Alcides Fonseca

Resource-Aware Programming (RAP) is an exploratory research project, funded by FCT.

The RAP project aims to give developers more feedback of the resources (time, memory energy) their programs cost, during the development process. The projects relies on considering cost as a probabilistic function, and relying on Genetic Programming to infer the cost of basic blocks, based on execution traces.

RAP is funded by FCT:

• EXPL/CCI-COM/1306/2021 – Main project (2022-2023)
• 2022.15800.CPCA.A1 – Genetic Programming for Genomics Interpretation (2023)

Team

• Alcides Fonseca (PI)

• Francisco Martins

• Antónia Lopes

• Vasco Vasconcelos

• Andreia Mordido

• José Campos

• Wellington Oliveira

• Guilherme Espada (PhD Student)

• Paulo Canelas Santos (PhD Student)

• Catarina Gamboa (PhD Student)

• Leon ingelse (MSc Student)

Software

• GeneticEngine An hybrid of Grammar-Guided and Strongly Typed Genetic Programming in Python.

• Aeon A programming language with liquid types, focused on synthesis

• LiquidJava A library+typechecker + VSCode plugin for Java that adds Liquid Types and TypeState.

Publications

Journal

• Computational prediction of human deep intronic variation (TBP)

Conference Proceedings

• Usability-Oriented Design of Liquid Types for Java @ ICSE 2023

• Data Types as a More Ergonomic Frontend for Grammar-Guided Genetic Programming @ GPCE 2022

Workshop Proceedings

• Type Systems in Resource-Aware Programming: Opportunities and Challenges @ RAW 2022

• An Experience Report on Challenges in Learning the Robot Operating System @ ROSE 2022

• Figra: evaluating a larger search space for cardumen in automatic program repair @ APR 2022

Short Papers

• Benchmarking Representations of Individuals in Grammar-Guided Genetic Programming @ Evo* 2022

Demos

• Genetic Engine: Genetic Programming for the Common Programmer @ <Programming> 2022

• Dive into LiquidJava ⁠— Extending Java with Liquid Types @ <Programming> 2022

Posters

• Genetic Engine: Grammar-Guided Genetic Programming without the Grammar @ <Programming> 2022

Master Thesis

• Optimization of feature learning through grammar-guided genetic programming by Leon Ingelse