Software metrics have been proven to be useful in indicating problems in development projects, but they are limited to analyzing information captured in software artifacts. By applying metrics to communication artifacts, we want to make the development process more insightful, validate the software metrics by combining them with results derived from aspects of team communication, and make it possible to recognize problems from a very early project stage.

The team allocation for software projects is often done solely based on the subjective judgment of the project manager. The objective of this thesis is to create a hybrid system called TEASE (TEam Allocation for Software Engineering). It makes use of an algorithm while allowing user interaction to create optimal teams from a pool of persons. The project manager can adapt the generated team allocation through an intuitive user interface based on his own knowledge and experience. The focus of this thesis is the user interface of TEASE.

The problem of team allocation is a complex task for humans to manage. We address this problem by exploring possible algorithmic solutions and implementing some of them in an interactive decision-support system. We collected a range of algorithmic approaches, introduced a convenient cat- egorization of them, and implemented one of the most promising ones into an actual software solution. We further held two distinct experiments with the implemented system. The results of these experiments show that the sys- tem finds an optimal team allocation with regards to the specified allocation objective and satisfies all specified constraints.

Many exercises include multiple parts that depend on each other. Then it is impossible, difficult and/or demotivating for students to continue with the 2nd or 3rd part if they were not able to solve the 1st part. It is also misleading if the 1st part is finished and the student gets the feedback that e.g. 8 out of 12 test cases still fail. In this thesis, you will extend ArTEMiS so that instructors can easily add multiple parts for exercises. In addition, ArTEMiS should allow student to receive automatic help in form of live tutorials for exercise parts that they don't understand or cannot solve. While they cannot obtain the full points any more, they can work on the other parts as well and learn the system and the theory behind the concept through interactive live tutorials (comparable to an on-boarding in a mobile app). The thesis should also evaluate if these improvements help and motivate students to achieve a better learning experience. ArTEMiS is open source and available on https://github.com/ls1intum/ArTEMiS