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Auflistung nach Schlagwort "complexity"

1 - 4 von 4
  • Konferenzbeitrag
    Managing Software Complexity in Automotive SW Development
    Quante, Jochen; Grundler, Thomas
    In the last decades, software has become more and more important in the automotive domain. With features like autonomous driving and increasing connectivity, the software’s sheer volume has increased by an order of magnitude. This ever-growing complexity has to be accompanied by processes that limit its negative effect on maintainability. Also, the prospect of “end of combustion” demands reduction of development effort for combustion engine control software. In this paper, we report on our approach to control software complexity in the powertrain domain. We describe the basic idea for measuring and managing maintainability, the challenges on adopting such an approach in practice, like having to measure on different kinds of artifacts, and the factors that have lead to success.
  • Konferenzbeitrag
    Mining of Comprehensible State Machine Models for Embedded Software Comprehension
    (Softwaretechnik-Trends Band 39, Heft 2, 2019) Said, Wasim; Quante, Jochen
    Embedded legacy software contains a lot of expert knowledge that has been cumulated over many years. Therefore, it usually provides highly valuable and indispensable functionality. At the same time, it becomes more and more complex to understand and maintain. Mining of understandable models, such as state machines, from such software can greatly support developers in maintenance, evolution and reengineering tasks. Developers need to understand the software in order to evolve it. Existing state machine mining approaches are based on symbolic execution, which means enumeration of all paths. This quickly leads to path explosion problem. One effect of this problem on state machine mining is that the extracted models contain a very high number of states and transitions, and therefore are not useful for human comprehension. This means that additional measures towards comprehensibility of extracted state machines are required. To reach this goal, we introduced user interaction measures that can reduce the complexity of extracted state machines by reducing the number of states and transitions.
  • Konferenzbeitrag
    Possible Voter Control in k-Approval and k-Veto Under Partial Information
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Erdélyi, Gábor; Reger, Christian
    We study the complexity of possible constructive/destructive control by adding voters (PCCAV/PDCAV) and deleting voters (PCCDV/PDCDV) under nine different models of partial information for k-Approval and k-Veto. For the two destructive variants, we can further settle a polynomial-time result holding even for each scoring rule. Generally, in voter control, an external agent (called the chair) tries to change the outcome of the election by adding new voters to the elec- tion or by deleting voters from the election. Usually there is full information in voting theory, i.e., the chair knows the candidates, each voter’s complete ranking about the candidates and the voting rule used. In this paper, we assume the chair to have partial information about the votes and ask if the chair can add (delete) some votes so that his preferred (despised) candidate is (not) a winner for at least one completion of the partial votes to complete votes.
  • Conference poster
    Towards Computer-Aided Teaching of Reductions in Theoretical Computer Science
    (Proceedings of DELFI 2024, 2024) Herwig, Maurice; Hundeshagen, Norbert; Kastaun, Marit; Kollenberg, Cedric
    Reductions play a crucial role in the theory of computer science, aiding in the identification of computationally unsolvable or intractable problems. Despite their significance, mastering reductions remains challenging for students due to their high level of abstraction. In this work we report on an educational approach to learn reductions in a more practical way as a programming exercise. Through a pilot study (𝑛 = 41) with three measurement points, insights were gathered on the usage of a prototype learning tool for reductions, leveraging Python as the main computational model. Initial findings highlight further enhancements of computer-aided learning and teaching of reductions, such as incorporating mathematical foundations in a tools feedback, visualizing and generically generating problem instances, as well as improving extensibility by simplifying the creation of exercises.