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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/

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.

Welcome to the First International Workshop on Requirements Engineering for Explainable Systems (RE4ES), where we aim to advance requirements engineering (RE) for explainable systems, foster interdisciplinary exchange, and build a community. On the one hand, we believe that the methods and techniques of the RE community can add much value to explainability research. On the other hand, we have to ensure that we develop techniques fitted to the needs of other communities.This first workshop explores synergies between the RE community and other communities already researching explainability.To this end, we have based our agenda on a mix of paper presentations from authors of different domains, one keynote from industry and one from research, as well as interactive activities to stimulate lively discussions.

The need for systems to explain behavior to users has become more evident with the rise of complex technology like machine learning or self-adaptation. In general, the need for an explanation arises when the behavior of a system does not match the user’s expectations. However, there may be several reasons for a mismatch including errors, goal conflicts, or multi-agent interference. Given the various situations, we need precise and agreed descriptions of explanation needs as well as benchmarks to align research on explainable systems. In this paper, we present a taxonomy that structures needs for an explanation according to different reasons. We focus on explanations to improve the user interaction with the system. For each leaf node in the taxonomy, we provide a scenario that describes a concrete situation in which a software system should provide an explanation. These scenarios, called explanation cases, illustrate the different demands for explanations. Our taxonomy can guide the requirements elicitation for explanation capabilities of interactive intelligent systems and our explanation cases build the basis for a common benchmark. We are convinced that both, the taxonomy and the explanation cases, help the community to align future research on explainable systems.

Two-thirds of the people who buy a new car prefer to use a substitute instead of the built-in navigation system. However, for many applications, knowledge about a user's intended destination and route is crucial. For example, suggestions for available parking spots close to the destination can be made or ride-sharing opportunities along the route are facilitated. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data. The approach follows a three-step procedure: First, a $k$-d tree-based space discretization is performed, mapping GPS locations to discrete regions. Secondly, a recurrent neural network is trained to predict the destination based on partial sequences of trajectories. The neural network produces destination scores, signifying the probability of each region being the destination. Finally, the routes to the most probable destinations are calculated. To evaluate the method, we compare multiple neural architectures and present the experimental results of the destination prediction. The experiments are based on two public datasets of non-personalized, timestamped GPS locations of taxi trips. The best performing models were able to predict the destination of a vehicle with a mean error of 1.3 km and 1.43 km respectively., Comment: 2020 IEEE Intelligent Vehicles Symposium

Trace Link Recovery tries to identify and link related existing requirements with each other to support further engineering tasks. Existing approaches are mainly based on algebraic Information Retrieval or machine-learning. Machine-learning approaches usually demand reasonably large and labeled datasets to train. Algebraic Information Retrieval approaches like distance between tf-idf scores also work on smaller datasets without training but are limited in providing explanations for trace links. In this work, we present a Trace Link Recovery approach that is based on an explicit representation of the content of requirements as a semantic relation graph and uses Spreading Activation to answer trace queries over this graph. Our approach is fully automated including an NLP pipeline to transform unrestricted natural language requirements into a graph. We evaluate our approach on five common datasets. Depending on the selected configuration, the predictive power strongly varies. With the best tested configuration, the approach achieves a mean average precision of 40% and a Lag of 50%. Even though the predictive power of our approach does not outperform state-of-the-art approaches, we think that an explicit knowledge representation is an interesting artifact to explore in Trace Link Recovery approaches to generate explanations and refine results. Trace Link Recovery versucht, verwandte bestehende Anforderungen zu identifizieren und miteinander zu verknüpfen, um weitere technische Aufgaben zu unterstützen. Bestehende Ansätze basieren hauptsächlich auf algebraischem Information Retrieval oder maschinellem Lernen. Machine-Learning-Ansätze erfordern in der Regel relativ große und vorab klassifizierte Datensätze zum Trainieren. Algebraische Ansätze wie z.B. tf-idf funktionieren auch bei kleineren Datensätzen ohne Training, sind aber in der Bereitstellung von Erklärungen für Verknüpfungen begrenzt. In dieser Arbeit stellen wir einen Trace Link Recovery Ansatz vor, der auf einer expliziten Darstellung des Inhalts von Anforderungen durch einen semantischen Relationsgraphs basiert und die Ausbreitungsaktivierung verwendet, um Verknüpfungen über diesen Graphen zu identifizieren. Unser Ansatz ist vollständig automatisiert, einschließlich einer NLP-Pipeline zur Umwandlung uneingeschränkt natürlichsprachlicher Anforderungen in einen Graphen. Wir evaluieren unseren Ansatz anhand von fünf öffentlichen Datensätzen. Abhängig von der gewählten Konfiguration variiert die Performanz stark. Mit der am besten getesteten Konfiguration erreicht der Ansatz eine Genauigkeit von 40% und einen Lag von 50%. Auch wenn die Vorhersagekraft unseres Ansatzes dem Stand der Technik nicht überlegen ist, sind wir der Meinung, dass eine explizite Wissensrepräsentation ein interessantes Artefakt ist, das in Trace Link Recovery Ansätzen untersucht werden sollte, um Erklärungen zu generieren und die Ergebnisse zu verfeinern.

With the increasing complexity of Cyber-Physical Systems, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.

[Context:] Model-based testing is an instrument for automated generation of test cases. It requires identifying requirements in documents, understanding them syntactically and semantically, and then translating them into a test model. One light-weight language for these test models are Cause-Effect-Graphs (CEG) that can be used to derive test cases. [Problem:] The creation of test models is laborious and we lack an automated solution that covers the entire process from requirement detection to test model creation. In addition, the majority of requirements is expressed in natural language (NL), which is hard to translate to test models automatically. [Principal Idea:] We build on the fact that not all NL requirements are equally unstructured. We found that 14 % of the lines in requirements documents of our industry partner contain "pseudo-code"-like descriptions of business rules. We apply Machine Learning to identify such semi-structured requirements descriptions and propose a rule-based approach for their translation into CEGs. [Contribution:] We make three contributions: (1) an algorithm for the automatic detection of semi-structured requirements descriptions in documents, (2) an algorithm for the automatic translation of the identified requirements into a CEG and (3) a study demonstrating that our proposed solution leads to 86 % time savings for test model creation without loss of quality.

The technological advancements of recent years have steadily increased the complexity of vehicle-internal software systems, and the ongoing development towards autonomous driving will further aggravate this situation. This is leading to a level of complexity that is pushing the limits of existing vehicle software architectures and system designs. By changing the software structure to a service-based architecture, companies in other domains successfully managed the rising complexity and created a more agile and future-oriented development process. This paper presents a case-study investigating the feasibility and possible effects of changing the software architecture for a complex driver assistance function to a microservice architecture. The complete procedure is described, starting with the description of the software-environment and the corresponding requirements, followed by the implementation, and the final testing. In addition, this paper provides a high-level evaluation of the microservice architecture for the automotive use-case. The results show that microservice architectures can reduce complexity and time-consuming process steps and makes the automotive software systems prepared for upcoming challenges as long as the principles of microservice architectures are carefully followed.

Creating glossaries for large corpora of requirments is an important but expensive task. Glossary term extraction methods often focus on achieving a high recall rate and, therefore, favor linguistic proecssing for extracting glossary term candidates and neglect the benefits from reducing the number of candidates by statistical filter methods. However, especially for large datasets a reduction of the likewise large number of candidates may be crucial. This paper demonstrates how to automatically extract relevant domain-specific glossary term candidates from a large body of requirements, the CrowdRE dataset. Our hybrid approach combines linguistic processing and statistical filtering for extracting and reducing glossary term candidates. In a twofold evaluation, we examine the impact of our approach on the quality and quantity of extracted terms. We provide a ground truth for a subset of the requirements and show that a substantial degree of recall can be achieved. Furthermore, we advocate requirements coverage as an additional quality metric to assess the term reduction that results from our statistical filters. Results indicate that with a careful combination of linguistic and statistical extraction methods, a fair balance between later manual efforts and a high recall rate can be achieved.

We would like to welcome you to the 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE'18). This interdisciplinary workshop is intended to explore and extend the synergies between Artificial Intelligence and Requirements Engineering. Our objective is to discover Requirements Engineering areas that may benefit from the application of AI tools and techniques. We intend to inspire a new and broad community for interdisciplinary discussions concerning novel research directions for Requirements Engineering and Artificial Intelligence.

Automotive software systems are often characterized by a set of features that are implemented through a network of communicating components. It is common practice to implement or adapt features by an ad hoc (re) use of signals that originate from components of another feature. Thereby, over time some components become so-called implicit communal components. These components increase the necessary efforts for several development activities because they introduce feature dependencies. Refactoring implicit communal components reduces these efforts but also costs refactoring effort. In this paper, we provide empirical evidence that implicit communal components exist in industrial automotive systems. For two cases, we show that less than 10% of the components are responsible for more than 90% of the feature dependencies. Secondly, we propose a refactoring approach for implicit communal components, which makes them explicit by moving them to a dedicated platform component layer. Finally, we characterize implicit communal components as technical debt, which is a metaphor for suboptimal solutions having short-term benefits but causing a long-term negative impact. With this metaphor, we describe the trade-off between accepting the negative effects of implicit communal components and spending the necessary refactoring costs.

Many requirements engineering approaches structure and specify requirements based on the notion of modes or system states. The set of all modes is usually considered as the mode model of a system or problem domain.

Innovation management of automotive control sys- tems is a challenging issue not least because of the need to handle short iterative life cycles and families of these systems. After collecting experience in control engineering from a three year collaboration in the automotive domain, we conclude that innovation management is only weakly aligned with feature and platform development. This situation makes it difficult to assure feasibility of innovations and to identify potentials for innovations. We present a novel approach to integrating innovation man- agement with requirements and technology management by using behavioral system models. We investigate requirements-based and technology-based innovation. To the best of our knowledge, this is the first approach to using such models for managing feature and platform innovations in an automotive company. We discuss an example to illustrate how our approach can be applied.

Recently reported progress regarding thin film deposition under atmospheric pressure conditions led to increased interests for its application in optics, semiconductor production, automotive, or medical industry. Therefore, extensively research has been performed in the development of atmospheric pressure plasma sources for thin film deposition. Miniaturized non-thermal atmospheric pressure plasma jets represent a suitable tool for local surface coating and thus for the preparation of chemical micro-patterns. Consequently, investigations are of interest concerning the feasibility of plasma jets in surface engineering for customer-specific requirements. So far, two atmospheric pressure plasma jets with different geometries have been developed, which can be used for this purposes 1-2. In these set-ups, the supply of the precursor can be realized in different ways: I) the mixture of carrier gas and precursor is introduced into the main flow downstream the active discharge or II) by using a cap which was build to control and tailor the gas curtain which can diffuse into the effluent of the jet2-3. In the present paper, results are given of an experimental study on plasma enhanced chemical vapor deposition under atmospheric pressure conditions. Emphasis is given on depositing films which exhibit either hydrophilic (e.g. nitrogen-rich coatings) or hydrophobic surface properties (e.g. Teflon-like coatings). The chemical structure of these films, measured by X-ray photo electron spectroscopy, as well as their wettability will be shown and discussed. Deposition rates have been determined by weighing. Hence, by controlling the deposition conditions film growth rates of 6-43 nm s-1 have been obtained for fluorine-rich films, for example.