arXiv subjects

Andreas Vogelsang

Publications and source records attributed to Andreas Vogelsang.

At least 55 records · Page 3Linked to original sources

arXiv2021

Visualizing Event Sequence Data for User Behavior Evaluation of In-Vehicle Information Systems

With modern IVIS becoming more capable and complex than ever, their evaluation becomes increasingly difficult. The analysis of large amounts of user behavior data can help to cope with this complexity and can support UX experts in designing IVIS that serve customer needs and are safe to operate while driving. We, therefore, propose a Multi-level User Behavior Visualization Framework providing effective visualizations of user behavior data that is collected via telematics from production vehicles. Our approach visualizes user behavior data on three different levels: (1) The Task Level View aggregates event sequence data generated through touchscreen interactions to visualize user flows. (2) The Flow Level View allows comparing the individual flows based on a chosen metric. (3) The Sequence Level View provides detailed insights into touch interactions, glance, and driving behavior. Our case study proves that UX experts consider our approach a useful addition to their design process.

cs.HC↗

arXiv2021

Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews

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.

cs.CL↗

arXiv2021

Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks

[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.

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arXiv2021

CATE: CAusality Tree Extractor from Natural Language Requirements

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/

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arXiv2021

Integrated and Iterative Requirements Analysis and Test Specification: A Case Study at Kostal

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.

cs.SE↗

arXiv2021

CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts

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.

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arXiv2021

Automatic Detection of Causality in Requirement Artifacts: the CiRA 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.

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arXiv2020

How do Practitioners Perceive the Relevance of Requirements Engineering Research?

The relevance of Requirements Engineering (RE) research to practitioners is vital for a long-term dissemination of research results to everyday practice. Some authors have speculated about a mismatch between research and practice in the RE discipline. However, there is not much evidence to support or refute this perception. This paper presents the results of a study aimed at gathering evidence from practitioners about their perception of the relevance of RE research and at understanding the factors that influence that perception. We conducted a questionnaire-based survey of industry practitioners with expertise in RE. The participants rated the perceived relevance of 435 scientific papers presented at five top RE-related conferences. The 153 participants provided a total of 2,164 ratings. The practitioners rated RE research as essential or worthwhile in a majority of cases. However, the percentage of non-positive ratings is still higher than we would like. Among the factors that affect the perception of relevance are the research's links to industry, the research method used, and respondents' roles. The reasons for positive perceptions were primarily related to the relevance of the problem and the soundness of the solution, while the causes for negative perceptions were more varied. The respondents also provided suggestions for future research, including topics researchers have studied for decades, like elicitation or requirement quality criteria.

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arXiv2020

What Makes Agile Test Artifacts Useful? An Activity-Based Quality Model from a Practitioners' Perspective

Background: The artifacts used in Agile software testing and the reasons why these artifacts are used are fairly well-understood. However, empirical research on how Agile test artifacts are eventually designed in practice and which quality factors make them useful for software testing remains sparse. Aims: Our objective is two-fold. First, we identify current challenges in using test artifacts to understand why certain quality factors are considered good or bad. Second, we build an Activity-Based Artifact Quality Model that describes what Agile test artifacts should look like. Method: We conduct an industrial survey with 18 practitioners from 12 companies operating in seven different domains. Results: Our analysis reveals nine challenges and 16 factors describing the quality of six test artifacts from the perspective of Agile testers. Interestingly, we observed mostly challenges regarding language and traceability, which are well-known to occur in non-Agile projects. Conclusions: Although Agile software testing is becoming the norm, we still have little confidence about general do's and don'ts going beyond conventional wisdom. This study is the first to distill a list of quality factors deemed important to what can be considered as useful test artifacts.

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arXiv2020

The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective

We are interested in the role of field user interaction data in the development of IVIS, the potentials practitioners see in analyzing this data, the concerns they share, and how this compares to companies with digital products. We conducted interviews with 14 UX professionals, 8 from automotive and 6 from digital companies, and analyzed the results by emergent thematic coding. Our key findings indicate that implicit feedback through field user interaction data is currently not evident in the automotive UX development process. Most decisions regarding the design of IVIS are made based on personal preferences and the intuitions of stakeholders. However, the interviewees also indicated that user interaction data has the potential to lower the influence of guesswork and assumptions in the UX design process and can help to make the UX development lifecycle more evidence-based and user-centered.

cs.HC↗

arXiv2020

Topic Modeling on User Stories using Word Mover's Distance

Requirements elicitation has recently been complemented with crowd-based techniques, which continuously involve large, heterogeneous groups of users who express their feedback through a variety of media. Crowd-based elicitation has great potential for engaging with (potential) users early on but also results in large sets of raw and unstructured feedback. Consolidating and analyzing this feedback is a key challenge for turning it into sensible user requirements. In this paper, we focus on topic modeling as a means to identify topics within a large set of crowd-generated user stories and compare three approaches: (1) a traditional approach based on Latent Dirichlet Allocation, (2) a combination of word embeddings and principal component analysis, and (3) a combination of word embeddings and Word Mover's Distance. We evaluate the approaches on a publicly available set of 2,966 user stories written and categorized by crowd workers. We found that a combination of word embeddings and Word Mover's Distance is most promising. Depending on the word embeddings we use in our approaches, we manage to cluster the user stories in two ways: one that is closer to the original categorization and another that allows new insights into the dataset, e.g. to find potentially new categories. Unfortunately, no measure exists to rate the quality of our results objectively. Still, our findings provide a basis for future work towards analyzing crowd-sourced user stories.

cs.CL↗

arXiv2020

Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks

Requirements Engineering (RE) is a means to reduce the risk of delivering a product that does not fulfill the stakeholders' needs. Therefore, a major challenge in RE is to decide how much RE is needed and what RE methods to apply. The quality of such decisions is strongly based on the RE expert's experience and expertise in carefully analyzing the context and current state of a project. Recent work, however, shows that lack of experience and qualification are common causes for problems in RE. We trained a series of Bayesian Networks on data from the NaPiRE survey to model relationships between RE problems, their causes, and effects in projects with different contextual characteristics. These models were used to conduct (1) a postmortem (diagnostic) analysis, deriving probable causes of suboptimal RE performance, and (2) to conduct a preventive analysis, predicting probable issues a young project might encounter. The method was subject to a rigorous cross-validation procedure for both use cases before assessing

cs.SE↗

arXiv2020

Towards Causality Extraction from Requirements

System behavior is often based on causal relations between certain events (e.g. If event1, then event2). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements. Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance. In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs. Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction. We encourage fellow researchers to contribute to our dataset and help us in finalizing the causality annotation process. Additionally, the dataset can also be annotated further to serve as a benchmark for other RE-relevant NLP tasks such as requirements classification.

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arXiv2020

Destination Prediction Based on Partial Trajectory Data

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.

cs.LG↗

arXiv2020

How do Quantifiers Affect the Quality of Requirements?

Context: Requirements quality can have a substantial impact on the effectiveness and efficiency of using requirements artifacts in a development process. Quantifiers such as "at least", "all", or "exactly" are common language constructs used to express requirements. Quantifiers can be formulated by affirmative phrases ("At least") or negative phrases ("Not less than"). Problem: It is long assumed that negation in quantification negatively affects the readability of requirements, however, empirical research on these topics remains sparse. Principal Idea: In a web-based experiment with 51 participants, we compare the impact of negations and quantifiers on readability in terms of reading effort, reading error rate and perceived reading difficulty of requirements. Results: For 5 out of 9 quantifiers, our participants performed better on the affirmative phrase compared to the negative phrase. Only for one quantifier, the negative phrase was more effective. Contribution: This research focuses on creating an empirical understanding of the effect of language in Requirements Engineering. It furthermore provides concrete advice on how to phrase requirements.

cs.SE↗

arXiv2020

Views on Quality Requirements in Academia and Practice: Commonalities, Differences, and Context-Dependent Grey Areas

Context: Quality requirements (QRs) are a topic of constant discussions both in industry and academia. Debates entwine around the definition of quality requirements, the way how to handle them, or their importance for project success. While many academic endeavors contribute to the body of knowledge about QRs, practitioners may have different views. In fact, we still lack a consistent body of knowledge on QRs since much of the discussion around this topic is still dominated by observations that are strongly context-dependent. This holds for both academic and practitioners' views. Our assumption is that, in consequence, those views may differ. Objective: We report on a study to better understand the extent to which available research statements on quality requirements, as found in exemplary peer-reviewed and frequently cited publications, are reflected in the perception of practitioners. Our goal is to analyze differences, commonalities, and context-dependent grey areas in the views of academics and practitioners to allow a discussion on potential misconceptions (on either sides) and opportunities for future research. Method: We conducted a survey with 109 practitioners to assess whether they agree with research statements about QRs reflected in the literature. Based on a statistical model, we evaluate the impact of a set of context factors to the perception of research statements. Results: Our results show that a majority of the statements is well respected by practitioners; however, not all of them. When examining the different groups and backgrounds of respondents, we noticed interesting deviations of perceptions within different groups that may lead to new research questions. Conclusions: Our results help identifying prevalent context-dependent differences about how academics and practitioners view QRs and pinpointing statements where further research might be useful.

cs.SE↗

arXiv2019

Automated Generation of Test Models from Semi-Structured Requirements

[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.

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arXiv2019

Requirements Engineering for Machine Learning: Perspectives from Data Scientists

Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.

cs.LG↗