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All Projects

  •  Explainable AI: Why Did the Robot Do That?

    Explainable AI: Why Did the Robot Do That?

    Autonomy and Counter-Autonomy

    To help human users trust their robot team members in critical situations, we develop tools that allow autonomous systems to explain their behavior.

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  •  Verifying Distributed, Adaptive Real-Time (DART) Systems

    Verifying Distributed, Adaptive Real-Time (DART) Systems

    Autonomy and Counter-Autonomy Mission Assurance System Verification and Validation

    Distributed, adaptive real-time (DART) systems must satisfy safety-critical requirements. We developed a method to verify DART systems and generate assured code.

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  •  Multi-Agent Decentralized Planning for Adversarial Robotic Teams

    Multi-Agent Decentralized Planning for Adversarial Robotic Teams

    Autonomy and Counter-Autonomy

    We created multi-agent planning techniques, middleware, and algorithms that enable single users to manage fleets of UASs in real-world environments with changing adversaries.

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  •  QUELCE: Quantifying Uncertainty in Early Lifecycle Cost Estimation

    QUELCE: Quantifying Uncertainty in Early Lifecycle Cost Estimation

    Data Modeling and Analytics Software Engineering and Information Assurance

    Costs for large new systems are hard to estimate. We developed a method to quantify uncertainty and increase confidence in a program's cost estimate.

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  •  Automated Code Repair

    Automated Code Repair

    Autonomy and Counter-Autonomy Software Engineering and Information Assurance Cybersecurity

    Finding security flaws in source code is daunting; fixing them is an even greater challenge. Our researchers are creating automated tools that can repair bugs automatically or by prompting developers for more information to make effective repairs.

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  •  Using Automation to Prioritize Alerts from Static Analysis Tools

    Using Automation to Prioritize Alerts from Static Analysis Tools

    System Verification and Validation Cybersecurity

    The new CERT method for validating and repairing defects found by static analysis tools helps auditors and coders address more alerts with less effort.

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  •  Improving Verification with Parallel Software Model Checking

    Improving Verification with Parallel Software Model Checking

    System Verification and Validation

    Current methods for software model checking can take too much time. We develop algorithms for SMC that execute many operations in parallel to improve scalability.

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  •  Design Pattern Recovery from Malware Binaries

    Design Pattern Recovery from Malware Binaries

    Software Engineering and Information Assurance Cybersecurity

    The U.S. Department of Defense (DoD) and industry face many malware problems. CERT researchers automate malware analysis capabilities, including those focused on malware family evolution and similarity.

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  •  Supporting the U.S. Army's Joint Multi-Role Technology Demonstrator Effort

    Supporting the U.S. Army's Joint Multi-Role Technology Demonstrator Effort

    Software Engineering and Information Assurance System Verification and Validation

    We build and analyze virtual software systems to find problems early in development, before a system is built. Early discovery reduces cost and certification time.

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  •  Automating Vulnerability Discovery in Critical Applications

    Automating Vulnerability Discovery in Critical Applications

    Software Engineering and Information Assurance Cybersecurity

    CERT researchers develop automated tools that discover and mitigate software vulnerabilities and transfer them to researchers, procurement specialists, and software vendors.

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  •  Converting a Navy Weapon System from a 32- to a 64-Bit Architecture

    Converting a Navy Weapon System from a 32- to a 64-Bit Architecture

    Mission Assurance Software Engineering and Information Assurance

    The SEI provided an independent assessment of the risks of migrating a weapons control system deployed by the U.S. Navy from one architecture to another.

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  •  GraphBLAS: A Programming Specification for Graph Analysis

    GraphBLAS: A Programming Specification for Graph Analysis

    Mission Assurance

    The GraphBLAS Forum is a world-wide consortium of researchers working to develop a programming specification for graph analysis that will simplify development.

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  •  Positive Incentives for Reducing Insider Threat

    Positive Incentives for Reducing Insider Threat

    Cybersecurity

    Insiders present unique challenges to cybersecurity. We research insider threats and develop tools to analyze threat indicators in sociotechnical networks.

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  •  Managing Technical Debt with Data-Driven Analysis

    Managing Technical Debt with Data-Driven Analysis

    Data Modeling and Analytics Software Engineering and Information Assurance

    Most software projects carry technical debt. We develop tools and techniques that identify it and provide a complete view of the debt that you need to manage.

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  •  A Tool Set to Support Big Data Systems Acquisition

    A Tool Set to Support Big Data Systems Acquisition

    Data Modeling and Analytics

    We offer an approach that reduces risk and simplifies the selection and acquisition of big data technologies when you acquire and develop big data systems.

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  •  Helping Government Realize the Agile Advantage

    Helping Government Realize the Agile Advantage

    Software Engineering and Information Assurance Mission Assurance

    We develop a wealth of resources to help the U.S. Department of Defense (DoD) and federal agencies make informed decisions about using Agile and lean approaches in achieving their goals.

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  •  Security-Aware Acquisition

    Security-Aware Acquisition

    Cybersecurity

    The techniques developed by CERT researchers help you evaluate and manage cyber risk in today’s complex software supply chains.

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  •  System and Platform Evaluation

    System and Platform Evaluation

    Cybersecurity

    CERT researchers develop and perform advanced penetration testing and cyber vulnerability assessments of organizations' systems and platforms.

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  •  Empirical Research Office

    Empirical Research Office

    Data Modeling and Analytics

    We improve the capability delivered for every dollar of U.S. Department of Defense (DoD) investment made in software systems by improving the use of data in decision making.

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  •  Digital Forensics: Advancing Solutions for Today's Escalating Cybercrime

    Digital Forensics: Advancing Solutions for Today's Escalating Cybercrime

    Cybersecurity

    As cybercrime proliferates, CERT researchers help law enforcement investigators process digital evidence with courses, methodologies and tools, skills, and experience.

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  •  Acquiring Systems, Not Just Software

    Acquiring Systems, Not Just Software

    Data Modeling and Analytics

    The U.S. Department of Defense (DoD) and federal agencies are increasingly acquiring software-intensive systems instead of building them with internal resources. However, acquisition programs frequently have difficulty identifying the critical software acquisition activities, deliverables, risks, and opportunities.

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  •  USPS Case Study

    USPS Case Study

    The SEI teamed with the U.S. Postal Service to help it improve its cybersecurity and resilience and collaborated on a program to develop a strong cybersecurity workforce.

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  •  Cyber Lightning Case Study

    Cyber Lightning Case Study

    The SEI hosted Cyber Lightning, a three-day joint training exercise involving Air National Guard and Air Force Reserve units from western Pennsylvania and eastern Ohio.

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  •  SEI Hosts Crisis Simulation Exercise for Cyber Intelligence Research Consortium

    SEI Hosts Crisis Simulation Exercise for Cyber Intelligence Research Consortium

    Mission Assurance

    In SEI crisis simulation exercises, participants use scenarios that present fictitious malicious actors and environmental factors based on real-world events.

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  •  Runtime Assurance for Big Data Systems

    Runtime Assurance for Big Data Systems

    System Verification and Validation

    To help assure runtime performance in big data systems, we designed a reference architecture to automatically generate and insert monitors and aggregate metric streams.

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  •  Building Security into Application Lifecycles

    Building Security into Application Lifecycles

    Software Engineering and Information Assurance Cybersecurity

    Cybersecurity Engineering (CSE) prepares program managers, engineers, developers, educators, and others to better approach the acquisition, development, validation, and sustainment of software so they can address known and emerging patterns of software failure, misuse, and abuse.

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  •  Smart Grid Maturity Model (SGMM)

    Smart Grid Maturity Model (SGMM)

    Software Engineering and Information Assurance

    The smart grid is a constantly evolving infrastructure of digital technology and power industry practices for improving the management of electricity generation, transmission, and distribution. The Smart Grid Maturity Model (SGMM) helps utilities plan their smart grid journeys.

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  •  Developing Tomorrow’s Solutions for Improving Cyber Simulations

    Developing Tomorrow’s Solutions for Improving Cyber Simulations

    The CERT Division of the SEI develops tools that virtualize systems to deliver high-quality training and user performance validation to ensure cyber teams are ready to face ever-evolving threats and challenges.

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  •  Training Army Analysts to Use the Big Data Platform

    Training Army Analysts to Use the Big Data Platform

    Data Modeling and Analytics

    ARCYBER is teaming with the SEI CERT Division to create training capabilities that help Army analysts develop the necessary skills for using its Big Data Platform.

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  •  Cyber Intelligence Study

    Cyber Intelligence Study

    Cybersecurity

    The practice of cyber intelligence helps organizations protect their assets, know their risks, and recognize opportunities. In 2018, the SEI conducted a cyber intelligence study on behalf of the United States Office of the Director of National Intelligence (ODNI). Our task was to understand how organizations perform the work of cyber intelligence throughout the United States.

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  •  Delivering Real-World Experience with Cyber Simulations

    Delivering Real-World Experience with Cyber Simulations

    The SEI CERT Division develops simulations that offer cyber operators a way to get the experience they need to perform at elite levels.

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  •  Architecture Analysis and Design Language

    Architecture Analysis and Design Language

    Software Engineering and Information Assurance

    Software for mission- and safety-critical systems, such as avionics systems in aircraft, is growing larger and more expensive. The Architecture Analysis and Design Language (AADL) addresses common problems in the development of these systems, such as mismatched assumptions about the physical system, computer hardware, software, and their interactions that can result in system problems detected too late in the development lifecycle.

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  •  Designing Trustworthy Artificial Intelligence

    Designing Trustworthy Artificial Intelligence

    The Human-Machine Teaming Framework guides development in creating Artificial Intelligence systems that are accountable to humans, cognitive of speculative risks and benefits, secure, and usable.

     

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  •  Learning Patterns by Observing Behavior with Inverse Reinforcement Learning

    Learning Patterns by Observing Behavior with Inverse Reinforcement Learning

    The Software Engineering Institute (SEI) uses Inverse Reinforcement Learning (IRL) techniques—an area of machine learning—to more efficiently and effectively teach novices how to perform expert tasks, achieve robotic control, and perform activity-based intelligence.

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  •  xView 2 Challenge

    xView 2 Challenge

    The xView 2 Challenge applied computer vision and machine learning to analyze electro-optical satellite imagery before and after natural disasters to assess building damage. The competition’s sponsor was the Department of Defense’s Defense Innovation Unit (DIU). This technology is being used to assess building damage from wildfires in Australia and the United States.

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  •  Train, But Verify

    Train, But Verify

    Cybersecurity

    Attacks on machine learning (ML) systems can make them learn the wrong thing, do the wrong thing, or reveal sensitive information. Train, But Verify protects ML systems by training them to act against two of these threats at the same time and verifying them against realistic threat models.

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  •  Community Guidance to Prevent Common Coding Errors

    Community Guidance to Prevent Common Coding Errors

    Cybersecurity

    The SEI leads a community initiative to establish secure coding practices that prevent coding errors and that are reliable, usable, and effective.

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  •  Knowing When You Don’t Know: Engineering AI Systems in an Uncertain World

    Knowing When You Don’t Know: Engineering AI Systems in an Uncertain World

    Data Modeling and Analytics

    This project is benchmarking methods for quantifying uncertainty in machine learning (ML) models. It is also developing techniques to identify the causes of uncertainty, rectify them, and efficiently update ML models to reduce uncertainty in their predictions.

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  •  AI Engineering: A National Initiative

    AI Engineering: A National Initiative

    The SEI is taking the initiative to develop an AI engineering discipline that will lay the groundwork for establishing the practices, processes, and knowledge to build new generations of AI solutions.

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  •  Applying Causal Learning to Improve Software Cost Estimation and Project Control

    Applying Causal Learning to Improve Software Cost Estimation and Project Control

    SEI researchers have applied causal learning to help the Department of Defense identify factors that increase software costs and to provide guidance to control them.

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  •  Characterizing and Detecting Mismatch in ML-Enabled Systems

    Characterizing and Detecting Mismatch in ML-Enabled Systems

    The development of machine learning-enabled systems typically involves three separate workflows with three different perspectives—data scientists, software engineers, and operations. The mismatches that arise can result in failed systems. We developed a set of machine-readable descriptors for elements of ML-enabled systems to make stakeholder assumptions explicit and prevent mismatch.

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  •  Architecting the Future of Software Engineering: A National Agenda for Software Engineering Research & Development

    Architecting the Future of Software Engineering: A National Agenda for Software Engineering Research & Development

    This study identifies the technologies and areas of research that are most critical for enabling future software systems. The technology roadmap that resulted from this work is intended to guide the research efforts of the software engineering community toward future systems that are safe, predictable, and evolvable.

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