Martina Saletta

Orcid: 0000-0001-7250-8987

According to our database1, Martina Saletta authored at least 14 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Exploring the Prompt Space of Large Language Models through Evolutionary Sampling.
Proceedings of the Genetic and Evolutionary Computation Conference, 2024

2023
Exploring Neural Dynamics in Source Code Processing Domain.
Inf., April, 2023

Local Search, Semantics, and Genetic Programming: a Global Analysis.
CoRR, 2023

Evolutionary Approaches for Adversarial Attacks on Neural Source Code Classifiers.
Algorithms, 2023

Naturalness in Source Code Summarization. How Significant is it?
Proceedings of the 31st IEEE/ACM International Conference on Program Comprehension, 2023

2022
Heuristic search of (semi-)bent functions based on cellular automata.
Nat. Comput., 2022

Do Neural Transformers Learn Human-Defined Concepts? An Extensive Study in Source Code Processing Domain.
Algorithms, 2022

Towards the evolutionary assessment of neural transformers trained on source code.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

A Grammar-based Evolutionary Approach for Assessing Deep Neural Source Code Classifiers.
Proceedings of the IEEE Congress on Evolutionary Computation, 2022

2021
Mining Program Properties From Neural Networks Trained on Source Code Embeddings.
CoRR, 2021

Deceiving neural source code classifiers: finding adversarial examples with grammatical evolution.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020
A Neural Embedding for Source Code: Security Analysis and CWE Lists.
Proceedings of the IEEE Intl Conf on Dependable, 2020

Exploring Semi-bent Boolean Functions Arising from Cellular Automata.
Proceedings of the Cellular Automata, 2020

2019
Extending Local Search in Geometric Semantic Genetic Programming.
Proceedings of the Progress in Artificial Intelligence, 2019


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