Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

Knowledge extraction and representation aims to identify information and to transform it into a machine-readable format. Knowledge representations support Information Retrieval tasks such as searching for single statements, documents, or metadata. Requirements specifications of complex systems such as automotive software systems are usually divided into different subsystem specifications. Nevertheless, there are semantic relations between individual documents of the separated subsystems, which have to be considered in further processes (e.g. dependencies). If requirements engineers or other developers are not aware of these relations, this can lead to inconsistencies or malfunctions of the overall system. Therefore, there is a strong need for tool support in order to detects semantic relations in a set of large natural language requirements specifications. In this work we present a knowledge extraction approach based on an explicit knowledge representation of the content of natural language requirements as a semantic relation graph. Our approach is fully automated and includes an NLP pipeline to transform unrestricted natural language requirements into a graph. We split the natural language into different parts and relate them to each other based on their semantic relation. In addition to semantic relations, other relationships can also be included in the graph. We envision to use a semantic search algorithm like spreading activation to allow users to search different semantic relations in the graph

Threshold-Resummed Cross Section for the Drell-Yan Process in Pion-Nucleon Collisions at COMPASS

We present a study of the Drell-Yan process in pion-proton collisions including next-to-leading-logarithmic threshold-resummed contributions. We analyze rapidity-integrated as well as rapidity-differential cross sections in the kinematic regime relevant for the COMPASS fixed target experiment. We find that resummation leads to a significant enhancement of the cross section compared to fixed-order calculations in this regime. Particularly large corrections arise at large forward and backward rapidities of the lepton pair. We also study the scale dependence of the cross section and find it to be substantially reduced by threshold resummation.Comment: 8 pages, 7 figure

What Am I Testing and Where? Comparing Testing Procedures based on Lightweight Requirements Annotations

[Context] The testing of software-intensive systems is performed in different test stages each having a large number of test cases. These test cases are commonly derived from requirements. Each test stages exhibits specific demands and constraints with respect to their degree of detail and what can be tested. Therefore, specific test suites are defined for each test stage. In this paper, the focus is on the domain of embedded systems, where, among others, typical test stages are Software- and Hardware-in-the-loop. [Objective] Monitoring and controlling which requirements are verified in which detail and in which test stage is a challenge for engineers. However, this information is necessary to assure a certain test coverage, to minimize redundant testing procedures, and to avoid inconsistencies between test stages. In addition, engineers are reluctant to state their requirements in terms of structured languages or models that would facilitate the relation of requirements to test executions. [Method] With our approach, we close the gap between requirements specifications and test executions. Previously, we have proposed a lightweight markup language for requirements which provides a set of annotations that can be applied to natural language requirements. The annotations are mapped to events and signals in test executions. As a result, meaningful insights from a set of test executions can be directly related to artifacts in the requirements specification. In this paper, we use the markup language to compare different test stages with one another. [Results] We annotate 443 natural language requirements of a driver assistance system with the means of our lightweight markup language. The annotations are then linked to 1300 test executions from a simulation environment and 53 test executions from test drives with human drivers. Based on the annotations, we are able to analyze how similar the test stages are and how well test stages and test cases are aligned with the requirements. Further, we highlight the general applicability of our approach through this extensive experimental evaluation. [Conclusion] With our approach, the results of several test levels are linked to the requirements and enable the evaluation of complex test executions. By this means, practitioners can easily evaluate how well a systems performs with regards to its specification and, additionally, can reason about the expressiveness of the applied test stage.TU Berlin, Open-Access-Mittel - 202

Evaluation of a specification approach for vehicle functions using activity diagrams in requirements documents

Rising complexity of systems has long been a major challenge in requirements engineering. This manifests in more extensive and harder to understand requirements documents. At the Daimler AG, an approach is applied that combines the use of activity diagrams with natural language specifications to specify vehicle functions. The approach starts with an activity diagram that is created to get an early overview. The contained information is then transferred to a textual requirements document, where details are added and the behavior is refined. While the approach aims at reducing efforts needed to understand a function’s behavior, the application of the approach itself causes new challenges on its own. By examining existing specifications at Daimler, we identified nine categories of inconsistencies and deviations between activity diagrams and their textual representations. This paper extends a previous case study on the subject by presenting additional data we acquired. Our analysis indicates that a coexistence of textual and graphical representations of models without proper tool support results in inconsistencies and deviations

Soft-Gluon Resummation and the Valence Parton Distribution Function of the Pion

We determine the valence parton distribution function of the pion by performing a new analysis of data for the Drell-Yan process $\pi^- N\to \mu^+\mu^-X$. Compared to previous analyses, we include next-to-leading-logarithmic threshold resummation effects in the calculation of the Drell-Yan cross section. As a result of these, we find a considerably softer valence distribution at high momentum fractions $x$ than obtained in previous next-to-leading order analyses, in line with expectations based on perturbative-QCD counting rules or Dyson-Schwinger equations.Comment: 5 pages, 3 figures, accepted for publication in Physical Review Letter

Soft-gluon Resummation for High-pT Inclusive-Hadron Production at COMPASS

We study the cross section for the photoproduction reaction gamma N -> h X in fixed-target scattering at COMPASS, where the hadron h is produced at large transverse momentum. We investigate the role played by higher-order QCD corrections to the cross section. In particular we address large logarithmic "threshold" corrections to the rapidity dependent partonic cross sections, which we resum to all orders at next-to-leading accuracy. In our comparison to the experimental data we find that the threshold contributions are large and improve the agreement between data and theoretical predictions significantly.Comment: 13 pages, 7 figures, journal versio

What is a good textual representation of activity diagrams in requirements documents?

The use of graphical models has become a widely adopted approach to specify requirements of complex systems. Still, in practice, graphical models are often accompanied by textual descriptions to provide more detail, because of legal considerations, and to enable stakeholders with different backgrounds to understand a requirements document. One of our industry partners (Daimler AG) uses activity diagrams to specify vehicle functions in combination with a textual representation thereof in their requirements documents. Since graphical and textual representations serve different purposes, it is not obvious how textual representations of activity diagrams should be structured. In this paper, we present different textual representations of activity diagrams for use in requirements documents. The representation currently in use is presented as well as four alternatives. For each representation, we discuss advantages and disadvantages. To evaluate the representations, we asked five stakeholders of one system to create a preference ranking of the representations. The resulting ranking showed that the currently used representation is not considered to be the best possible option. The stakeholders’ favorite textual representation emphasizes structural similarity with the activity diagram, which however does not resemble the diagram’s structure exactly

“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