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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

[서평]「The Unified Modeling Language User Guide」

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

/pdf/handbook-of-software-reliability-engineering-32dwvaljmm.pdf

Handbook of software reliability engineering

コンピュータ・サイエンス : ACM computing surveys

The social network perspective has served as a useful framework for studying scientific research collaboration in different disciplines. Although collaboration in computer science research has received some attention, software engineering research collaboration has remained unexplored to a large extent. In this paper, we examine the collaboration networks based on co-authorship information of papers from ten software engineering publication venues over the 1976-2010 time period. We compare time variations of certain parameters of these networks with corresponding parameters of collaboration networks from other disciplines. We also explore whether software engineering collaboration networks manifest symptoms of the small-world phenomenon, conform to the criteria of "social networks", and manifest increasing collaboration with time. In the light of these observations, we highlight some general characteristics of collaboration in software engineering research. The results presented in this paper facilitate understanding of the progression of software engineering from its infancy to maturity, and lay the foundation for developing theoretical models to explain the evolution of its research collaboration characteristics.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6592&context=sis_research

The social network of software engineering research

To expedite the design process of a complex system, it is required to view the system as interactions of complex subsystems which are not necessarily RTL components and synthesise the hardware from such a specification In the paper, an automated design and synthesis environment called DOORS has been proposed that can accept a complex system specification at such a high level of abstraction and synthesises the circuit DOORS is based on an object-oriented design methodology Circuit elements are treated as classes The designer describes the circuit behaviour as a sequence of state transitions in response to external messages The method executed in response to a message is transparent to the external world The specification may involve presynthesised components whose functionalities are invoked by sending messages to them The synthesis system of DOORS accepts the specification and synthesises the circuit in terms of these components The authors have mainly concentrated on the datapath synthesis mechanism of DOORS

DOORS: an object-oriented CAD system for high level synthesis

Predicting the future is hard, more so in active research areas. In this paper, we customize an established model for citation prediction of research papers and apply it on research topics. We argue that research topics, rather than individual publications, have wider relevance in the research ecosystem, for individuals as well as organizations. In this study, topics are extracted from a corpus of software engineering publications covering 55,000+ papers written by more than 70,000 authors across 56 publication venues, over a span of 38 years, using natural language processing techniques. We demonstrate how critical aspects of the original paper-based prediction model are valid for a topic-based approach. Our results indicate the customized model is able to predict citations for many of the topics considered in our study with reasonably high accuracy. Insights from these results indicate the promise of citation of prediction of research topics, and its utility for individual researchers, as well as research groups.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6589&context=sis_research

Predicting the Impact of Software Engineering Topics: An Empirical Study

Energy efficiency is a major concern in today's data centers that house large scale distributed processing systems such as data parallel MapReduce clusters. Modern power aware systems utilize the dynamic voltage and frequency scaling mechanism available in processors to manage the energy consumption. In this paper, we initially characterize the energy efficiency of MapReduce jobs with respect to built-in power governors. Our analysis indicates that while a built-in power governor provides the best energy efficiency for a job that is CPU as well as IO intensive, a common CPU-frequency across the cluster provides best the energy efficiency for other types of jobs. In order to identify this optimal frequency setting, we derive energy and performance models for MapReduce jobs on a HPC cluster and validate these models experimentally on different platforms. We demonstrate how these models can be used to improve energy efficiency of the machine learning MapReduce applications running on the Yarn platform. The execution of jobs at their optimal frequencies improves the energy efficiency by average 25% over the default governor setting. In case of mixed workloads, the energy efficiency improves by up to 10% when we use an optimal CPU-frequency across the cluster.

CPU Frequency Tuning to Improve Energy Efficiency of MapReduce Systems

High performance computing applications are far more difficult to write, therefore, practitioners expect a well-tuned software to last long and provide optimized performance even when the hardware is upgraded. It may also be necessary to write software using sufficient abstraction over the hardware so that it is capable of running on heterogeneous architecture. Therefore, it is required to have a proper programming abstraction paradigm that strikes a balance between the abstraction and visibility over the hardware so that the programmer can write a program without having to understand the hardware nuances, yet exploit the compute power optimally. In this paper we have analyzed the power of design abstraction and performance of a popular design abstraction framework called Thrust. We have shown quantitatively that while it is easier to write an application using Thrust compared to writing the same in the native CUDA or OpenMP backends, the framework does not provide any abstraction over the memory hierarchy of the underlying backend to the programmer. We have compared the performance of three Thrust applications with their corresponding native versions in CUDA, OpenMP, Xeon-Phi and the CPP backends and demonstrate that the current Thrust version performs poorly in most of the cases when the application is compute intensive. However, the framework provides close to the native performance for a non-compute intensive applications. We analyze the reasons for the performance and highlight the improvements necessary for the framework.

Santonu Sarkar

Papers

The social network of software engineering research

DOORS: an object-oriented CAD system for high level synthesis

Predicting the Impact of Software Engineering Topics: An Empirical Study

CPU Frequency Tuning to Improve Energy Efficiency of MapReduce Systems

An Empirical Evaluation of Design Abstraction and Performance of Thrust Framework