Cornel Klein

Fabian Ritz, Thomy Phan, Andreas Sedlmeier et al.

The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonetheless, the underlying processes can be described in a formal way. We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way. In addition to the production of the necessary artifacts, we also focus on generating and validating fitting descriptions in the form of specifications. We stress the importance of further evolving the ML model throughout its life-cycle even after initial training and testing. Thus, we provide various interaction points with standard SD processes in which ML often is an encapsulated task. Further, our SD process model allows to formulate ML as a (meta-) optimization problem. If automated rigorously, it can be used to realize self-adaptive autonomous systems. Finally, our SD process model features a description of time that allows to reason about the progress within ML development processes. This might lead to further applications of formal methods within the field of ML.

Fabian Ritz, Thomy Phan, Robert Müller et al.

A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems. While such strategies sometimes yield superior performance, they may also result in undesired or even dangerous behavior. In industrial scenarios, a system's behavior also needs to be predictable and lie within defined ranges. To enable the agents to learn (how) to align with a given specification, this paper proposes to explicitly transfer functional and non-functional requirements into shaped rewards. Experiments are carried out on the smart factory, a multi-agent environment modeling an industrial lot-size-one production facility, with up to eight agents and different multi-agent reinforcement learning algorithms. Results indicate that compliance with functional and non-functional constraints can be achieved by the proposed approach.

Thomas Gabor, Marie Kiermeier, Andreas Sedlmeier et al.

From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various tasks in the process of software engineering is naturally re-distributed. We claim that all steps related to testing need to become self-adaptive to match the capabilities of the self-adaptive system-under-test. Otherwise, the adaptive system's behavior might elude traditional variants of quality assurance. We thus propose the paradigm of scenario coevolution, which describes a pool of test cases and other constraints on system behavior that evolves in parallel to the (in part autonomous) development of behavior in the system-under-test. Scenario coevolution offers a simple structure for the organization of adaptive testing that allows for both human-controlled and autonomous intervention, supporting software engineering for adaptive systems on a procedural as well as technical level.

Radu Grosu, Cornel Klein, Bernhard Rumpe

In this report, the SYSLAB model is complemented in different ways: State-box models are provided through timed port automata, for which an operational and a corresponding denotational semantics are given. Composition is defined for components modeled in the state-box view as well as for components modeled in the black-box view. This composition is well-defined for networks of infinitely many components. To show the applicability of the model, several examples are given.

Bernhard Rumpe, Cornel Klein

Relating formal re nement techniques with commercial object oriented software development methods is important to achieve enhancement of the power and exibility of these software development methods and tools. We will present an automata model together with a denotational and an operational semantics to describe the behavior of objects. Based on the given semantics we de ne a set of powerful re nement rules and discuss their applicability in software engineering practice especially with the use of inheritance.

Cornel Klein, Bernhard Rumpe, Manfred Broy

In the SysLab project we develop a software engineering method based on a mathematical foundation. The SysLab system model serves as an abstract mathematical model for information systems and their components. It is used to formalize the semantics of all used description techniques such as object diagrams state automata sequence charts or data-flow diagrams. Based on the requirements for such a reference model, we define the system model including its different views and their relationships.

Ruth Breu, Ursula Hinkel, Christoph Hofmann et al.

The Unified Modeling Language UML is a language for specifying visualizing and documenting object oriented systems UML combines the concepts of OOA OODOMT and OOSE and is intended as a standard in the domain of object oriented analysis and design Due to the missing formal mathematical foundation of UML the syntax and the semantics of a number of UML constructs are not precisely defined.This paper outlines a proposal for the formal foundation of UML that is based on a mathematical system model

Cornel Klein, Christian Prehofer, Bernhard Rumpe

In this paper, we introduce a graphic specification technique, called state transition diagrams (STD), and show the application to the feature interaction problem. Using a stream-based formal semantics, we provide refinement rules for STDs. Refinements define an implementation relation on STD specifications. We view features as particular refinements which add previously unspecified behavior to a given STD specification. The refinement relation is then used to add features, and to define the notion of conflicting features. Our techniques are demonstrated by a systematic development of an example given in [25].