Software Changes Research Articles

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.

CASPHAr: Cache-Managed Accelerator Staging and Pipelining in Heterogeneous System Architectures

Integrating accelerators onto the same chip with CPUs sharing the last level cache (LLC) is beneficial when CPUs and accelerators frequently exchange data. However, if shared data exceeds LLC capacity, expensive spills to and refetches from DRAM will be incurred, limiting the benefits of such integrated architectures. While this can be avoided through careful software optimizations, such as fine-grain data tiling and accelerator synchronization, this involves significant software changes and programmer effort. In this article, we introduce CASPHAr, an LLC architecture that performs automatic, software- and hardware-transparent fine-grain data staging and synchronization between CPUs and accelerators in hardware. CASPHAr tracks and synchronizes producer and consumer accesses at cache line granularity. As soon as some fraction of shared data is produced and becomes ready in the LLC, the data will be delivered for processing in the waiting consumer. Furthermore, CASPHAr extends existing replacement policies to leverage synchronization information for making better eviction decisions. All combined, CASPHAr reduces data spills due to unnecessarily long lifetimes of shared data in the cache. In addition, fine-grain staging and synchronization inherently achieves system-level pipelining of interdependent kernels that can outperform software optimizations. Results show that CASPHAr can boost performance by up to 23% and achieve energy savings of up to 22% over baseline accelerations.

Grid-Forming Vector Current Control

In this article, a novel point of view on grid-forming control with current controlled power electronic converters is presented. A control concept based on the emulation of the combined effect of a virtual synchronous condenser and a parallel-connected virtual current source is proposed. It combines the positive effects of virtual synchronous generator control, such as the provision of inertia, voltage support, the possibility of islanded operation, and good control performance in weak grids, with the benefits of vector current control, such as fast current limitation, improved synchronization properties, and fast setpoint tracking. The proposed control concept is simply a modification of standard vector current control. Therefore, it can be implemented easily on all control platforms with minor software changes. The control method is verified with analytical models, time-domain simulations, and experimental results.

Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

Abstract Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection. Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, we used GitHub Actions continuous integration (CI) to automate data QA/QC and run routine data wrangling scripts to produce cleaned datasets ready for analysis. This system automation had numerous benefits, including (1) the production of near real‐time information on data collection status and errors requiring correction, resulting in final datasets free of detectable errors, (2) an apparent learning effect among field technicians, wherein original error rates in field data collection declined significantly following implementation of the system, and (3) an assurance of computational reproducibility—that is, robustness of the system to changes in code, data and software. By implementing CI, researchers can ensure that datasets are free of any errors for which a test can be coded. The result is dramatically improved data quality, increased skill among field technicians, and reduced need for expert oversight. Furthermore, we view CI implementation as a first step towards a data collection and analysis pipeline that is also more responsive to rapidly changing ecological dynamics, making it better suited to study ecological systems in the current era of rapid environmental change.

946 Transforming the Morbidity and Mortality Review Process: Customised Digitalisation Helps Us Learn from Mistakes

Abstract Aim Morbidity and Mortality (M&M) review is an important component of clinical governance in surgical practice. Constructive discussions are crucial for improving patient outcomes and quality of care. We aimed to transform the M&M review process within the paediatric surgical department of our tertiary referral centre. Method A critical assessment of our previous system identified weaknesses which we addressed by sequential changes in software and meeting organisation. Clinical staff developed a customised web-based reporting platform, followed by a more sophisticated mobile application for smart phones/tablets, allowing users to explore all reported cases and discussions, and to monitor actions from previous cases. The introduction of triage meetings structured the agenda and content for M&M meetings and monitoring of agreed recommendations/proposed actions ensured service improvement. Results The introduction of the reporting platform increased case reporting (11 vs 21 cases/month). The subsequent development of our mobile application standardised the recording of case discussion with contributing factors recorded for 100% of cases discussed. Recommendations are logged and tracked within the app's dashboard which shows every report coded by key words so case series can be identified, and audits easily facilitated. Conclusions M&M meetings are crucial in improving patient safety and quality of care and are pivotal to surgical training. Our mobile application simplifies, and encourages, case reporting and provides a platform to facilitate structured M&M conferences. This project demonstrates innovative uses of technology can facilitate quality improvement from M&M meetings, support audits and enhance education of surgical trainees.

Optimizing low contrast volume thoracic CT angiography: From the basics to the advanced.

Contrast-enhanced CT angiography (CTA) is a widely used, noninvasive imaging technique for evaluating cardiovascular structures. Contrast-induced nephrotoxicity is a concern in renal disease; however, the true nephrotoxic potential of iodinated contrast media (CM) is unknown. If a renal impaired patient requires CTA, it is important to protect the kidneys from further harm by reducing total iodinated CM volume while still obtaining diagnostic quality imaging. These same reduced volume CM techniques can also be applied to nonrenal impaired patients in times of CM shortage. This educational review discusses several modifications to CTA that can be adapted to both conventional 64-slice and the newer generation CT scanners which enable subsecond acquisition with a reduced CM volume technique. Such modifications include hardware and software adjustments and changes to both the volume and flow rate of administered CM, with the goal to reduce the dose of CM without compromising diagnostic yield.

An empirical study of data constraint implementations in Java

Software systems are designed according to guidelines and constraints defined by business rules. Some of these constraints define the allowable or required values for data handled by the systems. These data constraints usually originate from the problem domain (e.g., regulations), and developers must write code that enforces them. Understanding how data constraints are implemented is essential for testing, debugging, and software change. Unfortunately, there are no widely-accepted guidelines or best practices on how to implement data constraints. This paper presents an empirical study that investigates how data constraints are implemented in Java. We study the implementation of 187 data constraints extracted from the documentation of eight real-world Java software systems. First, we perform a qualitative analysis of the textual description of data constraints and identify four data constraint types. Second, we manually identify the implementations of these data constraints and reveal that they can be grouped into 31 implementation patterns. The analysis of these implementation patterns indicates that developers prefer a handful of patterns when implementing data constraints. We also found evidence suggesting that deviations from these patterns are associated with unusual implementation decisions or code smells. Third, we develop a tool-assisted protocol that allows us to identify 256 additional trace links for the data constraints implemented using the 13 most common patterns. We find that almost half of these data constraints have multiple enforcing statements, which are code clones of different types. Finally, a study with 16 professional developers indicates that the patterns we describe can be easily and accurately recognized in Java code.

Design and Testing of a Multimode Capable Passive Battery Management System

A customized passive battery management system (BMS), which offers a selection of different operating configurations regarding the connection of external sources and loads, has been developed. The device supports balance, charge, de-balance, discharge and permanent storage battery processes. The control unit is run by its own written algorithm (code). Suggestions for potential hardware and software changes that can be made to expand the capabilities of the device are listed. The device is tested in five different operating configurations and the output data (battery-cell voltages and balancing currents) are plotted in characteristic diagrams. The output data is analyzed and the unique capabilities of the device are explained. The detailed PCB design, code, and output measurement data files are included within the paper.

Changes in software as a medical device based on artificial intelligence technologies.

Purposeto develop a procedure for registering changes, notifying users about changes made, unifying software as a medical device based on artificial intelligence technologies (SaMD-AI) changes, as well as requirements for testing and inspections—quality control before and after making changes.MethodsThe main types of changes, divided into two groups—major and minor. Major changes imply a subsequent change of a SaMD-AI version to improve efficiency and safety, to change the functionality, and to ensure the processing of new data types. Minor changes imply those that SaMD-AI developers can make due to errors in the program code. Three types of SaMD-AI testings are proposed to use: functional testing, calibration testing or control, and technical testing.ResultsThe presented approaches for validation SaMD-AI changes were introduced. The unified requirements for the request for changes and forms of their submission made this procedure understandable for SaMD-AI developers, and also adjusted the workload for the Experiment experts who checked all the changes made to SaMD-AI.ConclusionThis article discusses the need to control changes in the module of SaMD-AI, as innovative products influencing medical decision making. It justifies the need to control a module operation of SaMD-AI after making changes. To streamline and optimize the necessary and sufficient control procedures, a systematization of possible changes in SaMD-AI and testing methods was carried out.

Change Impact Analysis Case Study for Aviation: Mutation Testing

As the complexity of modern software systems increases, changes in software have become crucial to the software lifecycle. For this reason, it is an important issue for software developers to analyze the changes that occur in the software and to prevent the changes from causing errors in the software. In this paper, mutation testing as software test analysis is examined. Mutation tests have been implemented on open-source Java projects. In addition, for aviation projects, emphasis is placed on performing change impact analysis processes in compliance with the certification based on DO-178C processes.

SFOL DME Pulse Shaping Through Digital Predistortion for High-Accuracy DME

The Stretched-FrOnt-Leg (SFOL) pulse is a high-accuracy distance measuring equipment (DME) pulse developed to support alternative positioning and navigation for aircraft during global navigation satellite system outages. To facilitate the use of the SFOL pulse, it is best to use legacy DMEs that are already deployed to transmit the SFOL pulse, rather than the current Gaussian pulse, through software changes only. When attempting to transmit the SFOL pulse in legacy DMEs, the greatest challenge is the pulse shape distortion caused by the pulse-shaping circuits and power amplifiers (PAs) in the transmission unit such that the original SFOL pulse shape is no longer preserved. This letter proposes an inverse-learning-based DME digital predistortion method and presents successfully transmitted SFOL pulses from a testbed based on a commercial legacy DME that was designed to transmit Gaussian pulses.

Tracking Code Bug Fix Ripple Effects Based on Change Patterns Using Markov Chain Models

Change impact analysis evaluates the changes that are made in the software and finds the ripple effects, in other words, finds the affected software components. Code changes and bug fixes can have a high impact on code quality by introducing new vulnerabilities or increasing their severity. A recent high-visibility example of this is the code changes in the log4j web software CVE-2021-45105 to fix known vulnerabilities by removing and adding method called change types. This bug fix process exposed further code security concerns. In this article, we analyze the most common set of bug fix change patterns to have a better understanding of the distribution of software changes and their impact on code quality. To achieve this, we implemented a tool that compares two versions of the code and extracts the changes that have been made. Then, we investigated how these changes are related to change impact analysis. In our case study, we identified the change types for bug-inducing and bug fix changes using the Quixbugs dataset. Furthermore, we used 13 of the projects and 621 bugs from Defects4J to identify the common change types in bug fixes. Then, to find the change types that cause an impact on the software, we performed an impact analysis on a subset of projects and bugs of Defects4J. The results have shown that, on average, 90% of the bug fix change types are adding a new method declaration and changing the method body. Then, we investigated if these changes cause an impact or a ripple effect in the software by performing a Markov chain-based change impact analysis. The results show that the bug fix changes had only impact rates within a range of 0.4–5%. Furthermore, we performed a statistical correlation analysis to find if any of the bug fixes have a significant correlation with the impact of change. The results have shown that there is a negative correlation between caused impact with the change types adding new method declaration and changing method body. On the other hand, we found that there is a positive correlation between caused impact and changing the field type.

How do Android developers improve non-functional properties of software?

Nowadays there is an increased pressure on mobile app developers to take non-functional properties into account. An app that is too slow or uses much bandwidth will decrease user satisfaction, and thus can lead to users simply abandoning the app. Although automated software improvement techniques exist for traditional software, these are not as prevalent in the mobile domain. Moreover, it is yet unknown if the same software changes would be as effective. With that in mind, we mined overall 100 Android repositories to find out how developers improve execution time, memory consumption, bandwidth usage and frame rate of mobile apps. We categorised non-functional property (NFP) improving commits related to performance to see how existing automated software improvement techniques can be improved. Our results show that although NFP improving commits related to performance are rare, such improvements appear throughout the development lifecycle. We found altogether 560 NFP commits out of a total of 74,408 commits analysed. Memory consumption is sacrificed most often when improving execution time or bandwidth usage, although similar types of changes can improve multiple non-functional properties at once. Code deletion is the most frequently utilised strategy except for frame rate, where increase in concurrency is the dominant strategy. We find that automated software improvement techniques for mobile domain can benefit from addition of SQL query improvement, caching and asset manipulation. Moreover, we provide a classifier which can drastically reduce manual effort to analyse NFP improving commits.

Improved Data Security in Cloud Environment for Test Automation Framework and Access Control for Industry 4.0

In analyzing project regressions, automation has emerged as a major agenda in managing changes in software which requires minimum manual intervention. For rapid testing environment, software development processes such as Agile, Scrum, and XP processes depend on continuous integration tools. There is no single tool to handle the project automation, and the main challenge is dependency on multiple tools. The proposed automation tool should support configuration, execution, and debugging facility. Integrating the project automation works such as software configuration management tools Mercurial and Git, job scheduling tools like Jenkins and Apache Continuum, test management tools like TestNG and Selenium need tight integration which is a challenge. The existing PKI infrastructure for access control does not share data among the software tools and processes increasing the complexity when an organization needs to leverage the existing cloud services. The proposed approach optimizes the execution time by taking single CSV with input test case and metadata information and efficiently group and executes the tests automatically. The proposed method includes implementation of security access control mechanism for the jobs execution platform in cloud environment.

Inclusive Improvisation: Exploring the Line between Listening and Playing Music

The field of Accessible Digital Musical Instruments (ADMIs) is growing rapidly, with instrument designers recognising that adaptations to existing Digital Musical Instruments (DMIs) can foster inclusive music making. ADMIs offer opportunities to engage with a wider range of sounds than acoustic instruments. Furthermore, gestural ADMIs free the music maker from relying on screen, keyboard, and mouse-based interfaces for engaging with these sounds. This brings greater opportunities for exploration, improvisation, empowerment, and flow through music making for people with disability and the communities of practice they are part of. This article argues that developing ADMIs from existing DMIs can speed up the process and allow for more immediate access for those with diverse needs. It presents three case studies of a gestural DMI, originally designed by the first author for his own creative practice, played by people with disability in diverse contexts. The article shows that system-based considerations that enabled an expert percussionist to achieve virtuoso performances with the instrument required minimal hardware and software changes to facilitate greater inclusivity. Understanding the needs of players and customising the system-based movement to sound mappings was of far greater importance in making the instrument accessible.

Mining commit messages to enhance software refactorings recommendation: A machine learning approach

Software refactoring is the common practice that is applied to improve the internal structure of software systems without altering their external behaviors. Software developers sometimes apply refactoring to prepare software systems for further extensions of requirements or adaptation to new requirements often presented as feature requests. However, in such context, identifying where and what type of refactoring to use is very challenging and mostly relies on developer’s intuition and experience. To facilitate refactorings selection during feature requests implementation, existing studies have relied on the past software change history to predict and recommend future refactorings. However, none of these approaches have attempted to exploit the potential of commit messages to drive refactoring recommendation. To this end, this paper proposes a machine-learning approach trained with the past history of previously applied refactorings detected using both traditional refactoring detectors and analysis of commit messages. The approach implements binary classifier to predict the need for refactoring, and a multi-label classifier to recommend required refactorings. The evaluation of the proposed approach based on the dataset comprised of commit messages of 65 open source projects suggest that, the approach significantly outperforms the state-of-the-art approach.

Reliability Assessment of Combined Hardware–Software Non-repairable Time-Critical Systems

Abstract Although there exist various reliability assessment models, many of these models did not consider the temporal incorrectness, incompatibilities and dependencies between hardware and software. Also, many models did not consider the hardware changes, software changes and hardware changes leading to software changes resulting in system failure. Thus, an approach that considers various aspects like pure software failures, pure hardware failures, software change, hardware change, hardware change leading to software change and incompatibility issues between hardware and software components (in the presence of temporal incorrectness) leading to the failure of non-repairable time-critical heterogeneous software systems has been developed. This approach considers the operation of the hardware–software system, both in normal (non-degraded) and degraded modes. The proposed approach was applied to a time-critical stepper motor system, results were obtained and a case study was performed. The effectiveness of the proposed approach has been analyzed by comparing the results obtained from the proposed approach with the results obtained from the other existing approaches.

Study on ecological environment change of water source area of the middle route of South-to-North Water Diversion Project in the past 20 years

The South-to-North Water Diversion Project (SNWDP), which began operation in 2013, is an important step in China’s water resources development, and the middle route of SNWDP is one of the most important parts. However, this project has caused a certain negative impact on the ecological environment of Danjiangkou reservoir, the water source area of this project. This study uses DEM data and Landsat data for different years to analyze land-use changes in QGIS software and discusses the causes and impacts of the middle route of SNWDP. The results show that the present area of water and vegetation increased significantly, while the cultivated land and human area (including urban, rural and other areas of human activity) decreased obviously compared with 1999. The reasons for this change include natural conditions, engineering construction and policies, but policies such as raising the Danjiangkou dam and returning farmland to forests have played a bigger role. The impacts of SNWDP include water quality deterioration, soil erosion, changes in vegetation area, changes in biodiversity and changes in the lives of residents in the area. These impacts have positive reference significance for the construction of water conservancy undertakings in China in the future.

Evaluation of the internal combustion engine pre-heating methods and their influence on the cold engine start performance at an ambient temperature of –40°C

Introduction (statement of the problem and relevance). Starting an internal combustion engine (ICE) at negative ambient temperatures is difficult for a number of reasons. A decrease in the volatility of gasoline leads to the liquid fuel deposition on the cylinder walls, as a result of which the fuel-air mixture ignition is difficult or impossible. Another reason is the geometric dimensions reduction of the gaps in the plain bearings, which is a consequence of the use of materials with different thermal expansion coefficients in the engine design. The high kinematic viscosity of the oil creates a higher hydraulic resistance in the system. These factors complicate the engine oil flow to friction pairs, which can lead to an oil starvation and bearing shell life reduction.The purpose of the research was to determine the optimal pre-heating mode from the point of view of the process energy efficiency and the preservation of the engine resource.Methodology and research methods. To control the thermal state, the internal combustion engine and the cooling circuit were equipped with measuring equipment. The values of temperature, pressure and volume flow sensors were recorded. To implement the pre-heating modes proposed in the article, an engineering engine control unit was used, which allowed making changes in the basic software configuration.Scientific novelty and results. An alternative method of pre-heating has been proposed, including preheating of the engine oil-water heat exchanger to reduce the time for reaching the minimum oil pressure at the main gallery inlet during a cold start. The article provides descriptions of the regular and proposed pre-heating cycles, their assessment in accordance with the represented criteria. Experimental data on pre-heating cycles and cold starts at an ambient temperature of –40°C are presented. The necessity of warming up the engine standard water-oil heat exchanger before a cold start has been experimentally proved, the curves of pressure changes at the main gallery inlet, depending on the pre-heating mode, are given.Practical significance. Reducing the negative influence of cold start on the ICE resource.

SCOPE and EaaSI: Software Emulation and the Future of Architectural History

SCOPE and EaaSI: Software Emulation and the Future of Architectural History