Continuous Quality Improvement of AI-based Systems: the QualAI Project
Authors:
- Nicole NovielliNNNicole Novielli
- Rocco OlivetoRORocco Oliveto
- Fabio PalombaFPFabio Palomba
- Fabio CalefatoFCFabio Calefato
- Giuseppe ColavitoGCGiuseppe Colavito
- Vincenzo De MartinoVMVincenzo De Martino
- Antonio Della PortaAPAntonio Della Porta
- Giammaria GiordanoGGGiammaria Giordano
- Emanuela GuglielmiEGEmanuela Guglielmi
- Filippo LanubileFLFilippo Lanubile
- Luigi QuarantaLQLuigi Quaranta
- Gilberto RecupitoGRGilberto Recupito
Open Access
- 24 October 2024
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 603-607
Abstract
QualAI is a two-year project aimed at defining a set of recommenders to continuously monitor, assess, and improve the quality of AI-based systems, with a particular focus on machine learning (ML) applications. We will develop recommenders for the quality assurance of both data and ML models to enable practitioners to mitigate technical debt. Special attention will be paid to communication challenges that may arise in hybrid teams comprising data scientists and software developers. This paper presents the project outline, provides an executive summary of the research activities, outlines the expected project outcomes, and reports the results obtained to date.Keywords
This publication has 12 references indexed in Scilit:
- Black-Box Reconstruction Attacks on LLMs: A Preliminary Study in Code Summarization2024
- Fixing Dockerfile smells: an empirical studyEmpirical Software Engineering, 2024
- Leveraging GPT-like LLMs to Automate Issue Labeling2024
- ReFAIR: Toward a Context-Aware Recommender for Fairness Requirements Engineering2024
- Impact of data quality for automatic issue classification using pre-trained language modelsJournal of Systems and Software, 2023
- A first look at the integration of machine learning models in complex autonomous driving systems: a case study on Apollo2020
- A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy2020
- An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms2019
- CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries2019
- Knowledge-aware Autoencoders for Explainable Recommender Systems2018