In this tutorial, we will cover some of the basic approaches to software testing including: various types of coverage-based testing, testing based on program graphs, input space testing, data flow testing, and syntax-based testing. In addition to providing the theoretical background and explanation for each type of testing, attendees will work through some hands-on exercises. Attendees are encouraged to bring some of their own code segments to use in the hands-on portion of the tutorial. If time permits, we will also introduce the concepts of Test Driven Development.
Speaker Description:
Dr. Jeffrey Carver is an Associate Professor in the Department of Computer Science at the University of Alabama. He earned his PhD in Computer Science from the University of Maryland. His main research interests include empirical software engineering, software engineering for science, software quality, human factors in software engineering and software process improvement. He has been an active leader in the Software Engineering for Science community over the last decade. In addition, he is the primary organizer of a workshop series focused on Software Engineering for Science. He is a Senior Member of the IEEE Computer Society and a Senior Member of the ACM.
Ryan May has a Ph.D. in radar meteorology and works as a Software Engineer at Unidata. His primary work is on the THREDDS data server and a wide array of Python efforts at Unidata: these include the MetPy and Siphon packages, putting together training materials, and contributing to other open source libraries to smooth the way for using Python in meteorology.
Sean Arms is a boundary-layer guy by training (PhD), and Software Engineer by Luck (TM) at UCAR/UCP/Unidata. His primary work is focused on THREDDS related projects, such as netCDF-Java, the THREDDS Data Server, Rosetta, and most recently, Siphon.
Dr. Kevin Goebbert is an Associate Professor of Meteorology at Valparaiso University where he teaches a broad spectrum of meteorology courses including synoptic meteorology, numerical weather prediction, and meteorological computer applications, in which he teaches Fortran and Python to upper-level undergraduate students. In addition, he currently serves on the Unidata Users Committee.
Initial data exploration often begins with an iterative approach involving reading data from files, writing small bits of code to process the data in some way, plotting data to visualize patterns and inter-variable relationships, and sharing ideas with colleagues. The combination of the Python programming language, the data analysis package pandas, and Jupyter (formerly IPython) notebooks, an interactive environment for development and sharing, provides a powerful and easy-to-use toolset for data exploration.
Speaker Description:
Michael Brandt, Paul Madden and Matt Savoie are software engineers at the National Snow and Ice Data Center (NSIDC). Michael works on a variety of software products in Ruby and JavaScript, and has taken a particular interest in the processing and visualization of data using Python. Paul enjoys working in the confluence of old-school and cutting-edge software technologies in support of science. Matt is a fluent translator between scientist and software and specializes in the processing and visualization of remotely sensed sea ice data.
Open Space is an approach that enables groups of any size to address complex, important issues and achieve meaningful results quickly. In Open Space meetings and events, participants create and manage their own agenda of parallel working sessions around a central theme of strategic importance. Conference participants will have the opportunity to apply Open Space technique to self-organize and discuss relevant topics with other meeting attendees. A space to write down ideas, questions, and discussion topics will be available throughout the first two days of the conference.
Authors: John C. Linford, Srinath Vadlamani, Sameer Shende, and Allen Malony
Speaker Description:
Dr. John Linford is a Scientist at ParaTools, Inc. He received his Ph.D. from Virginia Tech, where his dissertation on accelerating atmospheric modeling through emerging multi-core technologies was selected as the outstanding doctoral dissertation of 2010. John has developed a meta-programmer for chemical kinetic simulation, airborne signal processing applications, rotocraft engineering tools, and toolkits for porting parallel HPC applications to cloud computing platforms. John helps develop the TAU Performance System and has contributed to the Scalasca project and the MoinMoin project.
The North American Regional Climate Change Assessment Program (NARCCAP) is an international, multi-institution collaboration to simulate climate change over North America using high-resolution regional models driven by global models. The NARCCAP dataset has been used in a wide variety of research contexts, and is especially useful for impacts analysis.
Speaker Description:
Seth McGinnis is an Associate Scientist IV in the Institute for Mathematics Applied to Geosciences (IMAGe) at NCAR. As the Data Manager and User Community Manager for NARCCAP, the North American Regional Climate Change Assessment Program, he makes the output from climate models usable by and available to people who need information about climate change. His research focuses on bias correction, interpolation, and other issues affecting the practical use of model output by non-specialists.
R is a powerful open source and community supported programming environment first developed to satisfy the requirements of the Statistical community. Over the years R has developed into a general purpose language, and it has been applied to solve various problems across different disciplines. Powerful R packages are now available to analytically interact with spatial data in an efficient manner.
Students will be guided through hands-on experience working with extremes of data from very homogenized globally gridded data published by national centers to highly idiosyncratic (often unpublished) point source data.
Students will learn where to go for more information about data and data tools. Most importantly, they will examine which kinds of problems can be answered by data, which type(s) of data can help answer their questions, how to find that data, and the mechanics of how to use that data.
Speaker Description:
Grace Peng works in the Data Support Section.
Mary Haley works in the Data Visualization & Analysis Tools group
An innovative software framework is introduced as a tool to interactively visualize large three-dimensional datasets generated in real-time by high-resolution regional weather and climate models. The integrated system consists of a state-of-the-art, high-resolution coupled regional climate model, developed at Texas A&M University, and a combined, high-performance 3D visualization package and graphical user interface (GUI), developed by IBM Research.
Speaker Description:
Dr. Montuoro is an Instructional Assistant Professor in the Department of Atmospheric Sciences at Texas A&M University. He joined Texas A&M University in 2004, after working as IT consultant for Eutelsat SA in Paris, France. Dr. Montuoro holds a PhD in Theoretical Chemistry from the Scuola Normale Superiore di Pisa, Italy, and has developed innovative numerical models used for accurate calculations of photoionization phenomena. In 2010, some of his recent work in code optimization has been featured in the national press. Dr. Montuoro's current work focuses on the development of high-resolution coupled regional climate models and tools.
In climatology, the question of Detection and Attribution (D&A) is fairly well defined: ("Detection" is the process of demonstrating that climate has changed in some defined statistical sense, without providing a reason for that change and "Attribution" is the process of establishing the most likely causes for the detected change with some defined level of confidence, see the IPCC definition).
Speaker Description:
Philippe is Research Scientist at the “Laboratoire des Sciences du Climat et de l’Environnement”, in France. He specializes in statistical climatology and hydrology, extreme value theory, time series analysis, spatial statistics.
