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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Novel Solar Panel Defect Detection Hardware and Defect Analysis Software (CRADA Final Report)

The CRADA work involved 1) the development of a machine learning software architecture to automatically detect defects within Electroluminescence images of solar panels, and 2) demonstrations of the usefulness of UV Fluorescence (UVF) defect detection for a wide variety of defect types across many different solar panel technologies with different field or environmental chamber histories.

14 SOLAR ENERGY↗
DOE OSTI2025

Novel Solar Panel Defect Detection Hardware and Defect Analysis Software (CRADA Final Report)

The CRADA work involved 1) the development of a machine learning software architecture to automatically detect defects within Electroluminescence images of solar panels, and 2) demonstrations of the usefulness of UV Fluorescence (UVF) defect detection for a wide variety of defect types across many different solar panel technologies with different field or environmental chamber histories.

14 SOLAR ENERGY↗
NASA NTRS2011

Source Lines Counter (SLiC) Version 4.0

Source Lines Counter (SLiC) is a software utility designed to measure software source code size using logical source statements and other common measures for 22 of the programming languages commonly used at NASA and the aerospace industry. Such metrics can be used in a wide variety of applications, from parametric cost estimation to software defect analysis. SLiC has a variety of unique features such as automatic code search, automatic file detection, hierarchical directory totals, and spreadsheet-compatible output. SLiC was written for extensibility; new programming language support can be added with minimal effort in a short amount of time. SLiC runs on a variety of platforms including UNIX, Windows, and Mac OSX. Its straightforward command-line interface allows for customization and incorporation into the software build process for tracking development metrics. T

Monson, Erik W.↗
DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1990

An analysis of defect densities found during software inspections

Software inspection is a technical evaluation process for finding and removing defects in requirements, design, code, and tests. Detailed data was collected during the first three years of experience at JPL on 203 inspections. Statistics are discussed for this set of inspections. Included, on a per inspection basis, are averages of: staff time expended, pages covered, major defects found, minor defects found, and inspection team size. Analysis of variance showed a significantly higher density of defects during requirements inspections. It was also observed, that the defect densities found decreased exponentially as the work products approached the coding phase. Increasing the pace of the inspection meeting decreased the density of defects found. This relationship was observed to hold for both major and minor defect densities, although it was more pronounced for minor defects. Guidelines are provided for conducting successful software inspections based upon three years of JPL experience.

Kelly, John C.↗
DOE OSTI2023

CDB-AP: An application for coincidence Doppler broadening spectroscopy analysis

Coincidence Doppler Broadening (CDB) Positron Annihilation Spectroscopy (PAS) is a material analysis technique that can be used to non-destructively measure characteristics of structural defects in samples. Analyzing and comparing large datasets obtained using this technique, however, can be complicated and time intensive. The Coincidence Doppler Broadening Analysis Program (CDB-AP) is a graphical user interface that facilitates rapid analysis of many data files while using transparent processes. It is already used in three laboratories at Idaho National Laboratory and can be used in laboratories worldwide.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Defect measurement and analysis of JPL ground software: a case study

Ground software systems at JPL must meet high assurance standards while remaining on schedule due to relatively immovable launch dates for spacecraft that will be controlled by such systems. Toward this end, the Software Quality Improvement (SQI) project's Measurement and Benchmarking (M&B) team is collecting and analyzing defect data of JPL ground system software projects to build software defect prediction models. The aim of these models is to improve predictability with regard to software quality activities. Predictive models will quantitatively define typical trends for JPL ground systems as well as Critical Discriminators (CDs) to provide explanations for atypical deviations from the norm at JPL. CDs are software characteristics that can be estimated or foreseen early in a software project's planning. Thus, these CDs will assist in planning for the predicted degree to which software quality activities for a project are likely to deviation from the normal JPL ground system based on pasted experience across the lab.

defects↗
NASA NTRS2003

Patterns of software defect data on spacecraft

The identification of patterns of software defect data yields insights into improving the quality of both operational and future spacecraft. This paper describes the results of applying this technique to both post-launch and pre-launch spacecraft. It then describes four key challenges that remain to achieving fuller utilization of defect analysis in future systems.

software defect defect analysis defect data↗
DOE OSTI2021

Quantitative Risk Analysis of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants using IRADIC Technology

This report documents the activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2021 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) Risk Assessment project. In FY-2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems (IRADIC technology) was proposed for this strategy, which aims to (1) provide a best-estimate risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the High Safety Significant Safety-related (HSSSR) DI&C systems, (2) develop an advanced risk assessment technology to support transition from analog to DI&C technologies for nuclear industry, (3) assure the long-term safety and reliability of vital HSSSR DI&C systems, (4) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the IRADIC technology is instructive for nuclear vendors and utilities to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify responding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the individual level, system level, and plant level, (4) providing insights and suggestions on designs to manage the risks; thus, to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). In this report, an approach for performing software CCF analysis, given limited data, is developed and demonstrated using a case study of a highly redundant digital reactor trip system. Consequence analysis is also performed based on different accident scenarios. Results indicate that plant modernization including the improvement of HSSSR DI&C systems will make great benefits to plant safety by providing more safety margins to accident management. In addition, a novel approach is proposed in this report for the quantification of software hazards when sufficient operational and testing data available. The method incorporates software development quality as well as strong analysis techniques to identify and link software defects to potential failure modes. The approach includes both semantic and test-based analysis to detect failures that can exist in different stages of the software development life cycle. This method is applied to an advanced human system interface relevant to reactor trip safety developed from the APR 1400 design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2011

Toward Intelligent Software Defect Detection

Source code level software defect detection has gone from state of the art to a software engineering best practice. Automated code analysis tools streamline many of the aspects of formal code inspections but have the drawback of being difficult to construct and either prone to false positives or severely limited in the set of defects that can be detected. Machine learning technology provides the promise of learning software defects by example, easing construction of detectors and broadening the range of defects that can be found. Pinpointing software defects with the same level of granularity as prominent source code analysis tools distinguishes this research from past efforts, which focused on analyzing software engineering metrics data with granularity limited to that of a particular function rather than a line of code.

Benson, Markland J.↗
NASA NTRS2011

Independent Verification and Validation (IV and V) - Adding Mission Assurance to NASA Flight Software

The NASA Independent Verification and Validation (IV&V) Facility objective is to identify potential defects in flight software using independent analysis techniques. This paper describes the tailored IV&V techniques that have been developed in support of critical interactions on the Mars Science Laboratory (MSL) project, scheduled to launch in November, 2011. The IV&V techniques for interface analysis use independently developed sequence diagrams of critical scenarios. The results from these analyses have had a positive impact on the requirements flow down, consistency amongst MSL requirements and identification of missing requirements. The results of these analyses and the positive impact to the MSL project are provided.

performance evaluation↗
NASA NTRS2003

Low Temperature SQUID for NDE Applications

We have developed a low temperature SuperConducting Quantum Interference Device - SQUID measurement system for detection of defects deep under the surface of aluminum structures using eddy current techniques. The system uses a two dimensional planar inducer with two different excitation frequencies to induce a current in the sample. We have developed a data analysis software program that enabled us to distinguish between round defects (holes), straight defects (slots) and slots close to holes simulating cracks starting from rivets in aluminum structures. We were able to detect defects that are 8mm below the surface. We have also measured the change in phase of the detected signal as a function of depth of the defect. This relationship can be used to determine the depth of hidden flaws. Using this analysis software with the high temperature SQUID system at NASA Langley we were able to detect slots close to holes in layered aluminum sample.

Wincheski, Buzz↗