of rapid realization of forest landscape visualization. This method can batch process the 3D modeling data of large areas and has important reference value for landscape visualization and landscape planning.

Ramping down a clinical 3 T scanner: a journey into MRI and MRS at 0.75 T

ObjectLower-field MR is reemerging as a viable, potentially cost-effective alternative to high-field MR, thanks to advances in hardware, sequence design, and reconstruction over the past decades. Evaluation of lower field strengths, however, is limited by the availability of lower-field systems on the market and their considerable procurement costs. In this work, we demonstrate a low-cost, temporary alternative to purchasing a dedicated lower-field MR system.Materials and MethodsBy ramping down an existing clinical 3 T MRI system to 0.75 T, proton signals can be acquired using repurposed 13C transmit/receive hardware and the multi-nuclei spectrometer interface. We describe the ramp-down procedure and necessary software and hardware changes to the system.ResultsApart from presenting system characterization results, we show in vivo examples of cardiac cine imaging, abdominal two- and three-point Dixon-type water/fat separation, water/fat-separated MR Fingerprinting, and point-resolved spectroscopy. In addition, the ramp-down approach allows unique comparisons of, e.g., gradient fidelity of the same MR system operated at different field strengths using the same receive chain, gradient coils, and amplifiers.DiscussionRamping down an existing MR system may be seen as a viable alternative for lower-field MR research in groups that already own multi-nuclei hardware and can also serve as a testing platform for custom-made multi-nuclei transmit/receive coils.

P09 Attitudes, barriers and difficulties to discharge – a qualitative approach

Abstract Introduction It is well accepted that prompt and effective discharges are crucial in order to sustain patient continuity of care, patient flow and create bed space for patients. Systematic difficulties and the health professional staffing crisis may create further barriers to satisfying the above statement. Thus, the authors have investigated the titled characteristics with a view to instigating beneficial changes. Methods A curated questionnaire was designed and emailed to all health care professionals who worked within the general surgical department at the authors’ trust who completed discharge summaries. A total of 26 respondents completed the survey. Results The main obstacle in completing discharges was lack of patient knowledge (40% of respondents) followed by time pressure (28% of respondents). In addition, each discharge usually took 11 minutes or more. Discharge priority is deemed to be of variable importance, with 52% ranking it as moderate priority and 24% deeming it to be high priority. Multiple symbiotic factors were thought to be important in improving the discharge process. However, the single key factor was dedicated protected time to complete discharge paperwork (17.39% of respondents). 60% of respondents ideally favoured a typed discharge summary with 28% preferring dictation despite a lack of knowledge and understanding of the available resources. Conclusion This work has offered a valuable insight into the attitudes, barriers and difficulties surrounding discharges with scope to instigate improvements, such as the use of dictation software, protected time and subsequent possible policy changes.

Software medical device maintenance: DevOps based approach for problem and modification management

AbstractSoftware problem resolution and change control processes are crucial for the performance, safety, and regulatory clearance of a medical device. Software changes are frequent throughout the whole life cycle of a product; therefore, iterative analysis and risk assessment are necessary to determine the impact of changes on the whole system. IEC standard 62304 clarifies software medical device life cycle processes including problem and modification analysis and requirements. The aim of this work is to describe a cost‐effective and fully customizable solution for software medical devices problem and modification management implemented in our group. Key features of the problem and modification management related to the software life cycle have been identified and taken into account. A digital commercial platform for software development and maintenance has been identified and used to implement an architecture satisfying the standard. The architecture solution (SWMA, software maintenance architecture) has been successfully developed and a description of how it addresses IEC 62304 standard requirements both in terms of problem resolution and in terms of change control has been provided. The presented system can be useful for manufacturers/groups to establish software maintenance plans that include activities and tasks related to software problem resolution and change control processes. The approach can be adopted for analyzing and resolving problems arising before and after the release of a medical device.

Robust Grid-Forming Control With Active Susceptance

This letter proposes a grid-forming control scheme that is robust against a wide range of short-circuit ratio (SCR) of the ac grid. The approach modifies the traditional power synchronization control by adding an active susceptance loop, which enhances system damping for high stability robustness and enables to use a standard voltage-oriented vector current control. The latter can avoid major software changes on a classical grid-following control structure. Both small-signal dynamics analysis and experimental results confirm the effectiveness of the approach from ultraweak (SCR = 1, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ratio = 7) to stiff (SCR = 38.5) grids.

Demonstration of Near-Field Capacitive Standard Communication Bus for Ultrathin Reconfigurable Sensor Nodes

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">We developed a near field capacitive standard communication bus in order to enable reconfiguration of flexible electronics systems. Through minimum software and hardware changes, it is possible to easily connect and disconnect any application layer to a core layer. The capacitive bus transmission was designed through physical and electrical modeling, to result in a 42 pF coupling needed. This study allowed setting up a demonstrator of Copper-ParyleneC ultrathin printed circuit board. Zero errors over a thousand SPI packet transmissions were demonstrated using 3 millimeters pads associated to specifically developed PDMS-glycerol dielectrics</i> .

Deeper Understanding of Software Change

Managing change is a challenging task in today’s complex software engineering. Understanding the diversity of changes and their relationship to current technologies is critical for dealing with volatile business systems. This article aims to identify and assess the state of the art toward understanding software change for the sake of providing a deeper understanding of its causes, mechanisms, and effects.

Assessing the exposure of software changes

Assessing the exposure of software changes

Translating principles of quality control to cardiovascular magnetic resonance: assessing quantitative parameters of the left ventricle in a large cohort

Cardiac magnetic resonance (CMR) examinations require standardization to achieve reproducible results. Therefore, quality control as known as in other industries such as in-vitro diagnostics, could be of essential value. One such method is the statistical detection of long-time drifts of clinically relevant measurements. Starting in 2010, reports from all CMR examinations of a high-volume center were stored in a hospital information system. Quantitative parameters of the left ventricle were analyzed over time with moving averages of different window sizes. Influencing factors on the acquisition and on the downstream analysis were captured. 26,902 patient examinations were exported from the clinical information system. The moving median was compared to predefined tolerance ranges, which revealed an overall of 50 potential quality relevant changes (“alerts”) in SV, EDV and LVM. Potential causes such as change of staff, scanner relocation and software changes were found not to be causal of the alerts. No other influencing factors were identified retrospectively. Statistical quality assurance systems based on moving average control charts may provide an important step towards reliability of quantitative CMR. A prospective evaluation is needed for the effective root cause analysis of quality relevant alerts.

An Evaluation of Ranking-to-Learn Approaches for Test Case Prioritization in Continuous Integration

Continuous Integration (CI) environments is a practice adopted by most organizations that allows frequent integration of software changes, making software evolution more rapid and cost-effective. Such environments require dynamic Test Case Prioritization (TCP) approaches that adapt better to the test budgets and frequent addition/removal of test cases. In this sense, Ranking-to-Learn approaches have been proposed and are more suitable for CI constraints. By observing past prioritizations and guided by reward functions, they learn the best prioritization for a given commit. In order to contribute for improvements and direct future research, this work evaluates how far the solutions produced by these approaches are from optimal solutions produced by a deterministic approach (ground truth). To this end, we consider two learning-based approaches i) RETECS, which is based on Reinforcement Learning; and ii) COLEMAN, an approach based on Multi-Armed Bandit. The evaluation was conducted with twelve systems, three test budgets, two reward functions, and six measures concerning fault detection effectiveness, early fault detection, test time reduction in the CI cycles, prioritization time, and accuracy. Our findings have some implications for the approaches application and reward function choice. The approaches are applicable in real scenarios and produce solutions very close to the optimal ones, respectively, in 92% and 75% of the cases. Both approaches have some limitations to learn with few historical test data (a small number of CI Cycles) and deal with a large test case set, in which many failures are distributed over many test cases.

A Systematic Survey of Just-in-Time Software Defect Prediction

Recent years have experienced sustained focus in research on software defect prediction that aims to predict the likelihood of software defects. Moreover, with the increased interest in continuous deployment, a variant of software defect prediction called Just-in-Time Software Defect Prediction (JIT-SDP) focuses on predicting whether each incremental software change is defective. JIT-SDP is unique in that it consists of two interconnected data streams, one consisting of the arrivals of software changes stemming from design and implementation, and the other the (defective or clean) labels of software changes resulting from quality assurance processes. We present a systematic survey of 67 JIT-SDP studies with the objective to help researchers advance the state of the art in JIT-SDP and to help practitioners become familiar with recent progress. We summarize best practices in each phase of the JIT-SDP workflow, carry out a meta-analysis of prior studies, and suggest future research directions. Our meta-analysis of JIT-SDP studies indicates, among other findings, that the predictive performance correlates with change defect ratio, suggesting that JIT-SDP is most performant in projects that experience relatively high defect ratios. Future research directions for JIT-SDP include situating each technique into its application domain, reliability-aware JIT-SDP, and user-centered JIT-SDP.

A Procedure to Continuously Evaluate Predictive Performance of Just-In-Time Software Defect Prediction Models During Software Development

Just-In-Time Software Defect Prediction (JIT-SDP) uses machine learning to predict whether software changes are defect-inducing or clean. When adopting JIT-SDP, changes in the underlying defect generating process may significantly affect the predictive performance of JIT-SDP models over time. Therefore, being able to continuously track the predictive performance of JIT-SDP models during the software development process is of utmost importance for software companies to decide whether or not to trust the predictions provided by such models over time. However, there has been little discussion on how to continuously evaluate predictive performance in practice, and such evaluation is not straightforward. In particular, labeled software changes that can be used for evaluation arrive over time with a delay, which in part corresponds to the time we have to wait to label software changes as ‘clean’ (waiting time). A clean label assigned based on a given waiting time may not correspond to the true label of the software changes. This can potentially hinder the validity of any continuous predictive performance evaluation procedure for JIT-SDP models. This paper provides the first discussion of how to continuously evaluate predictive performance of JIT-SDP models over time during the software development process, and the first investigation of whether and to what extent waiting time affects the validity of such continuous performance evaluation procedure in JIT-SDP. Based on 13 GitHub projects, we found that waiting time had a significant impact on the validity. Though typically small, the differences in estimated predicted performance were sometimes large, and thus inappropriate choices of waiting time can lead to misleading estimations of predictive performance over time. Such impact did not normally change the ranking between JIT-SDP models, and thus conclusions in terms of which JIT-SDP model performs better are likely reliable independent of the choice of waiting time, especially when considered across projects.

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.

Complexity estimation service for change management in industrial automation systems using Digital Twin

Industrial automation systems are becoming increasingly complex. This complexity is caused by the higher proportion of software, networking, and cyber capability. Modern software-defined industrial automation systems provide higher flexibility and adaptivity against market uncertainty in dynamic and heterogeneous environmental conditions in industrial automation. Consequently, different conventional assets need changes during their life cycle to be adaptable. There are different ways to implement the required changes to reach the previously defined requirements such as systems’ software changes which have easier implementation. These options cause different levels of complexity that require a different amount of resources mainly time and cost. Achievement of the new requirements concurrently induces a large amount of complexity. The omnipresent complexity gives rise to many consequences on different abstraction levels in software-defined systems. In order to manage these software-defined systems properly, an automatic method for estimating this complexity due to the importance of a proper selection among different possible modifications is required. This paper proposes a novel concept for a complexity estimation service before applying real changes in the systems. The presented method enables the simulation of required changes using the Intelligent Digital Twin concept to decrease the cost of real changing and implementation of the software and their unknown consequences besides acquiring the future requirements of asset management. By automatic complexity drivers’ detection, this service simulates the new asset and predicts how it would be complex to apply the required changes by proper complexity metrics. To validate this service, an assistance system for a model car is implemented to show the complexity of software changes for adding new functionalities to the car.

Cross-Project Online Just-In-Time Software Defect Prediction

Cross-Project (CP) Just-In-Time Software Defect Prediction (JIT-SDP) makes use of CP data to overcome the lack of data necessary to train well performing JIT-SDP classifiers at the beginning of software projects. However, such approaches have never been investigated in realistic online learning scenarios, where Within-Project (WP) software changes naturally arrive over time and can be used to automatically update the classifiers. We provide the first investigation of when and to what extent CP data are useful for JIT-SDP in such realistic scenarios. For that, we propose three different online CP JIT-SDP approaches that can be updated with incoming CP and WP training examples over time. We also collect data on 9 proprietary software projects and use 10 open source software projects to analyse these approaches. We find that training classifiers with incoming CP+WP data can lead to absolute improvements in G-mean of up to 53.89% and up to 35.02% at the initial stage of the projects compared to classifiers using WP-only and CP-only data, respectively. Using CP+WP data was also shown to be beneficial after a large number of WP data were received. Using CP data to supplement WP data helped the classifiers to reduce or prevent large drops in predictive performance that may occur over time, leading to absolute G-Mean improvements of up to 37.35% and 48.16% compared to WP-only and CP-only data during such periods, respectively. During periods of stable predictive performance, absolute improvements were of up to 29.03% and up to 41.25% compared to WP-only and CP-only classifiers, respectively. Our results highlight the importance of using both CP and WP data together in realistic online JIT-SDP scenarios.

Radiotherapy teaching during COVID-19: An emergency teaching response

Background: The coronavirus disease 2019 (COVID-19) pandemic resulted in more than six million deaths in the first two years, a third of the estimated number of cancer-related deaths during this time. It directly impacted radiotherapy training in Africa.Aim: This study evaluated the changes applied to the Access to Care Cape Town Radiotherapy training programme during the pandemic.Setting: The training platform prior to March 2020 was used as a baseline and compared with the programme status in January 2022, representing the emergency teaching model.Methods: Five themes were investigated: computer hardware and software changes; e-Learning resources; programme and curriculum changes; challenges experienced and alignment with modern medical education principles.Results: Reconfiguration of the computer laboratories was required, including additional computer monitors, web cameras and headsets, as well as installation of screen recording and teleconferencing software. The EclipseTM radiotherapy treatment planning laboratory was reconfigured for remote student access, with simultaneous monitoring by local assistants. Online learning was augmented by adding the University of Cape Town VulaTM system as resource, and courses restructured for delivery of short blocks. Five new courses were developed, including collaborations with international training partners, showing good alignment with the principles of modern medical education.Conclusion: Reconfiguration was performed at a manageable cost but required a high level of information technology support. Connectivity and bandwidth issues remain a challenge, as well as online engagement.Contribution: Despite these challenges, the virtualisation allowed for continued training between March 2020 and December 2021, with 18 departments attending remote teaching courses.

Getting a Grip on the Ever‐Changing Software in Cyber‐Physical Systems

ABSTRACTAs industrial cyber‐physical systems grow ever more complex, their software grows naturally and changes continuously. In order to make risk‐free changes to their software, it is crucial to understand how the system behaves, and how software changes have an impact on system behavior. We propose a generic two‐fold approach to infer state machine models capturing system behavior, and to compare these models to determine and visualize the impact of software changes on system behavior, in a way to make them easily understandable for engineers. Our approach has been applied in the industry at ASML to help prevent software regression problems during critical software redesigns. In that, our approach has been shown to reduce risk and to be valuable.

Geospatial Cognitive Networking Protocols and Sensing Algorithms for 5G NR Beamforming

Although 5G New Radio (NR) has created new opportunities for cognitive radio networks, its increased physical layer security and flexibility limit the usefulness of traditional cognitive detectors such as energy and blind control channel algorithms. This paper presents CASINO-NR, a novel framework for establishing a cognitive self-reliant secondary network with no additional physical infrastructure, collaboration from the primary network nodes, and software or hardware changes to the existing 5G network. CASINO-NR includes a novel beam detection algorithm that finds and ranks 5G NR synchronization signals to determine geospatially non-interfering beams for secondary communications. We compare the developed beam detector with multiple existing approaches for sensitivity to interference and phase distortions. We also apply power control to prevent interference on neighboring beams. CASINO-NR is analyzed against the estimated throughput capacity and capabilities of other cognitive detectors found in literature. Finally, we examine an experimental beamforming example to demonstrate our beam detection algorithm and present a case for geospatial resources for cognitive radio communications.

The CSFs from the Perspective of Users in Achieving ERP System Implementation and Post-Implementation Success: A Case of Saudi Arabian Food Industry

Enterprise resource planning (ERP) systems have a major impact on the functioning of organizations and the development of business strategy. However, one of the main reasons that cause failure in ERP implementations to achieve the expected benefits is that the system is not fully accepted by end users. User rejection of the system is the second reason after time and budget overrun, while the fourth barrier to ERP post-implementation. Most studies have focused on ERP adoption and installation while neglecting post-implementation evaluation, which omits insights into the priority of ERP systems and CSFs from the stance of ERP users. Therefore, this study identified factors that led to user acceptance of the use of ERP systems at both implementation and post-implementation stages (after installation). In addition, this study assessed the interrelationship between the factors and the most influential factors toward user acceptance. A survey was conducted among pioneers of the food industry in Saudi Arabia, which included 144 ERP system users from assembly and manufacturing, accounts, human resources, warehouse, and sales departments. The descriptive-analytical approach was deployed in this study. As a result, project management, top management support, and user training had significant impacts on the efficacy of ERP system implementation. On the contrary, support for technological changes in new software and hardware, managing changes in systems, procedures, and work steps already in place within the organization, as well as user interfaces and custom code, displayed a direct impact on user acceptance of ERP systems post-implementation. This study is the first research that provides a rating of CSFs from the perspective of its users in Saudi Arabia. It also enables decision makers of food industries to better assess the project risks, implement risk-mitigation methods, create appropriate intervention techniques to discover the strengths and limitations of the ERP users, and value the “best of fit” solutions over “best practice” solutions when determining the most appropriate option for food industries.

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications

BackgroundUnexpected variability across healthcare datasets may indicate data quality issues and thereby affect the credibility of these data for reutilization. No gold-standard reference dataset or methods for variability assessment are usually available for these datasets. In this study, we aim to describe the process of discovering data quality implications by applying a set of methods for assessing variability between sources and over time in a large hospital database. MethodsWe described and applied a set of multisource and temporal variability assessment methods in a large Portuguese hospitalization database, in which variation in condition-specific hospitalization ratios derived from clinically coded data were assessed between hospitals (sources) and over time. We identified condition-specific admissions using the Clinical Classification Software (CCS), developed by the Agency of Health Care Research and Quality. A Statistical Process Control (SPC) approach based on funnel plots of condition-specific standardized hospitalization ratios (SHR) was used to assess multisource variability, whereas temporal heat maps and Information-Geometric Temporal (IGT) plots were used to assess temporal variability by displaying temporal abrupt changes in data distributions. Results were presented for the 15 most common inpatient conditions (CCS) in Portugal. Main findingsFunnel plot assessment allowed the detection of several outlying hospitals whose SHRs were much lower or higher than expected. Adjusting SHR for hospital characteristics, beyond age and sex, considerably affected the degree of multisource variability for most diseases. Overall, probability distributions changed over time for most diseases, although heterogeneously. Abrupt temporal changes in data distributions for acute myocardial infarction and congestive heart failure coincided with the periods comprising the transition to the International Classification of Diseases, 10th revision, Clinical Modification, whereas changes in the Diagnosis-Related Groups software seem to have driven changes in data distributions for both acute myocardial infarction and liveborn admissions. The analysis of heat maps also allowed the detection of several discontinuities at hospital level over time, in some cases also coinciding with the aforementioned factors. ConclusionsThis paper described the successful application of a set of reproducible, generalizable and systematic methods for variability assessment, including visualization tools that can be useful for detecting abnormal patterns in healthcare data, also addressing some limitations of common approaches. The presented method for multisource variability assessment is based on SPC, which is an advantage considering the lack of gold standard for such process. Properly controlling for hospital characteristics and differences in case-mix for estimating SHR is critical for isolating data quality-related variability among data sources. The use of IGT plots provides an advantage over common methods for temporal variability assessment due its suitability for multitype and multimodal data, which are common characteristics of healthcare data. The novelty of this work is the use of a set of methods to discover new data quality insights in healthcare data.