Search Results for author:
Found 12 papers, 3 papers with code
Domain Generalization through Meta-Learning: A Survey
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications.
Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing
On average, DroidAgent achieved 61% activity coverage, compared to 51% for current state-of-the-art GUI testing techniques.
Test2Vec: An Execution Trace Embedding for Test Case Prioritization
In this paper, we hypothesize that execution traces of the test cases can be a good alternative to abstract their behavior for automated testing tasks.
A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning
However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information.
Automated Support for Unit Test Generation: A Tutorial Book Chapter
To illustrate how AI can support unit testing, this chapter introduces the concept of search-based unit test generation.
Towards Human-Like Automated Test Generation: Perspectives from Cognition and Problem Solving
The framework helps map test design steps and criteria used in human test activities and thus to better understand how effective human testers perform their tasks.
Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state of the art while highlighting the boundaries of its validity.
Software Engineering
Empirical Standards for Software Engineering Research
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e. g. a questionnaire survey).
Software Engineering General Literature
Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving
The development process in use consists of multiple iterations of data collection, labelling, training, and evaluation.
SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation
The testing of Deep Neural Networks (DNNs) has become increasingly important as DNNs are widely adopted by safety critical systems.
Guiding Deep Learning System Testing using Surprise Adequacy
Recently, a number of coverage criteria based on neuron activation values have been proposed.
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
