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Carnegie Mellon University | Software Engineering Institute

Software Engineering Workshop for Educators

August 3-5, 2021 | Online

18th SEI Software Engineering Workshop for Educators

The SEI hosted this annual Workshop for Educators to foster an ongoing exchange of ideas among educators whose curricula include subjects spanning software engineering. The event was free of charge and open to any accredited college-level educator. 

This three-day virtual workshop featured two days of training on Software Engineering for Artificial Intelligence (SE4AI), Artificial Intelligence for Software Engineering (AI4SE), and Blockchain. In lieu of the traditional artifact sharing and discussion on day three, we conducted instructor-led interactive sessions continuing last year’s theme of best practices and lessons learned related to remote and virtual teaching.

 

Facilitators

Dr. Grace Lewis

Grace Lewis

Dr. Lewis is principal researcher and lead of the Tactical and AI-enabled Systems (TAS) initiative at the Software Engineering Institute at Carnegie Mellon University. Lewis is the principal investigator for the “Predicting Inference Degradation in Production ML Systems” and “Characterizing and Detecting Mismatch in ML-Enabled Systems” research projects. Lewis’ current areas of expertise and interest include software engineering for AI/ML systems, edge computing, software architecture (in particular the development of software architecture practices for systems that integrate emerging technologies), and software engineering in society.

How Attendees Describe the Workshop

"a significant aid in teaching software engineering"

"a great source of relevant and timely software education guidance and resources"

 

AI Engineering

Advances in machine learning (ML) algorithms and increasing availability of computational power are already resulting in huge investments in systems that aspire to exploit artificial intelligence (AI). AI-enabled systems — software-reliant systems that include data and components that implement AI algorithms mimicking learning and problem solving — have inherently different characteristics than software systems alone. These different characteristics are driving academia, industry and governments to explore the definition of a new discipline of engineering as AI Engineering. However, AI-enabled systems are, above all, software systems. The development and sustainment of these systems have many parallels with building, deploying, and sustaining software systems. Research programs in software engineering will need to focus on the challenges that AI elements bring to software analysis, design, construction, deployment, maintenance, and evolution. Exploring what existing software engineering practices can reliably support development of AI systems and what new practices will need to be developed to drive the discipline of AI Engineering will drive research initiatives in the next decade. This talk will summarize the progress that the SEI has made to date with an emphasis on foundational AI engineering practices.

 

Training


Software Engineering for AI

Grace Lewis
Grace Lewis

AI-enabled systems, defined as systems that contain AI components, are increasingly being used to support businesses with capabilities such as decision making, user assistance, and data analysis. The challenges of developing and deploying AI-enabled systems, and in particular ML-enabled systems, has been extensively reported in the literature and practitioner blogs and articles. However, we need to keep in mind that AI-enabled systems are software systems! As such, we require disciplined software engineering practices to ensure these systems meet their business goals and required capabilities. In particular, the interaction between software, data, and AI/ML components requires software design and architecture approaches to be incorporated early and continuously. This presentation will introduce the general field of software engineering for AI (SE4AI) and report on several SEI projects in this area.

Grace Lewis is principal researcher and lead of the Tactical and AI-enabled Systems (TAS) initiative at the Software Engineering Institute at Carnegie Mellon University. Lewis is the principal investigator for the “Predicting Inference Degradation in Production ML Systems” and “Characterizing and Detecting Mismatch in ML-Enabled Systems” research projects. Lewis’ current areas of expertise and interest include software engineering for AI/ML systems, edge computing, software architecture (in particular the development of software architecture practices for systems that integrate emerging technologies), and software engineering in society.

Educator-Led Interactive Sessions

The third day of the Software Engineering Educators workshop included instructor-led sessions that continued the discussions that started last year with respect to best practices for virtual and hybrid teaching.