System Test and Evaluation

Graduate Certificate

This course presents advanced concepts in requirements engineering. The course will combine a practical focus on improving the quality of problem formulation and a research focus on advancing the state of the art in problem formulation. Topics include different types of problem spaces (outcomes vs functions), formal distinction between problem and solution, formal modeling of needs and requirements, formal syntax and ontologies for textual formulation of needs and requirements, elicitation and derivation as a byproduct of mission engineering, decomposition as a byproduct of systems architecture, mixed-formulation approaches, traceability, techniques to identify necessary vs constraining needs and requirements, and techniques to identify gaps in needs and requirements.

In this course you will learn how to derive and develop software requirements that are measurable, testable and lead to a compliant software design and implementation. Using industry best practices and tools, you will learn how to elicit, analyze, specify, and validate functional requirements (what should the software system do) and non-functional software requirements (how should the software system fulfill the functional requirements). You will develop software requirement models and specifications that capture the customer / user's needs. The requirements will be captured in a commercially available software requirements management tool and exported to create a Software Requirements Specification (SRS). You will also develop a formal Software Test Plan (STP) that can be used in a software acceptance test to validate that the product will meet its requirements as specified. Additionally, you will also establish and maintain a software requirement configuration baseline, utilizing widely adopted industry processes to incorporate any subsequent additions, deletions, and enhancements to the software requirements over its lifecycle. 
 

This course explores system integration principles, focusing on planning, interoperability, and performance across various industries. It covers integration strategies, digital twins, and simulation techniques to ensure seamless system functionality before physical integration. 

This course delves into systematic planning and execution of Verification and Validation or Test and Evaluation (V&V/T&E) strategies. The course explores foundational aspects that are critical in understanding the value and need of V&V/T&E activities and to effectively reason about V&V/T&E evidence, quantitative methods to inform decisions about what to V&V/T&E and when, procedural practices necessary to implement and execute V&V/T&E, and quantitative and qualitative guidance to integrate V&V/T&E considerations into system architecture.

Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions. Graduate-level requirements include additionally more difficult homework assignments.Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions. Graduate-level requirements include additionally more difficult homework assignments. 

This course will provide senior undergraduate and graduate students from a diverse engineering disciplines with fundamental concepts, principles and tools to extract and generalize knowledge from data. Students will acquire an integrated set of skills spanning data processing, statistics and machine learning, along with a good understanding of the synthesis of these skills and their applications to solving problem. The course is composed of a systematic introduction of the fundamental topics of data science study, including: (1) principles of data processing and representation, (2) theoretical basis and advances in data science, (3) modeling and algorithms, and (4) evaluation mechanisms. The emphasis in the treatment of these topics will be given to the breadth, rather than the depth. Real-world engineering problems and data will be used as examples to illustrate and demonstrate the advantages and disadvantages of different algorithms and compare their effectiveness as well as efficiency, and help students to understand and identify the circumstances under which the algorithms are most appropriate. 
 

Statistical inference and hypothesis test for linear regression models, design and analysis of physical experiments to characterize and improve engineering systems, mixed effects model, response surface methodology.