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Context: Conditional statements like "If A and B then C" are core elements for describing software requirements. However, there are many ways to express such conditionals in natural language and also many ways how they can be interpreted. We hypothesize that conditional statements in requirements are a source of ambiguity, potentially affecting downstream activities such as test case generation negatively. Objective: Our goal is to understand how specific conditionals are interpreted by readers who work with requirements. Method: We conduct a descriptive survey with 104 RE practitioners and ask how they interpret 12 different conditional clauses. We map their interpretations to logical formulas written in Propositional (Temporal) Logic and discuss the implications. Results: The conditionals in our tested requirements were interpreted ambiguously. We found that practitioners disagree on whether an antecedent is only sufficient or also necessary for the consequent. Interestingly, the disagreement persists even when the system behavior is known to the practitioners. We also found that certain cue phrases are associated with specific interpretations. Conclusion: Conditionals in requirements are a source of ambiguity and there is not just one way to interpret them formally. This affects any analysis that builds upon formalized requirements (e.g., inconsistency checking, test-case generation). Our results may also influence guidelines for writing requirements.

Creating datasets manually by human annotators is a laborious task that can lead to biased and inhomogeneous labels. We propose a flexible, semi-automatic framework for labeling data for relation extraction. Furthermore, we provide a dataset of preprocessed sentences from the requirements engineering domain, including a set of automatically created as well as hand-crafted labels. In our case study, we compare the human and automatic labels and show that there is a substantial overlap between both annotations.

We report on two types of Raman laser sources emitting in the near and middle ultraviolet spectral ranges by the use of a solarization-resilient gas-filled inhibited-coupling (IC) hollow-core photonic-crystal fiber (HCPCF) with record low transmission loss (minimum of 5 dB/km at 480 nm). The first source type emits a Raman comb generated in a hydrogen-filled HCPCF pumped by a 355 nm wavelength microchip nanosecond pulsed laser. The generated comb lines span from 270 nm to the near-infrared region with no less than 20 lines in the 270-400 nm wavelength range. The second type stands for the first dual-wavelength Raman source tuned to the ozone absorption band in the ultraviolet. Such dual-wavelength source emits at either 266 nm and 289 nm, or 266 nm and 299 nm. The relative power of the pair components is set to optimize the sensitivity of ozone detection in differential absorption lidar (DIAL). The source's physical package represents more than 10-fold size-reduction relative to current DIAL lasers, thus opening new opportunities in on-field ozone monitoring and mapping. Both Raman sources exhibit a very small footprint and are solarization-free.

We provide a comprehensive summary of concepts from Calabi-Yau motives relevant to the computation of multi-loop Feynman integrals. From this we derive several consequences for multi-loop integrals in general, and we illustrate them on the example of multi-loop banana integrals. For example, we show how Griffiths transversality, known from the theory of variation of mixed Hodge structures, leads quite generically to a set of quadratic relations among maximal cut integrals associated to Calabi-Yau motives. These quadratic relations then naturally lead to a compact expression for $l$-loop banana integrals in $D=2$ dimensions in terms of an integral over a period of a Calabi-Yau $(l-1)$-fold. This new integral representation generalizes in a natural way the known representations for $l\le 3$ involving logarithms with square root arguments and iterated integrals of Eisenstein series. In a second part, we show how the results obtained by some of the authors in earlier work can be extended to dimensional regularization. We present a method to obtain the differential equations for banana integrals with an arbitrary number of loops in dimensional regularization without the need to solve integration-by-parts relations. We also present a compact formula for the leading asymptotics of banana integrals with an arbitrary number of loops in the large momentum limit. This generalizes the novel $\widehat{\Gamma}$-class introduced by some of the authors to dimensional regularization and provides a convenient boundary condition to solve the differential equations for the banana integrals. As an application, we present for the first time numerical results for equal-mass banana integrals with up to four loops and up to second order in the dimensional regulator., Comment: 112 pages, 3 figures

Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and app stores is challenging since (1) about 70% of user comments contain noisy, irrelevant information, (2) the amount of user comments grows daily making manual analysis unfeasible, and (3) user comments are written in different languages. Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for this task. In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments. Specifically, we train both monolingual and multilingual BERT models and compare the performance with state-of-the-art methods. We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets. However, we also observed that the application of heavyweight TL models does not necessarily lead to better performance. In fact, our multilingual BERT models perform worse than traditional ML methods.

Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities (e.g., automatic derivation of suitable test cases). However, we lack an approach capable of extracting causal relations from natural language with reasonable performance. In this paper, we present our tool CATE (CAusality Tree Extractor), which is able to parse the composition of a causal relation as a tree structure. CATE does not only provide an overview of causes and effects in a sentence, but also reveals their semantic coherence by translating the causal relation into a binary tree. We encourage fellow researchers and practitioners to use CATE at https://causalitytreeextractor.com/

[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective & Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on Recursive Neural Tensor Networks. Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74 % in the evaluation on the Causality Treebank. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community.

Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models are not trusted in the clinical routine due to failing silently in out-of-distribution (OOD) data. We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. The proposed approach can be seamlessly integrated into state-of-the-art segmentation pipelines without requiring changes in model architecture or training procedure, and can therefore be used to assess the suitability of pre-trained models to new data. We validate our method with a patch-based nnU-Net architecture trained with a multi-institutional dataset and find that it effectively detects samples that the model segments incorrectly.

Currently, practitioners follow a top-down approach in automotive development projects. However, recent studies have shown that this top-down approach is not suitable for the implementation and testing of modern automotive systems. Specifically, practitioners increasingly fail to specify requirements and tests for systems with complex component interactions (e.g., e-mobility systems). In this paper, we address this research gap and propose an integrated and iterative scenario-based technique for the specification of requirements and test scenarios. Our idea is to combine both a top-down and a bottom-up integration strategy. For the top-down approach, we use a behavior-driven development (BDD) technique to drive the modeling of high-level system interactions from the user's perspective. For the bottom-up approach, we discovered that natural language processing (NLP) techniques are suited to make textual specifications of existing components accessible to our technique. To integrate both directions, we support the joint execution and automated analysis of system-level interactions and component-level behavior. We demonstrate the feasibility of our approach by conducting a case study at Kostal (Tier1 supplier). The case study corroborates, among other things, that our approach supports practitioners in improving requirements and test specifications for integrated system behavior.

The temporal coincidences of events detected in four neutrino detectors and two gravitational antennas still remains among the most puzzling phenomena associated with SN1987A. The coincidences form a six-hour signal approximately coincident in time with the well-known LSD signal at 2h52m UT on 23/02/1987. After 30 years of research, the characteristics and the shape of the six-hour signal have been studied quite well, but the mechanisms of its formation have not been fully understood as of yet. Here we suggest that data obtained from another technology, radioactive decays, might provide new insights into the origin of signals previously seen in neutrino detectors and gravity wave detectors. On August 17, 2017, at 12h41m UT, the GW170817 signal was detected by LIGO and Virgo. At the same time, an approximately 7-hour long signal coincident with GW170817 was detected in the Si/Cl experiment on precision measurement of the $^{32}$Si half-life. We show that the Si/Cl signal is unexpectedly similar to the six-hour signal from SN1987A. In addition, we establish that the sources of the coinciding events are similar to those of the Si/Cl signal. To explain the surprising similarities in both signals, we present a mechanism which could in principle account for this phenomenon in terms of a local increase in the density of axionic dark matter induced by a gravity wave., Comment: 8 pages, 5 figures, 3 tables. This is the author's version and not the final typeset

We explore what benefits network position in online business social networks like LinkedIn might confer to an aspiring entrepreneur. We compare two network attributes, size and embeddedness, and two actor attributes, location and diversity, between virtual and real-world networks. The promise of social networks like LinkedIn is that network friends enable easier access to critical resources such as legal and financial services, customers, and business partners. Our setting consists of one million public member profiles of the German business networking site XING (a German version of LinkedIn) from which we extracted the network structure of 15,000 start-up entrepreneurs from 12 large German universities. We find no positive effect of virtual network size and embeddedness, and small positive effects of location and diversity.

Complex structures can only form in a universe that allows for bound states. While this is clearly observed in three-dimensions, added degrees of freedom in a higher-dimensional space preclude the immediate assumption that binding potentials can in fact exist. In this paper, we derive a constraint on the dimensionality of a universe in the presence of an arbitrary set of forces. We then apply this constraint to systems with several example potentials. In doing so, we find that bound states in higher than 3 dimensions are in fact possible under specific circumstances which we characterize., Comment: 13 pages, 4 figures, added clarifying text and a new figure which shows an additional combination of forces

We have reanalyzed data obtained by Potter in a 1923 experiment aimed at testing whether the accelerations of test masses in the Earth's gravitational field are independent of their compositions. Although Potter concludes that the accelerations of his samples compared to a brass standard were individually consistent with a null result, we show that the pattern formed from a combined plot of all of his data suggests the presence of a fifth force coupling to baryon number., Comment: 9 pages, 5 figures

Recently we drew attention to the fact that most recent 5th force searches and tests of the weak equivalence principle (WEP) utilize only one or two pairs of test samples. We argue that, despite the great precision of these experiments, the lack of diversity of samples may mean they are unable to detect new composition-dependent forces, which requires the observation of a pattern in the data. Such a pattern was observed in the experiment by E\"{o}tv\"{o}s, Pek\'{a}r, and Fekete (EPF), the last high precision test of the WEP which used a significant number of different samples. We advocate for new experiments utilizing a sufficient number and range of samples to either confirm or refute the pattern found in the EPF experimental data., Comment: 4 pages, 1 figure, uses moriond.cls, contribution to the 2021 Gravitation session of the 55th Rencontres de Moriond

Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro F_1 score of 83%, which corroborates the feasibility of our approach.

System behavior is often expressed by causal relations in requirements (e.g., If event 1, then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language is still an open research challenge as existing approaches fail to extract causality with reasonable performance. We understand causality extraction from requirements as a two-step problem: First, we need to detect if requirements have causal properties or not. Second, we need to understand and extract their causal relations. At present, though, we lack knowledge about the form and complexity of causality in requirements, which is necessary to develop a suitable approach addressing these two problems. We conduct an exploratory case study with 14,983 sentences from 53 requirements documents originating from 18 different domains and shed light on the form and complexity of causality in requirements. Based on our findings, we develop a tool-supported approach for causality detection (CiRA). This constitutes a first step towards causality extraction from NL requirements. We report on a case study and the resulting tool-supported approach for causality detection in requirements. Our case study corroborates, among other things, that causality is, in fact, a widely used linguistic pattern to describe system behavior, as about a third of the analyzed sentences are causal. We further demonstrate that our tool CiRA achieves a macro-F1 score of 82 % on real word data and that it outperforms related approaches with an average gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision. Finally, we disclose our open data sets as well as our tool to foster the discourse on the automatic detection of causality in the RE community.