497 research outputs found
Untersuchung von Performanzveränderungen auf Quelltextebene
Änderungen am Quelltext einer Software können zu veränderter Performanz führen. Um das Auftreten von Regressionen zu verhindern und die Effekte von Quelltextänderungen, von denen eine Verbesserung erwartet wird, zu überprüfen, ist die Messung der Auswirkungen von Quelltextänderungen auf die Performanz sowie das tiefgehende Verständnis des Laufzeitverhaltens der beteiligten Quelltextkonstrukte notwendig. Die Spezifikation von Benchmarks oder Lasttests, um Regressionen zu erkennen, erfordert immensen manuellen Aufwand. Für das Verständnis der Änderungen sind anschließend oft weitere Experimente notwendig.
In der vorliegenden Arbeit wird der Ansatz Performanzanalyse von Softwaresystemen (Peass) entwickelt. Peass beruht auf der Annahme, dass Performanzänderungen durch Messung der Performanz von Unittests erkennbar ist. Peass besteht aus (1) einer Methode zur Regressionstestselektion, d. h. zur Bestimmung, zwischen welchen Commits sich die Performanz geändert haben kann basierend auf statischer Quelltextanalyse und Analyse des Laufzeitverhaltens, (2) einer Methode zur Umwandlung von Unittests in Performanztests und zur statistisch zuverlässigen und reproduzierbaren Messung der Performanz und (3) einer Methode zur Unterstützung des Verstehens von Ursachen von Performanzänderungen. Der Peass-Ansatzes ermöglicht es somit, durch den Workload von Unittests messbare Performanzänderungen automatisiert zu untersuchen.
Die Validität des Ansatzes wird geprüft, indem gezeigt wird, dass (1) typische Performanzprobleme in künstlichen Testfällen und (2) reale, durch Entwickler markierte Performanzänderungen durch Peass gefunden werden können. Durch eine Fallstudie in einem laufenden Softwareentwicklungsprojekt wird darüber hinaus gezeigt, dass Peass in der Lage ist, relevante Performanzänderungen zu erkennen.:1 Einleitung
1.1 Motivation
1.2 Ansatz
1.3 Forschungsfragen
1.4 Beiträge
1.5 Aufbau der Arbeit
2 Grundlagen
2.1 Software Performance Engineering
2.2 Modellbasierter Ansatz
2.2.1 Ãœberblick
2.2.2 Performanzantipattern
2.3 Messbasierter Ansatz
2.3.1 Messprozess
2.3.2 Messwertanalyse
2.4 Messung in künstlichen Umgebungen
2.4.1 Benchmarking
2.4.2 Lasttests
2.4.3 Performanztests
2.5 Messung in realen Umgebungen: Monitoring
2.5.1 Ãœberblick
2.5.2 Umsetzung
2.5.3 Werkzeuge
3 Regressionstestselektion
3.1 Ansatz
3.1.1 Grundidee
3.1.2 Voraussetzungen
3.1.3 Zweistufiger Prozess
3.2 Statische Testselektion
3.2.1 Selektierte Änderungen
3.2.2 Prozess
3.2.3 Implementierung
3.3 Tracevergleich
3.3.1 Selektierte Änderungen
3.3.2 Prozess
3.3.3 Implementierung
3.3.4 Kombination mit statischer Analyse
3.4 Evaluation
3.4.1 Implementierung
3.4.2 Exaktheit
3.4.3 Korrektheit
3.4.4 Diskussion der Validität
3.5 Verwandte Arbeiten
3.5.1 Funktionale Regressionstestbestimmung
3.5.2 Regressionstestbestimmung für Performanztests
4 Messprozess
4.1 Vergleich von Mess- und Analysemethoden
4.1.1 Vorgehen
4.1.2 Fehlerbetrachtung
4.1.3 Workloadgröße der künstlichen Unittestpaare
4.2 Messmethode
4.2.1 Aufbau einer Iteration
4.2.2 Beenden von Messungen
4.2.3 Garbage Collection je Iteration
4.2.4 Umgang mit Standardausgabe
4.2.5 Zusammenfassung der Messmethode
4.3 Analysemethode
4.3.1 Auswahl des statistischen Tests
4.3.2 Ausreißerentfernung
4.3.3 Parallelisierung
4.4 Evaluierung
4.4.1 Vergleich mit JMH
4.4.2 Reproduzierbarkeit der Ergebnisse
4.4.3 Fazit
4.5 Verwandte Arbeiten
4.5.1 Beenden von Messungen
4.5.2 Änderungserkennung
4.5.3 Anomalieerkennung
5 Ursachenanalyse
5.1 Reduktion des Overheads der Messung einzelner Methoden
5.1.1 Generierung von Beispielprojekten
5.1.2 Messung von Methodenausführungsdauern
5.1.3 Optionen zur Overheadreduktion
5.1.4 Messergebnisse
5.1.5 Überprüfung mit MooBench
5.2 Messkonfiguration der Ursachenanalyse
5.2.1 Grundlagen
5.2.2 Fehlerbetrachtung
5.2.3 Ansatz
5.2.4 Messergebnisse
5.3 Verwandte Arbeiten
5.3.1 Monitoringoverhead
5.3.2 Ursachenanalyse für Performanzänderungen
5.3.3 Ursachenanalyse für Performanzprobleme
6 Evaluation
6.1 Validierung durch künstliche Performanzprobleme
6.1.1 Reproduktion durch Benchmarks
6.1.2 Umwandlung der Benchmarks
6.1.3 Überprüfen von Problemen mit Peass
6.2 Evaluation durch reale Performanzprobleme
6.2.1 Untersuchung dokumentierter Performanzänderungen offenen Projekten
6.2.2 Untersuchung der Performanzänderungen in GeoMap
7 Zusammenfassung und Ausblick
7.1 Zusammenfassung
7.2 AusblickChanges to the source code of a software may result in varied performance. In order to prevent the occurance of regressions and check the effect of source changes, which are
expected to result in performance improvements, both the measurement of the impact of source code changes and a deep understanding of the runtime behaviour of the used
source code elements are necessary. The specification of benchmarks and load tests, which are able to detect performance regressions, requires immense manual effort. To
understand the changes, often additional experiments are necessary.
This thesis develops the Peass approach (Performance analysis of software systems). Peass is based on the assumption, that performance changes can be identified by unit
tests. Therefore, Peass consists of (1) a method for regression test selection, which determines between which commits the performance may have changed based on static code
analysis and analysis of the runtime behavior, (2) a method for transforming unit tests into performance tests and for statistically reliable and reproducible measurement of
the performance and (3) a method for aiding the diagnosis of root causes of performance changes. The Peass approach thereby allows to automatically examine performance
changes that are measurable by the workload of unit tests.
The validity of the approach is evaluated by showing that (1) typical performance problems in artificial test cases and (2) real, developer-tagged performance changes can be found by Peass. Furthermore, a case study in an ongoing software development project shows that Peass is able to detect relevant performance changes.:1 Einleitung
1.1 Motivation
1.2 Ansatz
1.3 Forschungsfragen
1.4 Beiträge
1.5 Aufbau der Arbeit
2 Grundlagen
2.1 Software Performance Engineering
2.2 Modellbasierter Ansatz
2.2.1 Ãœberblick
2.2.2 Performanzantipattern
2.3 Messbasierter Ansatz
2.3.1 Messprozess
2.3.2 Messwertanalyse
2.4 Messung in künstlichen Umgebungen
2.4.1 Benchmarking
2.4.2 Lasttests
2.4.3 Performanztests
2.5 Messung in realen Umgebungen: Monitoring
2.5.1 Ãœberblick
2.5.2 Umsetzung
2.5.3 Werkzeuge
3 Regressionstestselektion
3.1 Ansatz
3.1.1 Grundidee
3.1.2 Voraussetzungen
3.1.3 Zweistufiger Prozess
3.2 Statische Testselektion
3.2.1 Selektierte Änderungen
3.2.2 Prozess
3.2.3 Implementierung
3.3 Tracevergleich
3.3.1 Selektierte Änderungen
3.3.2 Prozess
3.3.3 Implementierung
3.3.4 Kombination mit statischer Analyse
3.4 Evaluation
3.4.1 Implementierung
3.4.2 Exaktheit
3.4.3 Korrektheit
3.4.4 Diskussion der Validität
3.5 Verwandte Arbeiten
3.5.1 Funktionale Regressionstestbestimmung
3.5.2 Regressionstestbestimmung für Performanztests
4 Messprozess
4.1 Vergleich von Mess- und Analysemethoden
4.1.1 Vorgehen
4.1.2 Fehlerbetrachtung
4.1.3 Workloadgröße der künstlichen Unittestpaare
4.2 Messmethode
4.2.1 Aufbau einer Iteration
4.2.2 Beenden von Messungen
4.2.3 Garbage Collection je Iteration
4.2.4 Umgang mit Standardausgabe
4.2.5 Zusammenfassung der Messmethode
4.3 Analysemethode
4.3.1 Auswahl des statistischen Tests
4.3.2 Ausreißerentfernung
4.3.3 Parallelisierung
4.4 Evaluierung
4.4.1 Vergleich mit JMH
4.4.2 Reproduzierbarkeit der Ergebnisse
4.4.3 Fazit
4.5 Verwandte Arbeiten
4.5.1 Beenden von Messungen
4.5.2 Änderungserkennung
4.5.3 Anomalieerkennung
5 Ursachenanalyse
5.1 Reduktion des Overheads der Messung einzelner Methoden
5.1.1 Generierung von Beispielprojekten
5.1.2 Messung von Methodenausführungsdauern
5.1.3 Optionen zur Overheadreduktion
5.1.4 Messergebnisse
5.1.5 Überprüfung mit MooBench
5.2 Messkonfiguration der Ursachenanalyse
5.2.1 Grundlagen
5.2.2 Fehlerbetrachtung
5.2.3 Ansatz
5.2.4 Messergebnisse
5.3 Verwandte Arbeiten
5.3.1 Monitoringoverhead
5.3.2 Ursachenanalyse für Performanzänderungen
5.3.3 Ursachenanalyse für Performanzprobleme
6 Evaluation
6.1 Validierung durch künstliche Performanzprobleme
6.1.1 Reproduktion durch Benchmarks
6.1.2 Umwandlung der Benchmarks
6.1.3 Überprüfen von Problemen mit Peass
6.2 Evaluation durch reale Performanzprobleme
6.2.1 Untersuchung dokumentierter Performanzänderungen offenen Projekten
6.2.2 Untersuchung der Performanzänderungen in GeoMap
7 Zusammenfassung und Ausblick
7.1 Zusammenfassung
7.2 Ausblic
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Novel DVFS Methodologies For Power-Efficient Mobile MPSoC
Low power mobile computing systems such as smartphones and wearables have become an integral part of our daily lives and are used in various ways to enhance our daily lives. Majority of modern mobile computing systems are powered by multi-processor System-on-a-Chip (MPSoC), where multiple processing elements are utilized on a single chip. Given the fact that these devices are battery operated most of the times, thus, have limited power supply and the key challenges include catering for performance while reducing the power consumption. Moreover, the reliability in terms of lifespan of these devices are also affected by the peak thermal behaviour on the device, which retrospectively also make such devices vulnerable to temperature side-channel attack. This thesis is concerned with performing Dynamic Voltage and Frequency Scaling (DVFS) on different processing elements such as CPU & GPU, and memory unit such as RAM to address the aforementioned challenges. Firstly, we design a Computer Vision based machine learning technique to classify applications automatically into different categories of workload such that DVFS could be performed on the CPU to reduce the power consumption of the device while executing the application. Secondly, we develop a reinforcement learning based agent to perform DVFS on CPU and GPU while considering the user's interaction with such devices to optimize power consumption and thermal behaviour. Next, we develop a heuristic based automated agent to perform DVFS on CPU, GPU and RAM to optimize the same while executing an application. Finally, we explored the affect of DVFS on CPUs leading to vulnerabilities against temperature side-channel attack and hence, we also designed a methodology to secure against such attack while improving the reliability in terms of lifespan of such devices
Protein Kinase C zeta Regulates the Development of Cord Blood T Cells from the Immature Th2 to the Mature Th1 Cytokine Phenotype and its Implication in Development of Allergic Sensitization
More than 30% of the Australian population is affected by allergies, posing a significant burden to the health system, affected individuals and their families, highlighting the urgent need to identify pathways that lead to sensitization and disease. There is a view that the foundation for allergy development is laid early in postnatal life. Due to immune immaturity, early diagnosis is difficult. T cells play a critical role in the development of allergic diseases. Cord blood T cells (CBTC) have been used as a proxy for neonatal T cells in studies to investigate the functional development of neonatal T cells. These are predominantly naive and produce interleukin-4 (IL-4) and very little Th1 cytokines such as interferon-gamma (IFN-γ), implicating a Th2 bias at birth. We have been interested in trying to understand the basis for persistence of this Th2 bias during T cell maturation and previously reported that a deficiency in Protein Kinase C (PKC) signalling, caused by reduced levels of PKC, is the likely cause. Low PKCζ expression in CBTC was associated with a n increased risk of allergic sensitization in infants, which could be attenuated by supplementing pregnant women with omega-3 fatty acids, associated with increased PKCζ expression in T cells at birth. Study aims (i) examine the role of PKCζ in the regulation of the transition of the Th2 phenotype to the mature Th1 cytokine propensity. (ii) attempt to identify the transient deficiency of PKCζ in the perinatal period. (iii) examine the effects of omega-3 fatty acid supplementation on PKCζ levels, during the transient period. (iv) examine the mechanisms of the transition from Th2 to Th1 cell bias. I validated a flow cytometry assay for the quantification of ten PKC isozymes by examining commercially available anti-PKC antibodies in western blotting. Antibodies with the required specificity were then used to determine PKC isozymes expression in T cells. Using this new technique, I characterized the expression of PKC isozymes in cultured CBTC over a 7-day period. Deficient levels normalized over 24h. A number of CBTC maturation systems using anti-CD3/CD28 antibodies, PHA, PHA/PMA, and PMA/Ionomycin in absence or presence of IL-2 were set up. When CBTC with low expression of PKCζ were matured using anti-CD3/CD28 antibodies and IL-2, they retained their Th2 bias. In contrast, high PKCζ expressing cells produced high levels of IFN-γ and minimal IL-4, and displayed PKCζ activation. In contrast, using PHA/PMA gave no PKCζ activation and the cells did not transit to a Th1 cytokine phenotype, but transfection of a constitutively active PKCζ mutant was sufficient to cause this transit, suggesting that PKCζ activation is important for the transit. We found that the low expression of PKCζ in CBTC is transient, identifying a ‘window of opportunity’ for modulating the levels and preventing development towards a Th2 cytokine bias and allergic sensitization, evidenced by showing that omega-3 fatty acids increase the PKCζ levels and promote T cell maturation towards a Th1 phenotype. The findings support PKCζ as a perinatal biomarker to predict babies at risk of allergy and target for nutritional intervention.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202
The Effectiveness of Using Learning Device Information Systems in Preparing Learning Plans
The importance of the ability to develop lesson plans requires educators to be able to understand and master learning content as well as understand various ways to complete it. The learning device information system (SIPP) as one of the results of technology is here to help educators complete this task. The purpose of this study was to demonstrate the effectiveness and efficiency of SIPP in developing learning plans in early childhood education. The research was conducted using a quantitative approach, with descriptive statistical analysis presented in the form of a frequency distribution table. The questionnaire was distributed using a Likert scale, which contains items regarding goal achievement, quality, output, and user response as indicators of effectiveness and contains items regarding the use of time, cost, effort, and use of paper materials as indicators of efficiency. The results showed that the four indicators of effectiveness were met with an average score of 385.8. Efficiency which includes four indicators achieves an average score of 19.3. The availability of components in templates that are adjusted to the applicable provisions and can be used automatically allows users to complete their tasks in a relatively faster time. The availability of components in templates that are adjusted to the applicable provisions and can be used automatically allows users to complete their tasks in a relatively faster time. The conclusion is that SIPP is very effective and efficient for preparing lesson plans so that it can facilitate and save the teacher's time in completing the task.
Keywords: learning device information; early childhood education, lesson plan
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