Zhendong Su: Catalogue data in Spring Semester 2023

Name	Prof. Dr. Zhendong Su
Field	Computer Science
Address	Professur für Informatik ETH Zürich, CNB H 102 Universitätstrasse 6 8092 Zürich SWITZERLAND
Telephone	+41 44 633 77 72
E-mail	zhendong.su@inf.ethz.ch
Department	Computer Science
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
263-2100-00L	Research Topics in Software Engineering The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.	2 credits	2S	Z. Su, M. Vechev, R. Jung
Abstract	This seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research.
Learning objective	Each student will be asked to study some papers from the recent software engineering literature and review them. This is an exercise in critical review and analysis. Active participation is required (a presentation of a paper as well as participation in discussions).
Content	The aim of this seminar is to introduce students to recent research results in the area of programming languages and software engineering. To accomplish that, students will study and present research papers in the area as well as participate in paper discussions. The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development tools.
Literature	The publications to be presented will be announced on the seminar home page at least one week before the first session.
Prerequisites / Notice	Papers will be distributed during the first lecture.
263-2815-00L	Automated Software Testing Last cancellation/deregistration date for this graded semester performance: 17 March 2023! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".	7 credits	2V + 1U + 3A	Z. Su
Abstract	This course introduces students to classic and modern techniques for the automated testing and analysis of software systems for reliability, security, and performance. It covers both techniques and their applications in various domains (e.g., compilers, databases, theorem provers, operating systems, machine/deep learning, and mobile applications), focusing on the latest, important results.
Learning objective	* Learn fundamental and practical techniques for software testing and analysis * Understand the challenges, open issues and opportunities across a variety of domains (security/systems/compilers/databases/mobile/AI/education) * Understand how latest automated testing and analysis techniques work * Gain conceptual and practical experience in techniques/tools for reliability, security, and performance * Learn how to perform original and impactful research in this area
Content	The course will be organized into the following components: (1) classic and modern testing and analysis techniques (coverage metrics, mutation testing, metamorphic testing, combinatorial testing, symbolic execution, fuzzing, static analysis, etc.), (2) latest results on techniques and applications from diverse domains, and (3) open challenges and opportunities. A major component of this course is a class project. All students (individually or two-person teams) are expected to select and complete a course project. Ideally, the project is original research related in a broad sense to automated software testing and analysis. Potential project topics will also be suggested by the teaching staff. Students must select a project and write a one or two pages proposal describing why what the proposed project is interesting and giving a work schedule. Students will also write a final report describing the project and prepare a 20-30 minute presentation at the end of the course. The due dates for the project proposal, final report, and project presentation will be announced. The course will cover results from the Advanced Software Technologies (AST) Lab at ETH as well as notable results elsewhere, providing good opportunities for potential course project topics as well as MSc project/thesis topics.
Lecture notes	Lecture notes/slides and other lecture materials/handouts will be available online.
Literature	Reading material and links to tools will be published on the course website.
Prerequisites / Notice	The prerequisites for this course are some programming and algorithmic experience. Background and experience in software engineering, programming languages/compilers, and security (as well as operating systems and databases) can be beneficial.