An extensive study on iterative solver resilience : characterization, detection and prediction

Soft errors caused by transient bit flips have the potential to significantly impactan applicalion's behavior. This has motivated the design of an array of techniques to detect, isolate, and correct soft errors using microarchitectural, architectural, compilation­based, or application-level techniques to minimize their impact on the executing application. The first step toward the design of good error detection/correction techniques involves an understanding of an application's vulnerability to soft errors. This work focuses on silent data e orruption's effects on iterative solvers and efforts to mitigate those effects. In this thesis, we first present the first comprehensive characterizalion of !he impact of soft errors on !he convergen ce characteris tics of six iterative methods using application-level fault injection. We analyze the impact of soft errors In terms of the type of error (single-vs multi-bit), the distribution and location of bits affected, the data structure and statement impacted, and varialion with time. We create a public access database with more than 1.5 million fault injection results. We then analyze the performance of soft error detection mechanisms and present the comparalive results. Molivated by our observations, we evaluate a machine-learning based detector that takes as features that are the runtime features observed by the individual detectors to arrive al their conclusions. Our evalualion demonstrates improved results over individual detectors. We then propase amachine learning based method to predict a program's error behavior to make fault injection studies more efficient. We demonstrate this method on asse ssing the performance of soft error detectors. We show that our method maintains 84% accuracy on average with up to 53% less cost. We also show, once a model is trained further fault injection tests would cost 10% of the expected full fault injection runs.“Soft errors” causados por cambios de estado transitorios en bits, tienen el potencial de impactar significativamente el comportamiento de una aplicación. Esto, ha motivado el diseño de una variedad de técnicas para detectar, aislar y corregir soft errors aplicadas a micro-arquitecturas, arquitecturas, tiempo de compilación y a nivel de aplicación para minimizar su impacto en la ejecución de una aplicación. El primer paso para diseñar una buna técnica de detección/corrección de errores, implica el conocimiento de las vulnerabilidades de la aplicación ante posibles soft errors. Este trabajo se centra en los efectos de la corrupción silenciosa de datos en soluciones iterativas, así como en los esfuerzos para mitigar esos efectos. En esta tesis, primeramente, presentamos la primera caracterización extensiva del impacto de soft errors sobre las características convergentes de seis métodos iterativos usando inyección de fallos a nivel de aplicación. Analizamos el impacto de los soft errors en términos del tipo de error (único vs múltiples-bits), de la distribución y posición de los bits afectados, las estructuras de datos, instrucciones afectadas y de las variaciones en el tiempo. Creamos una base de datos pública con más de 1.5 millones de resultados de inyección de fallos. Después, analizamos el desempeño de mecanismos de detección de soft errors actuales y presentamos los resultados de su comparación. Motivados por las observaciones de los resultados presentados, evaluamos un detector de soft errors basado en técnicas de machine learning que toma como entrada las características observadas en el tiempo de ejecución individual de los detectores anteriores al llegar a su conclusión. La evaluación de los resultados obtenidos muestra una mejora por sobre los detectores individualmente. Basados en estos resultados propusimos un método basado en machine learning para predecir el comportamiento de los errores en un programa con el fin de hacer el estudio de inyección de errores mas eficiente. Presentamos este método para evaluar el rendimiento de los detectores de soft errors. Demostramos que nuestro método mantiene una precisión del 84% en promedio con hasta un 53% de mejora en el tiempo de ejecución. También mostramos que una vez que un modelo ha sido entrenado, las pruebas de inyección de errores siguientes costarían 10% del tiempo esperado de ejecución.Postprint (published version

Online disturbance prediction for enhanced availability in smart grids

A gradual move in the electric power industry towards Smart Grids brings new challenges to the system's efficiency and dependability. With a growing complexity and massive introduction of renewable generation, particularly at the distribution level, the number of faults and, consequently, disturbances (errors and failures) is expected to increase significantly. This threatens to compromise grid's availability as traditional, reactive management approaches may soon become insufficient. On the other hand, with grids' digitalization, real-time status data are becoming available. These data may be used to develop advanced management and control methods for a sustainable, more efficient and more dependable grid. A proactive management approach, based on the use of real-time data for predicting near-future disturbances and acting in their anticipation, has already been identified by the Smart Grid community as one of the main pillars of dependability of the future grid. The work presented in this dissertation focuses on predicting disturbances in Active Distributions Networks (ADNs) that are a part of the Smart Grid that evolves the most. These are distribution networks with high share of (renewable) distributed generation and with systems in place for real-time monitoring and control. Our main goal is to develop a methodology for proactive network management, in a sense of proactive mitigation of disturbances, and to design and implement a method for their prediction. We focus on predicting voltage sags as they are identified as one of the most frequent and severe disturbances in distribution networks. We address Smart Grid dependability in a holistic manner by considering its cyber and physical aspects. As a result, we identify Smart Grid dependability properties and develop a taxonomy of faults that contribute to better understanding of the overall dependability of the future grid. As the process of grid's digitization is still ongoing there is a general problem of a lack of data on the grid's status and especially disturbance-related data. These data are necessary to design an accurate disturbance predictor. To overcome this obstacle we introduce a concept of fault injection to simulation of power systems. We develop a framework to simulate a behavior of distribution networks in the presence of faults, and fluctuating generation and load that, alone or combined, may cause disturbances. With the framework we generate a large set of data that we use to develop and evaluate a voltage-sag disturbance predictor. To quantify how prediction and proactive mitigation of disturbances enhance availability we create an availability model of a proactive management. The model is generic and may be applied to evaluate the effect of proactive management on availability in other types of systems, and adapted for quantifying other types of properties as well. Also, we design a metric and a method for optimizing failure prediction to maximize availability with proactive approach. In our conclusion, the level of availability improvement with proactive approach is comparable to the one when using high-reliability and costly components. Following the results of the case study conducted for a 14-bus ADN, grid's availability may be improved by up to an order of magnitude if disturbances are managed proactively instead of reactively. The main results and contributions may be summarized as follows: (i) Taxonomy of faults in Smart Grid has been developed; (ii) Methodology and methods for proactive management of disturbances have been proposed; (iii) Model to quantify availability with proactive management has been developed; (iv) Simulation and fault-injection framework has been designed and implemented to generate disturbance-related data; (v) In the scope of a case study, a voltage-sag predictor, based on machine- learning classification algorithms, has been designed and the effect of proactive disturbance management on downtime and availability has been quantified

From detection to optimization: impact of soft errors on high-performance computing applications

As high-performance computing (HPC) continues to progress, constraints on HPC system design forces the handling of errors to higher levels in the software stack. Of the types of errors facing HPC, soft errors that silently corrupt system or application state are among the most severe. The behavior of HPC applications in the presence of soft errors is critical to gain insight for effective utilization of HPC systems. The need to understand this behavior can be used in developing algorithm-based error detection guided by application characteristics from fault injection and error propagation studies. Furthermore, the realization that applications are tolerant to small errors allows optimizations such as lossy compression on high-cost data transfers. Lossy compression adds small user controllable amounts of error when compressing data, to reduce data size before expensive data transfers saving time. This dissertation investigates and improves the resiliency of HPC applications to soft errors, and explores lossy compression as a new form of optimization for expensive, time-consuming data transfers

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment

Investigating the attainment of optimum data quality for EHR Big Data: proposing a new methodological approach

The value derivable from the use of data is continuously increasing since some years. Both commercial and non-commercial organisations have realised the immense benefits that might be derived if all data at their disposal could be analysed and form the basis of decision taking. The technological tools required to produce, capture, store, transmit and analyse huge amounts of data form the background to the development of the phenomenon of Big Data. With Big Data, the aim is to be able to generate value from huge amounts of data, often in non-structured format and produced extremely frequently. However, the potential value derivable depends on general level of governance of data, more precisely on the quality of the data. The field of data quality is well researched for traditional data uses but is still in its infancy for the Big Data context. This dissertation focused on investigating effective methods to enhance data quality for Big Data. The principal deliverable of this research is in the form of a methodological approach which can be used to optimize the level of data quality in the Big Data context. Since data quality is contextual, (that is a non-generalizable field), this research study focuses on applying the methodological approach in one use case, in terms of the Electronic Health Records (EHR). The first main contribution to knowledge of this study systematically investigates which data quality dimensions (DQDs) are most important for EHR Big Data. The two most important dimensions ascertained by the research methods applied in this study are accuracy and completeness. These are two well-known dimensions, and this study confirms that they are also very important for EHR Big Data. The second important contribution to knowledge is an investigation into whether Artificial Intelligence with a special focus upon machine learning could be used in improving the detection of dirty data, focusing on the two data quality dimensions of accuracy and completeness. Regression and clustering algorithms proved to be more adequate for accuracy and completeness related issues respectively, based on the experiments carried out. However, the limits of implementing and using machine learning algorithms for detecting data quality issues for Big Data were also revealed and discussed in this research study. It can safely be deduced from the knowledge derived from this part of the research study that use of machine learning for enhancing data quality issues detection is a promising area but not yet a panacea which automates this entire process. The third important contribution is a proposed guideline to undertake data repairs most efficiently for Big Data; this involved surveying and comparing existing data cleansing algorithms against a prototype developed for data reparation. Weaknesses of existing algorithms are highlighted and are considered as areas of practice which efficient data reparation algorithms must focus upon. Those three important contributions form the nucleus for a new data quality methodological approach which could be used to optimize Big Data quality, as applied in the context of EHR. Some of the activities and techniques discussed through the proposed methodological approach can be transposed to other industries and use cases to a large extent. The proposed data quality methodological approach can be used by practitioners of Big Data Quality who follow a data-driven strategy. As opposed to existing Big Data quality frameworks, the proposed data quality methodological approach has the advantage of being more precise and specific. It gives clear and proven methods to undertake the main identified stages of a Big Data quality lifecycle and therefore can be applied by practitioners in the area. This research study provides some promising results and deliverables. It also paves the way for further research in the area. Technical and technological changes in Big Data is rapidly evolving and future research should be focusing on new representations of Big Data, the real-time streaming aspect, and replicating same research methods used in this current research study but on new technologies to validate current results

Evidence-driven testing and debugging of software systems

Program debugging is the process of testing, exposing, reproducing, diagnosing and fixing software bugs. Many techniques have been proposed to aid developers during software testing and debugging. However, researchers have found that developers hardly use or adopt the proposed techniques in software practice. Evidently, this is because there is a gap between proposed methods and the state of software practice. Most methods fail to address the actual needs of software developers. In this dissertation, we pose the following scientific question: How can we bridge the gap between software practice and the state-of-the-art automated testing and debugging techniques? To address this challenge, we put forward the following thesis: Software testing and debugging should be driven by empirical evidence collected from software practice. In particular, we posit that the feedback from software practice should shape and guide (the automation) of testing and debugging activities. In this thesis, we focus on gathering evidence from software practice by conducting several empirical studies on software testing and debugging activities in the real-world. We then build tools and methods that are well-grounded and driven by the empirical evidence obtained from these experiments. Firstly, we conduct an empirical study on the state of debugging in practice using a survey and a human study. In this study, we ask developers about their debugging needs and observe the tools and strategies employed by developers while testing, diagnosing and repairing real bugs. Secondly, we evaluate the effectiveness of the state-of-the-art automated fault localization (AFL) methods on real bugs and programs. Thirdly, we conducted an experiment to evaluate the causes of invalid inputs in software practice. Lastly, we study how to learn input distributions from real-world sample inputs, using probabilistic grammars. To bridge the gap between software practice and the state of the art in software testing and debugging, we proffer the following empirical results and techniques: (1) We collect evidence on the state of practice in program debugging and indeed, we found that there is a chasm between (available) debugging tools and developer needs. We elicit the actual needs and concerns of developers when testing and diagnosing real faults and provide a benchmark (called DBGBench) to aid the automated evaluation of debugging and repair tools. (2) We provide empirical evidence on the effectiveness of several state-of-the-art AFL techniques (such as statistical debugging formulas and dynamic slicing). Building on the obtained empirical evidence, we provide a hybrid approach that outperforms the state-of-the-art AFL techniques. (3) We evaluate the prevalence and causes of invalid inputs in software practice, and we build on the lessons learned from this experiment to build a general-purpose algorithm (called ddmax) that automatically diagnoses and repairs real-world invalid inputs. (4) We provide a method to learn the distribution of input elements in software practice using probabilistic grammars and we further employ the learned distribution to drive the test generation of inputs that are similar (or dissimilar) to sample inputs found in the wild. In summary, we propose an evidence-driven approach to software testing and debugging, which is based on collecting empirical evidence from software practice to guide and direct software testing and debugging. In our evaluation, we found that our approach is effective in improving the effectiveness of several debugging activities in practice. In particular, using our evidence-driven approach, we elicit the actual debugging needs of developers, improve the effectiveness of several automated fault localization techniques, effectively debug and repair invalid inputs, and generate test inputs that are (dis)similar to real-world inputs. Our proposed methods are built on empirical evidence and they improve over the state-of-the-art techniques in testing and debugging.Software-Debugging bezeichnet das Testen, Aufspüren, Reproduzieren, Diagnostizieren und das Beheben von Fehlern in Programmen. Es wurden bereits viele Debugging-Techniken vorgestellt, die Softwareentwicklern beim Testen und Debuggen unterstützen. Dennoch hat sich in der Forschung gezeigt, dass Entwickler diese Techniken in der Praxis kaum anwenden oder adaptieren. Das könnte daran liegen, dass es einen großen Abstand zwischen den vorgestellten und in der Praxis tatsächlich genutzten Techniken gibt. Die meisten Techniken genügen den Anforderungen der Entwickler nicht. In dieser Dissertation stellen wir die folgende wissenschaftliche Frage: Wie können wir die Kluft zwischen Software-Praxis und den aktuellen wissenschaftlichen Techniken für automatisiertes Testen und Debugging schließen? Um diese Herausforderung anzugehen, stellen wir die folgende These auf: Das Testen und Debuggen von Software sollte von empirischen Daten, die in der Software-Praxis gesammelt wurden, vorangetrieben werden. Genauer gesagt postulieren wir, dass das Feedback aus der Software-Praxis die Automation des Testens und Debuggens formen und bestimmen sollte. In dieser Arbeit fokussieren wir uns auf das Sammeln von Daten aus der Software-Praxis, indem wir einige empirische Studien über das Testen und Debuggen von Software in der echten Welt durchführen. Auf Basis der gesammelten Daten entwickeln wir dann Werkzeuge, die sich auf die Daten der durchgeführten Experimente stützen. Als erstes führen wir eine empirische Studie über den Stand des Debuggens in der Praxis durch, wobei wir eine Umfrage und eine Humanstudie nutzen. In dieser Studie befragen wir Entwickler zu ihren Bedürfnissen, die sie beim Debuggen haben und beobachten die Werkzeuge und Strategien, die sie beim Diagnostizieren, Testen und Aufspüren echter Fehler einsetzen. Als nächstes bewerten wir die Effektivität der aktuellen Automated Fault Localization (AFL)- Methoden zum automatischen Aufspüren von echten Fehlern in echten Programmen. Unser dritter Schritt ist ein Experiment, um die Ursachen von defekten Eingaben in der Software-Praxis zu ermitteln. Zuletzt erforschen wir, wie Häufigkeitsverteilungen von Teileingaben mithilfe einer Grammatik von echten Beispiel-Eingaben aus der Praxis gelernt werden können. Um die Lücke zwischen Software-Praxis und der aktuellen Forschung über Testen und Debuggen von Software zu schließen, bieten wir die folgenden empirischen Ergebnisse und Techniken: (1) Wir sammeln aktuelle Forschungsergebnisse zum Stand des Software-Debuggens und finden in der Tat eine Diskrepanz zwischen (vorhandenen) Debugging-Werkzeugen und dem, was der Entwickler tatsächlich benötigt. Wir sammeln die tatsächlichen Bedürfnisse von Entwicklern beim Testen und Debuggen von Fehlern aus der echten Welt und entwickeln einen Benchmark (DbgBench), um das automatische Evaluieren von Debugging-Werkzeugen zu erleichtern. (2) Wir stellen empirische Daten zur Effektivität einiger aktueller AFL-Techniken vor (z.B. Statistical Debugging-Formeln und Dynamic Slicing). Auf diese Daten aufbauend, stellen wir einen hybriden Algorithmus vor, der die Leistung der aktuellen AFL-Techniken übertrifft. (3) Wir evaluieren die Häufigkeit und Ursachen von ungültigen Eingaben in der Softwarepraxis und stellen einen auf diesen Daten aufbauenden universell einsetzbaren Algorithmus (ddmax) vor, der automatisch defekte Eingaben diagnostiziert und behebt. (4) Wir stellen eine Methode vor, die Verteilung von Schnipseln von Eingaben in der Software-Praxis zu lernen, indem wir Grammatiken mit Wahrscheinlichkeiten nutzen. Die gelernten Verteilungen benutzen wir dann, um den Beispiel-Eingaben ähnliche (oder verschiedene) Eingaben zu erzeugen. Zusammenfassend stellen wir einen auf der Praxis beruhenden Ansatz zum Testen und Debuggen von Software vor, welcher auf empirischen Daten aus der Software-Praxis basiert, um das Testen und Debuggen zu unterstützen. In unserer Evaluierung haben wir festgestellt, dass unser Ansatz effektiv viele Debugging-Disziplinen in der Praxis verbessert. Genauer gesagt finden wir mit unserem Ansatz die genauen Bedürfnisse von Entwicklern, verbessern die Effektivität vieler AFL-Techniken, debuggen und beheben effektiv fehlerhafte Eingaben und generieren Test-Eingaben, die (un)ähnlich zu Eingaben aus der echten Welt sind. Unsere vorgestellten Methoden basieren auf empirischen Daten und verbessern die aktuellen Techniken des Testens und Debuggens