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Found 13 papers, 6 papers with code
Semantic similarity prediction is better than other semantic similarity measures
Semantic similarity between natural language texts is typically measured either by looking at the overlap between subsequences (e. g., BLEU) or by using embeddings (e. g., BERTScore, S-BERT).
AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays
The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays, e. g., it is characterized by fewer discourse and epistemic markers, but more nominalizations and greater lexical diversity.
What are the Machine Learning best practices reported by practitioners on Stack Exchange?
In particular, Software Engineering (SE) is one of those disciplines in which ML has been used for multiple tasks, like software categorization, bugs prediction, and testing.
Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP
Objective: We want to understand if machine learning models provide explanations for the classification that are reasonable to us as humans and align with our assumptions of what the models should learn.
Differential testing for machine learning: an analysis for classification algorithms beyond deep learning
The execution of the feasible tests revealed that there is a large amount of deviations for the scores and classes.
Predicting Issue Types with seBERT
Pre-trained transformer models are the current state-of-the-art for natural language models processing.
On the validity of pre-trained transformers for natural language processing in the software engineering domain
Transformers are the current state-of-the-art of natural language processing in many domains and are using traction within software engineering research as well.
Expert Decision Support System for aeroacoustic source type identification using clustering
Second, clustering is performed based on these properties.
Automatic source localization and spectra generation from sparse beamforming maps
This paper presents two methods that enable the automated identification of aeroacoustic sources in sparse beamforming maps and the extraction of their corresponding spectra to overcome the manual definition of Regions Of Interest.
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits.
Software Engineering
Smoke Testing for Machine Learning: Simple Tests to Discover Severe Defects
Moreover, we found that these concepts can also be applied to hyperparameters, to further improve the quality of the smoke tests.
A Multi-Objective Anytime Rule Mining System to Ease Iterative Feedback from Domain Experts
In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for classification problems: (1) Domain experts' acceptance is of critical importance, and domain experts can provide valuable input, but it is hard to use this feedback.
An Industrial Case Study on Shrinking Code Review Changesets through Remark Prediction
To determine the importance of change parts, we extract data from software repositories and build prediction models for review remarks based on this data.
