Prof. Dr. Stefan Lessmann

Die Forschung konzentriert sich auf den Einsatz fortschrittlicher KI-Methoden, insbesondere Deep Learning und Large Language Models, zur Analyse komplexer Datenstrukturen wie Zeitreihen, Texten und Bildern. Zentrale Anwendungsfelder umfassen die Optimierung von Geschäftsprozessen, die Vorhersage von Verbraucherverhalten sowie die Automatisierung in der Finanz- und Energiewirtschaft. Ein weiterer wichtiger Aspekt ist die Unterstützung nachhaltiger Produktionsmodelle und die Reduktion von CO₂-Emissionen durch datengetriebene Entscheidungsfindung.

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• EX: Sentientic

  Quelle ↗

  Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 11/2015 - 10/2016 Projektleitung: Prof. Dr. Stefan Lessmann

• Operations Research in der Wirtschaftsinformatik

  Quelle ↗
  409-02 · Softwaretechnik und Programmiersprachen

  Förderer: Andere Hochschulfördergesellschaften Zeitraum: 06/2016 - 12/2026 Projektleitung: Prof. Dr. Stefan Lessmann

• EXIST Creatext

  Quelle ↗

  Förderer: BMWE: EXIST Zeitraum: 11/2019 - 10/2020 Projektleitung: Prof. Dr. Stefan Lessmann

• Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

  2008

  IEEE Transactions on Software Engineering · DOI

  Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary data sets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and, finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository. Overall, an appealing degree of predictive accuracy is observed, which supports the view that metric-based classification is useful. However, our results indicate that the importance of the particular classification algorithm may be less than previously assumed since no significant performance differences could be detected among the top 17 classifiers.

• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

  2015

  European Journal of Operational Research · DOI