Search result: Catalogue data in Spring Semester 2019

Number	Title	Type	ECTS	Hours	Lecturers
Computer Science Master
Focus Courses
Focus Courses in Software Engineering
Seminar in Software Engineering
263-2100-00L	Research Topics in Software Engineering Number of participants limited to 22. 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.	W	2 credits	2S	T. Gross
Abstract	This seminar introduces students to the latest research trends that help to improve various aspects of software quality. Topics cover the following areas of research: Compilers, domain-specific languages, concurrency, formal methods, performance optimization, program analysis, program generation, program synthesis, testing, tools, verification
Learning objective	At the end of the course, the students should be: - familiar with a broad range of key research results in the area as well as their applications. - know how to read and assess high quality research papers - be able to highlight practical examples/applications, limitations of existing work, and outline potential improvements.
Content	The course will be structured as a sequence of presentations of high-quality research papers, spanning both theory and practice. These papers will have typically appeared in top conferences spanning several areas such as POPL, PLDI, OOPSLA, OSDI, ASPLOS, SOSP, AAAI, ICML and others.
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.
263-2926-00L	Deep Learning for Big Code Number of participants limited to 24. 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.	W	2 credits	2S	V. Raychev
Abstract	The seminar covers some of the latest and most exciting developments (industrial and research) in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities.
Learning objective	The objective of the seminar is to: - Introduce students to the field of Deep Learning for Big Code. - Learn how machine learning models can be used to solve practical challenges in software engineering and programming beyond traditional methods. - Highlight the latest research and work opportunities in industry and academia available on this topic.
Content	The last 5 years have seen increased interest in applying advanced machine learning techniques such as deep learning to new kind of data: program code. As the size of open source code increases dramatically (over 980 billion lines of code written by humans), so comes the opportunity for new kind of deep probabilistic methods and commercial systems that leverage this data to revolutionize software creation and address hard problems not previously possible. Examples include: machines writing code, program de-obfuscation for security, code search, and many more. Interestingly, this new type of data, unlike natural language and images, introduces technical challenges not typically encountered when working with standard datasets (e.g., images, videos, natural language), for instance, finding the right representation over which deep learning operates. This in turn has the potential to drive new kinds of machine learning models with broad applicability. Because of this, there has been substantial interest over the last few years in both industry (e.g., companies such as Facebook starting, various start-ups in the space such as http://deepcode.ai), academia (e.g., http://plml.ethz.ch) and government agencies (e.g., DARPA) on using machine learning to automate various programming tasks. In this seminar, we will cover some of the latest and most exciting developments in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities. The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation.
Prerequisites / Notice	The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation. The seminar is ideally suited for M.Sc. students in Computer Science.
263-2930-00L	Blockchain Security Seminar Number of participants limited to 22. 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.	W	2 credits	2S	M. Vechev, D. Drachsler Cohen, P. Tsankov
Abstract	This seminar introduces students to the latest research trends in the field of blockchains.
Learning objective	The objectives of this seminar are twofold: (1) learning about the blockchain platform, a prominent technology receiving a lot of attention in computer Science and economy and (2) learning to convey and present complex and technical concepts in simple terms, and in particular identifying the core idea underlying the technicalities.
Content	This seminar introduces students to the latest research trends in the field of blockchains. The seminar covers the basics of blockchain technology, including motivation for decentralized currency, establishing trust between multiple parties using consensus algorithms, and smart contracts as a means to establish decentralized computation. It also covers security issues arising in blockchains and smart contracts as well as automated techniques for detecting vulnerabilities using programming language techniques.

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