Contributions in Merlin

A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models.

When performed on a model, a set of operations (e.g., queries or model transformations) rarely uses all the information present in the model. Unintended underuse of a model can indicate various problems: the model may contain more detail than necessary or the operations may be immature or erroneous. Analyzing the footprints of the operations - i.e., the part of a model actually used by an operation - is a simple technique to diagnose and analyze such problems. However, precisely calculating the footprint of an operation is expensive, because it requires analyzing the operation's execution trace.In this paper, we present an automated technique to estimate the footprint of an operation without executing it. We evaluate our approach by applying it to 75 models and five operations. Our technique provides software engineers with an efficient, yet precise, evaluation of the usage of their models.

In this paper we consider a canonical stochastic overlapping generations economy with sequentially complete markets. We examine how aggregate and individual shocks translate to changes in the distribution of wealth and how these movements in the wealth distribution affect asset prices and the interest rate. We show that effects are generally small if agents have identical beliefs but that differences in opinion lead to large movements in the wealth distribution. The interplay of belief heterogeneity and life-cycle savings motives creates very large movements of asset prices and can potentially generate realistic moments of asset returns.

In many optimization and variational problems, abstract set constraints of the type $h(x) \in C$, $C$ polyhedral, appear. Such set constraints are often handled separately in its algebraic form when deriving stability (and solvability) conditions, since standard constraint qualifications may fail to hold. We show how such problems can be rewritten in traditional inequality and equation form in such a way that results on stability of solutions and feasible points for the related classical models can be directly applied. The paper is co-authored by Bernd Kummer, Humboldt University Berlin.

Compliant actuation contributes enormously in legged locomotion robotics since it is able to alleviate control efforts in improving the robot’s adaptability and energy efficiency. In this paper, we present a novel design of a variable stiffness rotary actuator, called MESTRAN, which was especially targeted to address the limitations in terms of the amount of energy and time required to vary the stiffness of an actuated joint. We have constructed a mechanical model in simulation and a physical prototype. We conducted a series of experiments to validate the performance of the MESTRAN actuator prototype. The results from the simulation and experiments show that MESTRAN allows independent control of stiffness and position of an actuated rotary joint with a large operational range and high speed. The torque-displacement relationship is close to linear. Lastly, the MESTRAN actuator is energy-efficient since a certain stiffness level is maintained without energy input.

Fish excel in their swimming capabilities. These result from a dynamic interplay of actuation, passive properties of fish body, and interaction with the surrounding fluid. In particular, fish are able to exploit wakes that are generated by objects in flowing water. A powerful demonstration that this is largely due to passive body properties are studies on dead trout. Inspired by that, we developed a multi joint swimming platform that explores the potential of a passive dynamic mechanism. The platform has one actuated joint only, followed by three passive joints whose stiffness can be changed online, individually, and can be set to an almost arbitrary nonlinear stiffness profile. In a set of experiments, using online optimization, we investigated how the platform can discover optimal stiffness distribution along its body in response to different frequency and amplitude of actuation. We show that a heterogeneous stiffness distribution - each joint having a different value - outperforms a homogeneous one in producing thrust. Furthermore, different gaits emerged in different settings of the actuated joint. This work illustrates the potential of online adaption of passive body properties, leading to optimized swimming, especially in an unsteady environment.

Biomimetics is a fruitful combination of biology and engineering, leading not only to technological innovations but also new nsights into biological questions. In this ongoing project, embodied artificial intelligence (embodied AI), artificial evolution and palaeontology are combined to investigate the functional morphology of bivalves. This cross-fertilization allows to expand biomimetics from current biological systems to the whole evolutionary history and to apply the synthetic approach common in embodied AI as a method to tackle open palaeontological questions. So far, a robotic platform has been built to mimic the burrowing technique applied by bivalves. First results show interesting insights into underwater burrowing. We plan to build a more complex version of the system and to perform evolutionary robotics experiments.