Concepts, methods, and current practice of software engineering. Large-scale software production, software life cycle models, principles, and techniques for each stage of development.

Equips students to develop techniques of software-intensive systems through successful requirements analysis techniques and requirements engineering. Students learn systematic process of developing requirements through cooperative problem analysis, representation, and validation.

Preparation for developing high-quality software through successful verification and validation techniques. Fundamental principles of software testing, implementing software testing practices, ensuring the thoroughness of testing to gain confidence in the correctness of the software.

Specification, design, construction, testing, and documentation of a complete software system. Special emphasis on the need for and use of teamwork, careful planning, and other techniques for working with large systems.

Design, deploy, and manage software incorporating AI components in real-world production environments. Topics include types of models, in-house vs. third-party models, retrieval augmented generation, fine-tuning, prompt engineering, model testing, and reliability.

Introduction to application design: designing the overall functionality of a software application. Topics include general design theory, software design theory, and software architecture. Includes practice in designing and case studies of existing designs.

Introduction to implementation design: designing the internals of a software application. Topics include design aesthetics, design implementation, design recovery, design patterns, and component reuse. Includes practice in designing and case studies of existing designs.

Concepts in Internet applications engineering with emphasis on the Web. Peer-to-Peer and Interoperability. Topics include HTTP and REST, Remote Procedure/Method Calls, Web Services, data representations, content distribution networks, identity management, relevant W3C/IETF standards, and relevant new large-scale computing styles.

An introduction to information retrieval including indexing, retrieval, classifying, and clustering text and multimedia documents.

Study of the concepts, methods, and tools for the analysis, design, construction, and measurement of complex software-intensive systems. Underlying principles emphasized. State-of-the-art software engineering and promising research areas covered, including project management.

Concepts in modern programming languages, their interaction, and the relationship between programming languages and methods for large-scale, extensible software development. Empirical analysis of programming language usage.

Covers principles and concepts of automated program analysis. Topics include program representations; intra-procedural data-flow analysis; call-graph construction; pointer analysis; alias analysis; inter-procedural analysis; inter-procedural finite distributive subset problems; inter-procedural distributive environment problems; symbolic execution; and program-analysis applications.

Studies techniques for developing confidence in software from traditional testing techniques to state-of-the-art dynamic analysis and debugging techniques. Emphasis is on assisting developer comprehension of runtime behavior.

Study of the concepts, representation techniques, development methods, and tools for architecture-centric software engineering. Topics include domain-specific software architectures, architectural styles, architecture description languages, software connectors, and dynamism in architectures.

Algorithms for the storage, retrieval, filtering, and classification of textual and multimedia data. The vector space model, Boolean and probabilistic queries, and relevance feedback. Latent semantic indexing; collaborative filtering; and relationship to machine learning methods.

Explores example software systems and their underlying concepts that leverage computing to empower and augment human individuals in their activities. Topics span the fields of user interface design, human-computer interaction, software engineering, and cognitive computing.

Selected topics about the cooperative and human aspects of software engineering from the perspective that software engineering is inherently an endeavor involving human stakeholders. Topics may include but are not limited to collaboration, trust, creativity, well-being, productivity, and software tools.

Exploration of well-known data structures to represent computational problems. Topics include linked lists, graphs, binary trees, stacks, queues, heaps, and hash tables, among others.

Exploration of strategies to tackle computational problems whose solutions include well-known algorithms. Topics include sorting, searching, and indexing, among others.

Exploration of networking principles and concepts for the development of distributed software. Topics include programming against well-known network protocols, ports and sockets, and network APIs.

Exploration of software development with substantial reliance on a database for storage and retrieval of data. Topics include relational databases, structured query language, relational database management systems, APIs and libraries for database programming, among others.

Exploration of concepts and mechanisms for the development of concurrent software. Topics include threads, locks, race conditions, and deadlocks, among others.

Exploration of interactive software with substantial graphical user interface elements. Topics include libraries and frameworks for GUI programming, layout design and alternatives, event-driven programming, among others.

Exploration of contemporary libraries and frameworks for construction of mobile applications. Topics include emulators, mobile development standards and patterns, energy consumption issues, screen layout, among others.

Exploration of principles and concepts for textual information retrieval. Topics include tokenization, inverted indexes, scored retrieval, and precision and recall.

Exploration of the concepts, terminology, and processes for training and using deep neural networks for classification problems. Topics include tensors and tensor operations, gradient-based optimization, feature engineering and learning, and workflow of learning systems.

Exploration of the concepts, terminology, and processes used in supervised and unsupervised learning for classification and clustering, from a software engineering perspective. Topics include K-Nearest Neighbors, Logistic Regression, Random Forests, Support Vector Machines, K-Means, and model assessment.

Exploration of the concepts, terminology, and popular frameworks for developing full-stack web applications. Students develop simple applications using multiple development stacks, and deploy them on the cloud.

Introduces LLMs for software engineering, covering transformers, prompt/context engineering, and retrieval-augmented generation (RAG). Students gain hands-on experience interacting with models using APIs and open-source models while laying the foundation for advanced study in SWE 270P.

Designed to teach students how to ensure high-quality software by means of testing, debugging, and other quality assurance activities. Students learn a combination of both theoretical and practical skills, including hands-on experience with modern tools and approaches.

Teaches students the various ways software can be decomposed and then how to put the parts back together. Students are exposed to a variety of different programming styles and composition mechanisms.

Provides an introduction to the basic principles of human-computer interaction (HCI) and the pragmatic aspects of usability engineering. Topics include the fundamentals of interaction, user experience, design for usability, and evaluation of products for their usability.

Study of software system architectures and architectural styles for large-scale distributed applications, and contemporary technologies and standards for their construction. Topics include client-server, peer-to-peer, publish-subscribe, REST, cloud computing, content distribution networks, scalability, latency, caching, and security, among others.

Introduces theories, concepts, representations, techniques, and case studies in understanding large-scale, complex software systems. Topics include static and dynamic modeling notations, manual and (semi-)automated reverse engineering techniques, APIs, patterns, and styles. A significant, hands-on project is included.

Principles and concepts for the design and construction of secure software. Topics include common types of software security vulnerabilities, methods for detecting vulnerabilities, design and process methodologies to improve security of software, and techniques for assessing security properties of software.

Cloud and security foundations provides students fundamental design and security principles about cloud computing and information security. Topics include cloud concepts and architectures; cloud security; data protection and privacy compliance and governance; and security by design, amongst other topics.

Design, deploy, and manage software incorporating AI components in real-world production environments. Topics include learning about different types of models, in-house vs. third-party models, retrieval augmented generation, fine-tuning, prompt engineering, model testing, and reliability.

Mandatory internship in which students individually work at an outside organization to gain experience with the challenges involved in the practice of software engineering.

Quarter-long software-intensive project focusing on the design and implementation of a novel software system. Students are expected to bring to bear the concepts acquired during the program.

Focuses on designing and implementing a novel software system. Students work on their project while emphasizing communication skills in business and technical settings. Students practice presenting project ideas, developing portfolios.

Forum for presentation and criticism by students of research work in progress. Presentation of problem areas and related work. Specific goals and progress of research.

Mandatory weekly meetings with a faculty advisor.

Studies in selected areas of software engineering. Topics addressed vary each quarter.

Individual research or investigation conducted in preparation for the M.S. thesis option or the dissertation requirements for the Ph.D. program.

Individual research or investigation under the direction of an individual faculty member.

Supervised individual practical project in Software Engineering.