Search Results for author:
Found 17 papers, 7 papers with code
Legal Requirements Analysis
Specifically, we describe possible alternatives for creating machine-analyzable representations from regulations, survey the existing automated means for enabling compliance verification against regulations, and further reflect on the current challenges of legal requirements analysis.
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection
The experimental results show that supervised traditional and deep ML techniques fare similarly in terms of their detection accuracy and prediction time.
FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair
Our experimental results using code models and few-shot learning show that we can correctly predict most of the fix categories.
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
It reduces the cost of labeling by prioritizing the selection of test inputs with high fault revealing power from large unlabeled datasets.
Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches
In our previous work, we proposed a white-box approach (HUDD) and a black-box approach (SAFE) to automatically characterize DNN failures.
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems
However, the environmental variables (e. g., lighting conditions) that might change during the systems' operation in the real world, causing the DES to violate requirements (safety, functional), are often kept constant during the execution of an online test scenario due to the two major challenges: (1) the space of all possible scenarios to explore would become even larger if they changed and (2) there are typically many requirements to test simultaneously.
HUDD: A tool to debug DNNs for safety analysis
We present HUDD, a tool that supports safety analysis practices for systems enabled by Deep Neural Networks (DNNs) by automatically identifying the root causes for DNN errors and retraining the DNN.
A Search-Based Testing Approach for Deep Reinforcement Learning Agents
However, such attacks often lead to unrealistic states of the environment.
Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems
When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with failures (i. e., erroneous outputs) observed during testing.
Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
Experimental results show the superior ability of SAFE in identifying different root causes of DNN errors based on case studies in the automotive domain.
Black-Box Testing of Deep Neural Networks Through Test Case Diversity
In this paper, we investigate black-box input diversity metrics as an alternative to white-box coverage criteria.
Can Offline Testing of Deep Neural Networks Replace Their Online Testing?
Further, we cannot exploit offline testing results to reduce the cost of online testing in practice since we are not able to identify specific situations where offline testing could be as accurate as online testing in identifying safety requirement violations.
Mutation Analysis for Cyber-Physical Systems: Scalable Solutions and Results in the Space Domain
On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures.
Software Engineering
On Systematically Building a Controlled Natural Language for Functional Requirements
[Results] Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is general and applicable to information systems beyond the financial domain, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study.
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
To address these problems in the context of DNNs analyzing images, we propose HUDD, an approach that automatically supports the identification of root causes for DNN errors.
An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts
Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML).
Software Engineering
Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study
Further, offline testing is more optimistic than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing.
