Andreas Jedlitschka leads the Data Science department at the Fraunhofer Institute for Experimental Software Engineering. His department deals with a systematic approach to data-related issues (why, what, when, where, who, which, how). His research interest is in empirical and data-driven decision support, where he has published more than 70 papers in journals and conferences. For more than 20 years, Andreas has been working on Artificial Intelligence (AI) and since more then ten years on its usage within the software engineering life cycle. Several projects, among them ProDebt, QRapids, DeepQuali, and QAI4SE contributed to the operationalization of the Andreas‘ research roadmap.

Other important research perspectives, he is particularly interested in, include exploring AI use cases for the benefit of humanity and the empirical evaluation of AI in settings with close Human-AI-Collaboration. In general, trustworthiness of AI, in particular of Generative AI is a major driver for his research.

Andreas is co-author of a German patent on „Method for unobtrusive monitoring of a person and system for carrying out the method“, which is a self-adapting, data-driven analysis approach (today, one would say AI). His most cited paper is on „Guidelines for Reporting Controlled Experiments in Software Engineering“. The guidelines provide a starting point for the adoption of the evidence-based SE paradigm. Dr Jedlitschka is a member of several programme committees (e.g. ESEM, PROFES) and served as Programme Co-Chair of ESEM 2016 and as General Chair of PROFES2013 and General CoChair of PROFES2023. In addition, he had several other roles in conferences, e.g., he organized CESI and WSESE Workshops at several ICSE conferences and significantly supported the organizers of Dagstuhl-Seminar 06262.

He represents Fraunhofer IESE in the International Software Engineering Research Network (ISERN) and is a life-time member of the ISERN Steering Committee. He is also serving his second five-year term as an elected member of the ESEM Steering Committee. Andeas has had leading roles in projects with industry and in the European FP5 (ESERNET), FP6 (EMERGE, VIDE, ReDSeeDS), FP7 (OASIS, AALIANCE, MUNDUS) and H2020 (Q-Rapids), ITEA (GreenCode, which just started), as well as KI4KMUinRLP (which is about to start). Andreas was a lecturer for „Empirical Modelling and Methods“ and “Software Project and Process Management) at the Rhineland Palatinate Technical University of Kaiserslautern. He is invited to give talks on Artificial Intelligence and moderates workshops to analyse the potential of AI for companies.

Andreas Jedlitschka received his M.S. (Diplom, 1994) and Ph.D. (Dr.-Ing., 2009) in Computer Science from the University of Kaiserslautern. After seven years as an IT consultant at Voss&Kamb und Partner GmbH, he joined Fraunhofer IESE in 2000 as a project manager and scientist.

Selected publications

1.   D. Seifert, L. Jöckel, A. Trendowicz, M. Ciolkowski, T. Honroth, A. Jedlitschka. Can Large Language Models (LLMs) compete with Human Requirement Reviewers? – Replication of an Inspection Experiment on Requirements Documents. In: 2024 International Conference on Product-Focused Software Process Improvement (PROFES), Tartu, Estonia, Dez. 2-4, 2024 (to appear)

2.  S. Martínez-Fernández, A.M. Vollmer, A. Jedlitschka, X. Franch, L. López, P. Ram, P. Rodríguez, S. Aaramaa, A. Bagnato, M. Choras, J. Partanen, „Continuously Assessing and Improving Software Quality With Software Analytics Tools: A Case Study,“ in IEEE Access, vol. 7, pp. 68219-68239, 2019, doi: 10.1109/ACCESS.2019.2917403  

3.  D. Falessi, N. Juristo, C. Wohlin, B. Turhan, J. Münch, A. Jedlitschka, M. Oivo; “Empirical software engineering experts on the use of students and professionals in experiments”; Empirical Software Engineering, (2018), 1-38; DOI 10.1007/s10664-017-9523-3

4. A. Jedlitschka, N. Juristo, D. Rombach (2013). Reporting experiments to satisfy professionals‘ information needs. Empirical Software Engineering, 2013, (DOI) 10.1007/s10664-013-9268-6 

5. A. Jedlitschka, D. Pfahl: Reporting guidelines for controlled experiments in software engineering. In: 2005 International Symposium on Empirical Software Engineering. ISESE 2005 – Proceedings.Los Alamitos: IEEE Computer Society, 2005, pp. 95-104