Search Results for author:
Found 12 papers, 4 papers with code
On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions
With modern infotainment systems, drivers are increasingly tempted to engage in secondary tasks while driving.
Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews
In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments.
CATE: CAusality Tree Extractor from Natural Language Requirements
Causal relations (If A, then B) are prevalent in requirements artifacts.
Fine-Grained Causality Extraction From Natural Language Requirements Using 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.
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).
Software Engineering
Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach
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.
Software Engineering
Topic Modeling on User Stories using Word Mover's Distance
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.
Towards Causality Extraction from Requirements
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.
Destination Prediction Based on Partial Trajectory Data
Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data.
Automated Generation of Test Models from Semi-Structured Requirements
[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.
Towards Self-Explainable Cyber-Physical Systems
With the increasing complexity of CPSs, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders.
Requirements Engineering for Machine Learning: Perspectives from Data Scientists
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.
