Open Projects

For Master's theses, Bachelor's theses or for Software Engineering projects in the Master's program

(Most topics can be adapted in scale to fit any of the above categories)

  • Multi-Language Benchmarks for a New Dynamic Taint Analysis Platform (C/C++, JavaScript, Python)
    Dynamic taint analysis is a program analysis technique in which a program is instrumented to track the flow of sensitive data. This analysis technique can be used to prevent information leaks, to uncover security vulnerabilities, and it also has applications in several other fields. TruffleTaint is a novel dynamic taint analysis platform based on GraalVM, a multi-language virtual machine, and aims to track sensitive data across the language boundary with little overhead in execution time.
    The goal of this project is to use common benchmarks, such as those from the *Are we fast yet?* benchmark suite, to evaluate TruffleTaint's run-time overhead and data tracking capability. To this end, all benchmark programs must be implemented using combinations of C/C++, JavaScript and Python code by using GraalVM's APIs for language interoperability. Furthermore, each benchmark program must also use TruffleTaint's API to mark certain data which the benchmark operates on as sensitive and check that its flow is properly tracked.

  • Data Flow and Call Graph Analysis for IEC 61631-3 Structure Text Programs (Java, optional Kotlin)
    IEC 61631-3 is a standard for languages for Programmable Logic Controller (PLC) Programs. One language within this standard is Structure Text, which is a language similar to Pascal. In this project, data flow and call graph graphs should be created for Structured Text programs. Data flow graphs represent the data dependencies between program statements. A call graph represents the call dependencies between procedures. The input is an abstract syntax tree of Structured Text programs (see project above). Remark: It is also possible to split the task into two independent projects, one for the data flow and one for the call graph.

  • New JavaScript Language features - ECMAScript proposals (Java, some JavaScript)
    JavaScript is specified in the ECMAScript language specification. It is an evolving language, and is improved by a "proposal" process. Each new or improved feature is specified by one proposal. Currently open proposals include Temporal (a date/time library), optional chaining (avoiding null value exceptions), decorators (similar to Annotations in Java), additional methods to the Set builtin, and many more. As the different proposals vastly differ in effort to implement them, we have topics for projects (project in software engineering), bachelor theses and master theses. The task is to fully implement the current state of the proposal in the GraalVM/Graal.js JavaScript engine.
    Contact: Dr. Wirth

  • Humongous Object Aware Region Allocation (C++)
    The Hotspot G1 garbage collector is a regional collector: the Java heap is strictly split into same-sized regions. Objects larger than a single region ("humongous regions") are allocated using separate contiguous sets of regions, and are unmovable for performance reasons. This poses a few problems, for example:
    • at the end of such a humongous region there is often a significant amount of space that is effectively wasted and unavailable for allocation.
    • region level fragmentation due to never moving these objects can cause unexpected Out-of-memory situations if there are not enough contiguous regions left for a given new allocation.
    This project could lessen the problem by implementing one or more changes to the existing strategy in heap management by for example better region selection for evacuation and placement, automatic region level defragmentation efforts, over-provisioning the heap area, more aggressive reclaimation of humongous objects and regular object allocation at the end of a humongous object.
    Contact: DI Schatzl

  • G1 garbage collector Full GC improvements (C++)
    Only in JDK10 the Hotspot G1 garbage collector received a parallel full-heap collector. It uses a parallelized mark-sweep-compact algorithm. While its performance is on par with the Parallel GC Full GC algorithm, there are opportunities to improve the algorithm related to work distribution, exploiting pre-existing work and handling various edge cases better.
    Contact: DI Schatzl