PENG9610 Evidence-based engineering Course description

Engineers have to make many important decisions on use of tools, work processes, project organizations, technical frameworks and many other topics throughout their career. Currently, too many of these choices are based on what is fashionable and argumentation made by people with vested interested. This course has as its main goal to teach students how to be evidence-based and make better judgments and decisions in engineering disciplines. To be evidence-based means in this context to base important decisions and judgments on well-formulated decisions and questions, collection of valid, relevant and representative empirical evidence, proper analyses and synthesis of the evidence, and use of the synthesized evidence as input in properly designed judgment and decision processes.

The course will be offered once a year, provided 5 or more students sign up for the course. If less than 5 students sign up for a course, the course will be cancelled for that year.

All aids are permitted.

None.

Knowledge

On successful completion of the course, the student:

• knows how to formulate decision to be made or questions to be answered so that it can be subject to evidence-based engineering
• knows about relevant sources for collection of evidence
• knows about the most common empirical methods for creating empirical evidence
• knows about theories and models for evaluation of argumentation and empirical evidence
• knows about theories and models for synthesizing evidence, including that of systematic litterature reviews
• knows about theories and models for good judgment and decision-making

Skills

On successful completion of the course, the student can:

• formulate decisions and questions as basis for evidence-based engineering
• collect and evaluate the quality and relevance of evidence
• design empirical studies for the purpose of creating empirical evidence
• aggregate the evaluated evidence so that it can be used to support a decision or answer a question
• design proper processes for judgment and decision-making

General competence

On successful completion of the course, the student can:

• practice evidence-based engineering in work settings
• support and lead experimentation and other means of creating local evidence in work settings
• assess the need for, initiate and drive innovation in the use of evidence-based principles in engineering contexts.

Pass or fail.

This course will give the student insight into the different parts that comprise the internet's architecture and how one can monitor, assess and characterise them. This involves a diverse set of topics that includes but is not limited to routing and addressing, content distribution, data centre networks, key services and application such as DNS and web and mobile broadband. The course will focus particularly on quantification of the robustness and reliability of the internet's architecture and services. Furthermore, the course will draw upon new advancments in the fields of machine learning and network science to extend and expand the toolset available for anlayzing Internet measurements.

The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

None.

Knowledge

On successful completion of the course, the student:

• has an overview of the different elements that comprise the architecture of today’s internet.
• has a good understanding about the approaches for conducting internet measurements and the latest advances in this field.
• be familiar of a broad set of tools that can help analyzing Internet measurments. Of a particular relevance here are tools that originate in other disciplines like Machine Learning and Statisitcal Physics. This will not only expand the available toolset but also increases the potential for interdisciplinory collaboration going forward.

Skills

On successful completion of the course, the student can:

• plan and carry out state-of-the-art measurement tasks
• can formulate research questions on the robustness and performance of operational networks, and design measurements for evaluating these questions.
• will have a general practical understanding of how different parts of the internet's architecture interplay to offer a performant end-to-end service.

General competence

On successful completion of the course, the student can:

• participate in debates and present aspects of his/her expertise in a way that promotes such discussions.
• drive innovation

Module 1 will take the form of lectures. Module 2 will take the form of lab and homework assignments. Module 3 will take the form of seminars. In module 3, the student will present a case to the other students. We will also invite guest lecturers from research groups that focuses on machine learning and network science to introduce the students to potential tools and analysis methods.

Practical training

The students will participate in lab experiments to explore how once can measure various aspects of internet's robustness and performance. The students will write a summary of one of the tools that were introduced in the lab and discuss its benefits and limitations.

None.

Both the presentation of the case in Module 3 of the course and the tool summary document in the practical training part the course will form basis of assessment.

Both exams must be passed in order to pass the course.

The oral presentation cannot be appealed.