PSIP Toolkit: A Lightweight Process for Incremental Software Process Improvement.
Abstract not provided.
Raybourn, Elaine M.↗
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Lightweight Software Process Improvement Using Productivity and Sustainability Improvement Planning (PSIP)
Productivity and Sustainability Improvement Planning (PSIP) is a lightweight, iterative workflow that allows software development teams to identify development bottlenecks and track progress to overcome them. In this paper, we present an overview of PSIP and how it compares to other software process improvement (SPI) methodologies, and provide two case studies that describe how the use of PSIP led to successful improvements in team effectiveness and efficiency.
Heroux, Michael A.↗
Solar Automated Permit Processing Software for Distributed PV (Cooperative Research and Development Final Report, CRADA Number CRD-19-00825)
NREL and its project partners built and commercialized the novel Solar Automated Permitting Processing Plus (SolarAPP+) software platform that delivers instant residential rooftop solar permits. The tool provides installers with a standard portal for entering permit information for residential solar systems across all Authorities Having Jurisdiction (AHJs) using SolarAPP+, thereby providing a streamlined plan review form, transparency into permitting timelines, and required specifications. The project has proven to reduce permitting timelines from as many as 20 business days to zero, delivering solar projects faster in the four participating AHJs using the platform. Streamlining permitting timelines, all else equal, results in consumers installing their systems and reducing their electricity bills faster. The continued expansion of SolarAPP+ features and adoption nationwide will expand the benefits of the SolarAPP+ beyond the current user base.
14 SOLAR ENERGY↗
Fibro: a fiber optic data processing software for unconventional reservoirs
Various embodiments of the present disclosure are directed to systems and methods for data collection using fiber-optic cable in a well, and analysis of the data to determine instantaneous frequency, instantaneous phase, instantaneous amplitude, and/or dominant frequency. These measures can be used to determine parameters associated with the operation of the well. The parameters can be used to control the operation of the well and/or the fracturing process.
Ghahfarokhi, Payam Kavousi↗
Enabling Developer Productivity with Software Process Improvement.
Abstract not provided.
Milewicz, Reed↗
Unit Testing Data Processing Software for LANL-GEO SOPA [Slides]
Abstract not provided.
97 MATHEMATICS AND COMPUTING↗
PSIP For HDF5 Pilot Project (Final Report)
Productivity and Sustainability Improvement Planning (PSIP) is a lightweight, incremental and iterative approach (much in the same spirit as Agile methodologies) for making routine software process improvements in software projects. It is designed to be easily applied in existing development workflows. Quoting from a November 2019 workshop paper describing PSIP. PSIP breaks from classic software process improvement approaches such as CMM(I), SPICE, ISO 9000 or Six Sigma, in that it trades comprehensive standards and certification-driven assessment for self-defined, internally driven goals. It does, however, carry forward such ideas as having staged models of improvement (like CMM(I)) in the form of progress tracking cards. Additionally, PSIP is more aligned with lean and agile methods; it adopts their emphasis on iterative improvement and continuous learning. At its core, PSIP is an instantiation of the Plan-Do-Check-Act management cycle (PDCA, also known as Plan-Do-Study-Adjust) which provides the foundation for much of the modern software process improvement literature. This situates PSIP within a constellation of bottom-up, inductive software process improvement methods. PSIP is designed around the notion that already overburdened teams can define and carry out a series of small, incremental steps of progression towards improvement goals without significant (there will be some, but the goal is to avoid significant) disruption to ongoing development activities. A key enabling tool in PSIP is the use of Progress Tracking Cards (PTCs) which define the steps of progression towards a given improvement goal. This is the theory of PSIP. The PSIP for HDF5 project was aimed at putting PSIP into practice with the purpose of evaluating its effectiveness in planning and facilitating quality and process improvements in a scientific software project as well as its associated artifacts. The HDF5 project was chosen as a use case to evaluate PSIP for several reasons. First, NNSA labs and LLNL in particular have a keen interest in how HDF5 quality impacts its uptake and sustainability as a community adopted and supported code. Next, HDF5 is a foundational library, a key substrate in the HPC/CSE software stack, and any improvements there realized through this contract will have benefits to many DOE applications depending on it. HDF5 also represents an older, legacy code with technical debts to pay down. These characteristics are similar to many NNSA and even some ECP code projects. But, because HDF5 is an I/O library, it represents a simpler use case within which to study PSIP than a full-fledged and significantly more complex PDE simulation code. We believe these attributes make HDF5 an ideal use case for evaluating PSIP.
97 MATHEMATICS AND COMPUTING↗
Software Deployment Process at NERSC: Deploying the Extreme-scale Scientific Software Stack (E4S) Using Spack at the National Energy Research Scientific Computing Center (NERSC)
One of the many benefits of using a high-performance computing (HPC) system at a Department of Energy (DOE) Office of Science (SC) HPC facility is the large number of software products, built and optimized for the system. The HPC center staff and HPC vendors provide optimized software such as libraries and even full scientific applications, ready to be used by users as building blocks to accelerate scientific discovery. Behind each provided packaged software module are a large number of decisions - which compiler, optimizations, variants/options - to build the software on the target system. And, even before the software gets deployed, the software must be developed, tested, and maintained, including deprecating old versions and ensuring compatibility across versions. The software lifecycle is complex and is further convoluted by a web of interdependencies on other software.
97 MATHEMATICS AND COMPUTING↗
Development and performance evaluation of a novel γ-γ coincidence analysis software
Processing of large amounts of data from a γ-γ coincidence system is a practical challenge in trace-level radionuclide measurements. Given the benefits and growing number of multidimensional gamma spectrometers worldwide, this is a major restriction in wide-spread applicability of coincidence techniques. This study demonstrates a novel γ-γ coincidence analysis software developed at Pacific Northwest National Laboratory, USA. The software utilizes advanced search techniques and Python libraries to readily identify coincidence signatures in the large data. Further, using both synthetic and experimental data, the study uncovers various strategies/libraries employed during software’s development. This work aims to provide a reference document in handling large data routinely encountered in trace-level measurements.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
An Agile/XP software development process for modernizing the accelerator control system at Fermilab
Fermilab is undergoing the most ambitious upgrade to its accelerator control system of the 21st century. As part of the ACORN project, hundreds of legacy control system applications written in C/C++ will be re-imagined and developed from the ground up. In addition, applications to support Fermilab’s new super-conducting linear accelerator are already under construction. To manage the development of modern controls applications, the Controls department has adopted an Agile software development process based on eXtreme Programming. In this paper we will describe our process and detail our experience applying it to the development of two case studies.
Diamond, John [Fermilab]↗
Fire Risk Investigation in 3D (FRI3D) Software and Process for Integrated Fire Modeling
Modeling and implementing fire safety for nuclear power plants is a costly activity. Because of the complexity of fire phenomena and multiple operational procedures, it is difficult to computationally provide assurance that mitigation methods are adequate for critical areas using current analysis methods in a cost-effective manner. The nuclear industry needs efficient methods to provide more accurate modeling and optimize mitigation methods to improve nuclear power plant understanding for fire scenarios. This report describes the work done over the last two year on the Fire Risk Investigation in 3D (FRI3D) software. This software simplifies the modeling process by importing existing plant models and data, incorporating a 3D modeling environment and coupling with the fire simulation software CFAST. This eliminates the manual labor and errors associated with much of the fire modeling process. The report lays out the status and path of FRID software for the Risk Informed Safety Analysis pathway of the Light Water Reactor Sustainability project at Idaho National Laboratory and industry collaborators to best help reduce costs and improve realism in fire probabilistic risk analysis.
97 MATHEMATICS AND COMPUTING↗
Impacting Software Quality and Process Through the Extreme-Scale Scientific Software Stack (E4S) and Software Development Kit (SDK) Projects.
Abstract not provided.
Willenbring, James↗
Combining In-situ Diagnostics and Data Analytics for Discovery of Process-Structure-Property Relationships in AM parts – A Step Toward Digital Twins
In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.
36 MATERIALS SCIENCE↗
Spiking Markov Reward Process v.0.1
SAND2024-11150O The Spiking Markov Reward Process software is a spiking neural network that streams binary arithmetic and computes the state value function of a Markov reward process. The software will be released to the SpiNNcloud group for development of neuromorphic acceleration. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Wang, Felix↗
WFIP3 - BLOC site - CU Profiling Lidar (Windcube v1) / Raw data
This dataset is from WFIP3 BLOC site WindCube v1 Profiling Lidar / Raw Data from CU Boulder. Please note that the .sta files are in human-readable text, while the .rtd files require some processing software that is not easily automated. We plan to process the .rtd data following the campaign, but if you wish to try it sooner, we can make it available upon request.
17 WIND ENERGY↗
WFIP3 - BLOC site - CU Profiling Lidar (Windcube v1) / Raw data
This dataset is from WFIP3 BLOC site WindCube v1 Profiling Lidar / Raw Data from CU Boulder. Please note that the .sta files are in human-readable text, while the .rtd files require some processing software that is not easily automated. We plan to process the .rtd data following the campaign, but if you wish to try it sooner, we can make it available upon request.
17 WIND ENERGY↗
WFIP3 - BLOC site - CU Profiling Lidar (Windcube v1) / Raw data
This dataset is from WFIP3 BLOC site WindCube v1 Profiling Lidar / Raw Data from CU Boulder. Please note that the .sta files are in human-readable text, while the .rtd files require some processing software that is not easily automated. We plan to process the .rtd data following the campaign, but if you wish to try it sooner, we can make it available upon request.
17 WIND ENERGY↗