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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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NASA NTRS2000

ISS Fiber Optic Failure Investigation Root Cause Report

In August of 1999, Boeing Corporation (Boeing) engineers began investigating failures of optical fiber being used on International Space Station flight hardware. Catastrophic failures of the fiber were linked to a defect in the glass fiber. Following several meetings of Boeing and NASA engineers and managers, Boeing created and led an investigation team, which examined the reliability of the cable installed in the U.S. Lab. NASA Goddard Space Flight Center's Components Technologies and Radiation Effects Branch (GSFC) led a team investigating the root cause of the failures. Information was gathered from: regular telecons and other communications with the investigation team, investigative trips to the cable distributor's plant, the cable manufacturing plant and the fiber manufacturing plant (including a review of build records), destructive and non-destructive testing, and expertise supplied by scientists from Dupont, and Lucent-Bell Laboratories. Several theories were established early on which were not able to completely address the destructive physical analysis and experiential evidence. Lucent suggested hydrofluoric acid (HF) etching of the glass and successfully duplicated the "rocket engine" defect. Strength testing coupled with examination of the low strength break sites linked features in the polyimide coating with latent defect sites. The information provided below explains what was learned about the susceptibility of the pre-cabled fiber to failure when cabled as it was for Space Station and the nature of the latent defects.

Leidecker, Henning↗

NASA NTRSDate not supplied

Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.

quantum machine learning↗

NASA NTRS2000

Tropical Rainfall Measuring Mission (TRMM) Satellite: Assessment and Lessons Learned After Three Flight Years

Tropical Rain Measuring Mission (TRMM) is an experiment in measuring rainfall and the associated latent heat release from space. A primary goal is to help in initializing the large-scale weather and climate models for crucial improvement in location and profile of atmospheric heat release. For this, precipitation and latent heating profiles are needed. This goal requires cloud-resolving models. The basic approach was to use passive microwave and rain radar in combination to issue a limited number of products, improving the retrieval algorithms by testing during flight, so the products are updated annually. Despite a tight budget, the TRMM observatory and data system worked excellently from launch for the past three years. A basic philosophy has been to do physical validation as much as possible, in preference to empirical adjustments for algorithm improvement. Additional information is contained in the original extended abstract.

Simpson, Joanne↗

NASA NTRS1986

Adaptation and readaptation medical concerns of a Mars trip

The ability of the human body to adapt to microgravity environments and to later readapt to a gravity environment was examined. Issues specifically relating to the effects of long duration space flight on the adaptation/readaptation process are discussed. The need for better health prediction techniques is stressed in order to be able to better anticipate crew health problems and to perform corrective actions. Several specific examples are discussed of latent diseases which could occur during a long duration space mission, even after having subjected the crew to thorough premission checkups. The need for learning how to prevent or ameliorate such problems as space adaptation syndrome, bone and muscle (and possibly tissue) atrophy, immune system atrophy, and heart arrythmias is also discussed. The implications of the age of the crew, the influence of an onboard low level gravity field, and drugs are briefly addressed as factors in the adaptation/readaptation process.

Johnson, Philip C.↗

NASA NTRSDate not supplied

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗

NASA NTRSDate not supplied

Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2

Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.

Europa↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗

NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗

NASA NTRS2016

Topic Modeling of NASA Space System Problem Reports: Research in Practice

Problem reports at NASA are similar to bug reports: they capture defects found during test, post-launch operational anomalies, and document the investigation and corrective action of the issue. These artifacts are a rich source of lessons learned for NASA, but are expensive to analyze since problem reports are comprised primarily of natural language text. We apply topic modeling to a corpus of NASA problem reports to extract trends in testing and operational failures. We collected 16,669 problem reports from six NASA space flight missions and applied Latent Dirichlet Allocation topic modeling to the document corpus. We analyze the most popular topics within and across missions, and how popular topics changed over the lifetime of a mission. We find that hardware material and flight software issues are common during the integration and testing phase, while ground station software and equipment issues are more common during the operations phase. We identify a number of challenges in topic modeling for trend analysis: 1) that the process of selecting the topic modeling parameters lacks definitive guidance, 2) defining semantically-meaningful topic labels requires nontrivial effort and domain expertise, 3) topic models derived from the combined corpus of the six missions were biased toward the larger missions, and 4) topics must be semantically distinct as well as cohesive to be useful. Nonetheless,topic modeling can identify problem themes within missions and across mission lifetimes, providing useful feedback to engineers and project managers.

Data Mining↗

NASA NTRS2000

The Tropical Rainfall Measuring (TRMM) - What Have We Learned and What Does the Future Hold?

Rainfall is important in the hydrological cycle and to the lives and welfare of humans. In addition to being a life-giving resource, rainfall processes also plays a crucial role in the dynamics of the global atmospheric circulation. Three-fourths of the energy that drives the atmospheric wind circulation comes from the latent heat released by tropical precipitation. It varies greatly in space and time. The rain-producing cloud systems may last several hours or days. Their dimensions range from 10 km to several hundred km. This makes it difficult to incorporate rainfall directly large-scale weather and climate models. Until the end of 1997, precipitation in the global tropics was not known to within a factor of two. Regarding "global warming", the various large-scale models differed among themselves in the predicted magnitude of the warming and in the expected regional effects of these temperature and moisture changes. The Tropical Rainfall Measuring Mission (TRMM) satellite has yielded important interim results related to rainfall observations, data assimilation and model forecast skills when rainfall data is assimilated. This talk will summarize where the TRMM science team is with regards to answering some of these important scientific challenges, as well as discuss the future Global Precipitation Mission which will provide 3 hourly rainfall coverage and offers some unique collaborative potential for NOAA and NASA.

Kummerow, C.↗

NASA NTRS2017

Integrated Systems Health Management for Sustainable Habitats (Using Sustainability Base as a Testbed)

Habitation systems provide a safe place for astronauts to live and work in space and on planetary surfaces. They enable crews to live and work safely in deep space, and include integrated life support systems, radiation protection, fire safety, and systems to reduce logistics and the need for resupply missions. Innovative health management technologies are needed in order to increase the safety and mission-effectiveness for future space habitats on other planets, asteroids, or lunar surfaces. For example, off-nominal or failure conditions occurring in safety-critical life support systems may need to be addressed quickly by the habitat crew without extensive technical support from Earth due to communication delays. If the crew in the habitat must manage, plan and operate much of the mission themselves, operations support must be migrated from Earth to the habitat. Enabling monitoring, tracking, and management capabilities on-board the habitat and related EVA platforms for a small crew to use will require significant automation and decision support software.Traditional caution and warning systems are typically triggered by out-of-bounds sensor values, but can be enhanced by including machine learning and data mining techniques. These methods aim to reveal latent, unknown conditions while still retaining and improving the ability to provide highly accurate alerts for known issues. A few of these techniques will briefly described, along with performance targets for known faults and failures. Specific system health management capabilities required for habitat system elements (environmental control and life support systems, etc.) may include relevant subsystems such as water recycling systems, photovoltaic systems, electrical power systems, and environmental monitoring systems. Sustainability Base, the agency's flagship LEED-platinum certified green building acts as a living laboratory for testing advanced information and sustainable technologies that provides an opportunity to test novel machine learning and controls capabilities. In this talk, key features of Sustainability Base that make it relevant to deep space habitat technology and its use of these kinds of subsystems previously listed will be presented. The fact that all such systems require less power to support human occupancy can be used as a focal point to serve as a testbed for deep space habitats that will need to operate within finite energy budgets.

Systems Health Management↗

NASA NTRS2020

A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealing

A class of quantum-classical hybrid machine-learning algorithms can be obtained by integrating classical deep generative models with quantum probability distributions as 'priors' over their latent variables. We introduce a hybrid implementation of variational autoencoders (QVAE) and also present a technique to hybridize flow-based invertible generative models. We demonstrate the use of D-Wave quantum annealers as physical simulators of quantum Boltzmann machines (QBM) to perform quantum-assisted training of QVAE. Latent-space QBM develop slowly mixing modes, opening a path to obtain quantum advantage in generative modeling with available quantum devices.

Vinci, Walter↗

NASA NTRSDate not supplied

ES2Vec: Earth Science Metadata Suggestions and Analogical Reasoning

As the volume of text-based Earth science research grows, it is increasingly possible to discover latent relationships in the literature. However, traditional methodologies are restricted by limited computational capabilities and intractable problem spaces. Advancements in natural language processing (NLP) have allowed us to use an extensive Earth science corpus to create a domain-specific word vector model, Es2Vec, which we have used to surface latent relationships between Earth science concepts and generate improved keyword tags. Earth science metadata keyword assignment is a challenging problem. Dataset curators select appropriate keywords from the Global Change Master Directory (GCMD) set of keywords. The keywords an are integral part of the search and discovery of these datasets. Hence, the selection of keywords is crucial to increasing the discoverability of datasets. Utilizing machine learning techniques, we provide users with automated keyword suggestions to complement manual selection. We trained a machine learning model that leverages the semantic embedding ability of Word2Vec models to process abstracts and suggest relevant keywords. A user interface tool we built to assist data curators in the assignment of such keywords is also described.

word vectors↗

NASA NTRSDate not supplied

High dimensional similarity search with quantum assisted variational autoencoder

Recent progress in quantum algorithms and hardware is indicator of the potential importance of quantum computing in the next future. However, finding suitable application areas remains an active area of research. Quantum machine learning [1] is touted as a potential approach to demonstrate quantum advantage within both the gate-model [2,3] and the adiabatic [4,5] schemes. For instance, the Quantum-assisted Variational Autoencoder (QVAE) [6] has been proposed as a quantum enhancement to the discrete VAE [7]. We extend on previous work and study the real-world applicability of a QVAE, specifically, for similarity search in large-scale high dimensional datasets. While similarity search algorithms are available for low dimensional datasets, scaling to billion-scale datasets with thousands of dimensions is non-trivial. We show how the latent-space representation of a QVAE can be used to construct a space-efficient search index. We back up our claims by experimental results which show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we show real-world speedups compared to linear search and demonstrate memory efficient scaling to large-scale datasets.

Nicholas D Gao↗

NASA NTRS1993

Melt spinning study

Containerless processing of materials provides an excellent opportunity to study nucleation phenomena and produce unique materials, primarily through the formation of metastable phases and deep undercoolings. Deep undercoolings can be readily achieved in falling drops of molten material. Extended solute solubilities and greatly refined microstructures can also be obtained in containerless processing experiments. The Drop Tube Facility at Marshall Space Flight Center has played an important role in enhancing that area of research. Previous experiments performed in the Drop Tube with refractory metals has shown very interesting microstructural changes associated with deep undercoolings. It is apparent also that the microstructure of the deep undercooled species may be changing due to the release of the latent heat of fusion during recalescence. For scientific purposes, it is important to be able to differentiate between the microstructures of the two types of metallic species. A review of the literature shows that although significant advances have been made with respect to the engineering aspects of rapid solidification phenomena, there is still much to be learned in terms of understanding the basic phenomena. The two major ways in which rapid solidification processing provides improved structures and hence improved properties are: (1) production of refined structures such as fine dendrites and eutectics, and (2) production of new alloy compositions, microstructures, and phases through extended solid solubility, new phase reaction sequences, and the formation of metallic-glass microstructures. The objective of this work has been to determine the optimal methodology required to extract this excess energy without affecting the thermo-physical parameters of the under-cooled melt. In normal containerless processing experiments recalescence occurs as the melt returns toward the melting point in order to solidify. A new type of experiment is sought in which the resultant microstructure of the undercooled species is frozen in without going through the melting point regime and subsequent near equilibrium solidification of the remaining liquid. This experimental approach entails the design of an appropriate melt spinning system which is compatible with Drop Tube operations and processing constraints. That work is the goal of this study.

Workman, Gary L.↗

NASA NTRS2018

Aeronautics Autonomy Testbed Capability (AATC) Team Developed Concepts

In 2015, the National Aeronautics and Space Administration (NASA) formed a multi-center, interdisciplinary team of engineers from three different aeronautics research centers who were tasked with improving NASA autonomy research capabilities. This group was subsequently named the Aeronautics Autonomy Testbed Capability (AATC) team. To aid in confronting the autonomy research directive, NASA contracted IDEO, a design firm, to provide consultants and guides to educate NASA engineers through the practice of design thinking, which is an unconventional method for aerospace design processes. The team then began learning about autonomy research challenges by conducting interviews with a diverse group of researchers and pilots, military personnel and civilians, experts and amateurs. Part of this design thinking process involved developing ideas for products or programs known as concepts that could enable real world fulfillment of the most important latent needs identified through analysis of the interviews. The concepts are intended to be sacrificial, intermediate steps in the design thinking process and are presented in this report to record the efforts of the AATC group. Descriptions are provided in present tense to allow for further ideation and imagining the concept as reality as was attempted during the teams discussions and interviews. This does not indicate that the concepts are actually in practice within NASA though there may be similar existing programs independent of AATC. These concepts were primarily created at two distinct stages during the design thinking process. After the initial interviews, there was a workshop for concept development and the resulting ideas are shown in this work as from the First Round. As part of succeeding interviews, the team members presented the First Round concepts to refine the understanding of existing research needs. This knowledge was then used to generate an additional set of concepts denoted as the Second Round. Some concepts were created by a single person in a few minutes while others were refined by the entire team over several weeks. Thus, certain ideas are more detailed than others but those from the second round are not necessarily more comprehensive than the first round concepts. Primarily, as reported here in the Second Round section, the designs serve to encompass more of the high level end user research needs which were not necessarily known to the team during the prior workshop. In the figures provided throughout this report, illustrations are often provided to represent a concept. Nearly all of the images are informal sketches or renderings and this casualness should, hopefully, not be held to negate the potential insights available within the concepts.

Smith, Phillip J.↗

NASA NTRS2005

Failure Assessment

Three questions to which software developers want accurate, precise answers are "How can the software system fail?", "mat bad things will happen if the software fails?t', and "How many failures will the software experience?". Numerous techniques have been devised to answer these questions; three of the best known are: 1) Software Fault Tree Analysis (SFTA) 2) Software Failure Modes, Effects, and Criticality Analysis (SFMECA 3) Software Fault/Failure Modeling. SFTA and SFMECA have been successfully used to analyze the flight software for a number of robotic planetary exploration missions, including Galileo, Cassini, and Deep Space 1. Given the increasing interest in reusing software components from mission to mission, one of us has developed techniques for reusing the corresponding portions of the SFTA and SFMECA, reducing the effort required to conduct these analyses. SFTA has also been shown to be effective in analyzing the security aspects of software systems; intrusion mechanisms and effects can easily be modeled using these techniques. The Bi- Directional Safety Analysis (BDSA) method combines a forward search (similar to SFMECA) from potential failure modes to their effects, with a backward search (similar to SFTA) from feasible hazards to the contributing causes of each hazard. BDSA offers an efficient way to identify latent failures. Recent work has extended BDSA to product-line applications such as flight-instrumentation displays and developed tool support for the reuse of the failure-analysis artifacts within a product line. BDSA has also been streamlined to support those projects having tight cost and/or schedule constraints for their failure analysis efforts. We discuss lessons learned from practice, describe available tools, and identi@ some future directions for the topic. A substantial amount of research has been devoted to estimating the number of failures that a software system will experience during test and operations, as well as the number of faults that have been inserted into that system during its development. One of us has found that the amount of structural change to a system during its development is strongly related to the number of faults inserted into it. Using techniques requiring no additional effort on the part of the development organization, the required measurements of structural evolution can be easily obtained from a development effort's configuration management system and readily transformed into an estimate of fault content. So far, structure-fault relationships have been identified for source code; current work seeks to examine artifacts available earlier in the lifecycle to determine if similar relationships between structure and fault content can be found. In particular, relationships between requirements change requests and the number of faults inserted into the implemented system would provide a significant improvement in our ability to control software quality during the early development phases.

fault tree↗