TY  - JOUR
A1  - Jörges, Sven
A1  - Margaria, Tiziana
A1  - Steffen, Bernhard
T1  - Assuring property conformance of code generators via model checking
JF  - Formal aspects of computing : the international journal of formal methods
N2  - Automatic code generation is an essential cornerstone of today's model-driven approaches to software engineering. Thus a key requirement for the success of this technique is the reliability and correctness of code generators. This article describes how we employ standard model checking-based verification to check that code generator models developed within our code generation framework Genesys conform to (temporal) properties. Genesys is a graphical framework for the high-level construction of code generators on the basis of an extensible library of well-defined building blocks along the lines of the Extreme Model-Driven Development paradigm. We will illustrate our verification approach by examining complex constraints for code generators, which even span entire model hierarchies. We also show how this leads to a knowledge base of rules for code generators, which we constantly extend by e.g. combining constraints to bigger constraints, or by deriving common patterns from structurally similar constraints. In our experience, the development of code generators with Genesys boils down to re-instantiating patterns or slightly modifying the graphical process model, activities which are strongly supported by verification facilities presented in this article.
KW  - Extreme Model-Driven Development
KW  - Code generation
KW  - Model checking
KW  - Verification
Y1  - 2011
U6  - https://doi.org/10.1007/s00165-010-0169-9
SN  - 0934-5043
VL  - 23
IS  - 5
SP  - 589
EP  - 606
PB  - Springer
CY  - New York
ER  - 
TY  - GEN
A1  - Razzaq, Misbah
A1  - Kaminski, Roland
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Bourdon, Jeremie
A1  - Guziolowski, Carito
T1  - Computing diverse boolean networks from phosphoproteomic time series data
T2  - Computational Methods in Systems Biology
N2  - Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.
KW  - Diverse solution enumeration
KW  - Answer set programming
KW  - Boolean Networks
KW  - Model checking
KW  - Time series data
Y1  - 2018
SN  - 978-3-319-99429-1
SN  - 978-3-319-99428-4
U6  - https://doi.org/10.1007/978-3-319-99429-1_4
SN  - 0302-9743
SN  - 1611-3349
VL  - 11095
SP  - 59
EP  - 74
PB  - Springer
CY  - Berlin
ER  -