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Dr. Lorijn van Rooijen

Dr. Lorijn van Rooijen

Datenbank- und Informationssysteme

Wissenschaftliche Mitarbeiterin

(+49) (0)5251 60-6839
+49 5251 60-5465202
Fürstenallee 11
33102 Paderborn

2015 - heute

Post-doc in SFB 901 On the fly Computing

Universität Paderborn

In the first semester of 2017, I taught the course Logik und Deduktion for bachelor students in Computer Science.

2011 - 2014

PhD, Computer Science (tres honorable)

Universite de Bordeaux, Laboratoire Bordelais de Recherche en Informatique (LaBRI)

Supervisor: Prof. Marc Zeitoun.

Dissertation: A combinatorial approach to the separation problem for regular languages.

2009 - 2011

MSc, Mathematics (cum laude)

Radboud Universiteit Nijmegen

MSc Thesis: Generalised Kripke Semantics for Various Substructural Logics.

01/2010 - 06/2010


Exchange student at Université Paris-Diderot (Paris VII), taking master courses in Logic and Computational Linguistics.

2004 - 2009

BSc, Mathematics (cum laude)

Radboud Universiteit Nijmegen

2003 - 2006

BSc, Nutrition and Health (cum laude)

Wageningen Univeristeit en Researchcentrum

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From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process

L. van Rooijen, F.S. Bäumer, M.C. Platenius, M. Geierhos, H. Hamann, G. Engels, in: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW), IEEE, 2017, pp. 379-385

Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.

A Characterization for Decidable Separability by Piecewise Testable Languages

W. Czerwinski, W. Martens, L. van Rooijen, M. Zeitoun, G. Zetzsche, Discrete Mathematics & Theoretical Computer Science (2017), 19(4)


Active Coevolutionary Learning of Requirements Specifications from Examples

M.D. Wever, L. van Rooijen, H. Hamann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2017, pp. 1327--1334

Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user’s needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.


An Overview of Service Specification Language and Matching in On-The-Fly Computing (v0.3)

M.C. Platenius, K. Josifovska, L. van Rooijen, S. Arifulina, M. Becker, G. Engels, W. Schäfer, Universität Paderborn, 2016

Requirements Specification-by-Example Using a Multi-Objective Evolutionary Algorithm

L. van Rooijen, H. Hamann, in: Proceedings of 24th IEEE International Requirements Engineering Conference (RE 2016), 2016, pp. 3--9

A task at the beginning of the software development process is the creation of a requirements specification. The requirements specification is usually created by a software engineering expert. We try to substitute this expert by a domain expert (the user) and formulate the problem of creating requirements specifications as a search-based software engineering problem. The domain expert provides only examples of event sequences that describe the behavior of the required software program. These examples are represented by simple sequence diagrams and are divided into two subsets: positive examples of required program behavior and negative examples of prohibited program behavior. The task is then to synthesize a generalized requirements specification that usefully describes the required software. We approach this problem by applying a genetic algorithm and evolve deterministic finite automata (DFAs). These DFAs take the sequence diagrams as input that should be either accepted (positive example) or rejected (negative example). The problem is neither to find the minimal nor the most general automaton. Instead, the user should be provided with several appropriate automata from which the user can select, or which help the user to refine the examples given initially. We present the context of our research ("On-The-Fly Computing"), present our approach, report results indicating its feasibility, and conclude with a discussion.


A Note on Decidable Separability by Piecewise Testable Languages

W. Czerwinski, W. Martens, L. van Rooijen, M. Zeitoun, in: Fundamentals of Computation Theory - 20th International Symposium, (FCT) 2015, Gdańsk, Poland, August 17-19, 2015, Proceedings, 2015, pp. 173-185



On Separation by Locally Testable and Locally Threshold Testable Languages

T. Place, L. van Rooijen, M. Zeitoun, Logical Methods in Computer Science (2014), 10(3)



Relational semantics for full linear logic

D. Coumans, M. Gehrke, L. van Rooijen, Journal of Applied Logic (2013), 12(1), pp. 50-66


Separating Regular Languages by Piecewise Testable and Unambiguous Languages

T. Place, L. van Rooijen, M. Zeitoun, in: Mathematical Foundations of Computer Science 2013 - 38th International Symposium, (MFCS) 2013, Klosterneuburg, Austria, August 26-30, 2013, Springer Berlin Heidelberg, 2013, pp. 729-740


Separating Regular Languages by Locally Testable and Locally Threshold Testable Languages

T. Place, L. van Rooijen, M. Zeitoun, in: Annual Conference on Foundations of Software Technology and Theoretical Computer Science, (FSTTCS) 2013, December 12-14, 2013, Guwahati, India, Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2013, pp. 363--375



Generalized Kripke semantics for the Lambek-Grishin calculus

A. Chernilovskaya, M. Gehrke, L. van Rooijen, Logic Journal of IGPL (2012), 20(6), pp. 1110-1132


Generalized Kripke semantics for the Lambek-Grishin calculus

A. Chernilovskaya, M. Gehrke, L. van Rooijen, Logic Journal of IGPL (2012), 20(6), pp. 1110-1132


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