Automatische Klassifikation von Anforderungen zur Unterstützung von Qualitätssicherungsprozessen

The article may be also found at https://dl.gi.de/handle/20.500.12116/1040.Während des Requirements-Engineering werden Anforderungen an zu entwickelnde Systeme und Komponenten in Form von Lastenheften dokumentiert. Diese Lastenhefte enthalten neben rechtlich relevanten Anforderungen weitere Inhalte wie Erklärungen, Zusammenfassungen und Abbildungen. Um zwischen Anforderungen und solchen Zusatzinformationen unterscheiden zu können, werden alle Lastenheftinhalte manuell als Anforderung oder Information eingestuft. Analysen haben ergeben, dass diese Klassifikation nicht konsequent durchgeführt wird. Daher liegt es nahe, diese Klassifikation zu automatisieren. In diesem Beitrag wird ein Ansatz vorgestellt, der diese Klassifikation mithilfe von Techniken aus dem Bereich Data Mining und Machine Learning automatisch durchführen kann

Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds

In this work we present a trajectory Optimization framework for whole-body motion planning through contacts. We demonstrate how the proposed approach can be applied to automatically discover different gaits and dynamic motions on a quadruped robot. In contrast to most previous methods, we do not pre-specify contact switches, timings, points or gait patterns, but they are a direct outcome of the optimization. Furthermore, we optimize over the entire dynamics of the robot, which enables the optimizer to fully leverage the capabilities of the robot. To illustrate the spectrum of achievable motions, here we show eight different tasks, which would require very different control structures when solved with state-of-the-art methods. Using our trajectory Optimization approach, we are solving each task with a simple, high level cost function and without any changes in the control structure. Furthermore, we fully integrated our approach with the robot's control and estimation framework such that optimization can be run online. By demonstrating a rough manipulation task with multiple dynamic contact switches, we exemplarily show how optimized trajectories and control inputs can be directly applied to hardware.Comment: Video: https://youtu.be/sILuqJBsyK

Robust Whole-Body Motion Control of Legged Robots

We introduce a robust control architecture for the whole-body motion control of torque controlled robots with arms and legs. The method is based on the robust control of contact forces in order to track a planned Center of Mass trajectory. Its appeal lies in the ability to guarantee robust stability and performance despite rigid body model mismatch, actuator dynamics, delays, contact surface stiffness, and unobserved ground profiles. Furthermore, we introduce a task space decomposition approach which removes the coupling effects between contact force controller and the other non-contact controllers. Finally, we verify our control performance on a quadruped robot and compare its performance to a standard inverse dynamics approach on hardware.Comment: 8 Page

Using tools to assist identification of non-requirements in requirements specifications : A controlled experiment

[Context and motivation] In many companies, textual fragments in specification documents are categorized into requirements and non-requirements. This categorization is important for determining liability, deriving test cases, and many more decisions. In practice, this categorization is usually performed manually, which makes it labor-intensive and error-prone. [Question/Problem] We have developed a tool to assist users in this task by providing warnings based on classification using neural networks. However, we currently do not know whether using the tool actually helps increasing the classification quality compared to not using the tool. [Principal idea/results] Therefore, we performed a controlled experiment with two groups of students. One group used the tool for a given task, whereas the other did not. By comparing the performance of both groups, we can assess in which scenarios the application of our tool is beneficial. [Contribution] The results show that the application of an automated classification approach may provide benefits, given that the accuracy is high enough

“What does my classifier learn?” : A visual approach to understanding natural language text classifiers

Neural Networks have been utilized to solve various tasks such as image recognition, text classification, and machine translation and have achieved exceptional results in many of these tasks. However, understanding the inner workings of neural networks and explaining why a certain output is produced are no trivial tasks. Especially when dealing with text classification problems, an approach to explain network decisions may greatly increase the acceptance of neural network supported tools. In this paper, we present an approach to visualize reasons why a classification outcome is produced by convolutional neural networks by tracing back decisions made by the network. The approach is applied to various text classification problems, including our own requirements engineering related classification problem. We argue that by providing these explanations in neural network supported tools, users will use such tools with more confidence and also may allow the tool to do certain tasks automatically

Automatic classification of requirements based on convolutional neural networks

The results of the requirements engineering process are predominantly documented in natural language requirements specifications. Besides the actual requirements, these documents contain additional content such as explanations, summaries, and figures. For the later use of requirements specifications, it is important to be able to differentiate between legally relevant requirements and other auxiliary content. Therefore, one of our industry partners demands the requirements engineers to manually label each content element of a requirements specification as "requirement" or "information". However, this manual labeling task is time-consuming and error-prone. In this paper, we present an approach to automatically classify content elements of a natural language requirements specification as "requirement" or "information". Our approach uses convolutional neural networks. In an initial evaluation on a real-world automotive requirements specification, our approach was able to detect requirements with a precision of 0.73 and a recall of 0.89. The approach increases the quality of requirements specifications in the sense that it discriminates important content for following activities (e.g., which parts of the specification do I need to test?)

Automatische Erkennung von Model Smells in Simulink-Modellen

Simulink-Modelle werden mit zunehmender Komplexität anfällig für Qualitätsdefizite. Die Ursache dafür sind strukturelle Probleme, sogenannte Model Smells. Die manuelle Erkennung von Model Smells ist aufwändig, daher wird ein Verfahren zur automatisierten Erkennung von Model Smells vorgestellt. Dieses ermöglicht es Modellierern, effizient die Qualität von Modellen zu verbessern