Technische Universität Darmstadt

About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.

DeepBugs: a learning approach to name-based bug detection

Abstract: Natural language elements in source code, e.g., the names of variables and functions, convey useful information. However, most existing bug detection tools ignore this information and therefore miss some classes of bugs. The few existing name-based bug detection approaches reason about names on a syntactic level and rely on manually designed and tuned algorithms to detect bugs. This paper presents DeepBugs, a learning approach to name-based bug detection, which reasons about names based on a semantic representation and which automatically learns bug detectors instead of manually writing them. We formulate bug detection as a binary classification problem and train a classifier that distinguishes correct from incorrect code. To address the challenge that effectively learning a bug detector requires examples of both correct and incorrect code, we create likely incorrect code examples from an existing corpus of code through simple code transformations. A novel insight learned from our work is that learning from artificially seeded bugs yields bug detectors that are effective at finding bugs in real-world code. We implement our idea into a framework for learning-based and name-based bug detection. Three bug detectors built on top of the framework detect accidentally swapped function arguments, incorrect binary operators, and incorrect operands in binary operations. Applying the approach to a corpus of 150,000 JavaScript files yields bug detectors that have a high accuracy (between 89% and 95%), are very efficient (less than 20 milliseconds per analyzed file), and reveal 102 programming mistakes (with 68% true positive rate) in real-world code.

Rearchitecting the UML infrastructure

Abstract: Metamodeling is one of the core foundations of computer-automated multiparadigm modeling. However, there is currently little agreement about what form the required metamodeling approach should take and precisely what role metamodels should play. This article addresses the problem by first describing some fundamental problems in the industry's leading metamodeling technology, the UML framework, and then explaining how this framework could be rearchitected to overcome these problems. Three main issues are identified in the current framework: the dual classification problem arising from the need to capture both the logical and physical classification of model elements, the class/object duality problem arising from the need to capture both the classlike and objectlike facets of some model elements, and the replication of concepts problem arising from the need to define certain concepts multiple times. Three main proposals for rearchitecting the UML framework to overcome these problems are then presented: the separation of logical and physical classification dimensions, the unification of the class and object facets of model elements, and the enhancement of the instantiation mechanism to allow definitions to transcend multiple levels. The article concludes with a discussion of other practical issues involved in rearchitecting the UML modeling framework in the proposed way.

Energy Band Alignment between Anatase and Rutile TiO2

Abstract: Using photoelectron spectroscopy, the interface formation of anatase and rutile TiO2 with RuO2 and tin-doped indium oxide (ITO) is studied. It is consistently found that the valence band maximum of rutile is 0.7 ± 0.1 eV above that of anatase. The alignment is confirmed by electronic structure calculations, which further show that the alignment is related to the splitting of the energy bands formed by the O 2pz lone-pair orbitals. The alignment can explain the different electron concentrations in doped anatase and rutile and the enhanced photocatalytic activity of mixed phase particles.

Hydrogenation of Glucose to Sorbitol over Nickel and Ruthenium Catalysts

Abstract: Hydrogenation of aqueous glucose solution was performed in batch and continuous reactors using supported nickel and ruthenium catalysts. Preparation methods were precipitation, impregnation, sol-gel and template syntheses, and SiO2, TiO2, Al2O3 and carbon were used as support materials. A procedure for the one-step synthesis of templated metal on support catalysts was established. The influence of support material and preparation methods was studied and the results were compared with those of an industrial nickel catalyst. In a detailed study taking more than 1100 h time on stream we investigated the deactivation of the industrial catalyst and a supported ruthenium catalyst, and evaluated the deactivation mechanisms. As yet unpublished side products of the glucose hydrogenation were identified and a scheme of the reaction network was set up.