arXiv subjects

Giulio Antoniol

Publications and source records attributed to Giulio Antoniol.

3 recordsLinked to original sources

arXiv2025

One Documentation Does Not Fit All: Case Study of TensorFlow Documentation

Software documentation guides the proper use of tools or services. With the rapid growth of machine learning libraries, individuals from various fields are incorporating machine learning into their workflows through programming. However, many of these users lack software engineering experience, affecting the usability of the documentation. Traditionally, software developers have created documentation primarily for their peers, making it challenging for others to interpret and effectively use these resources. Moreover, no study has specifically focused on machine learning software documentation or analyzing the backgrounds of developers who rely on such documentation, highlighting a critical gap in understanding how to make these resources more accessible. This study examined customization trends in TensorFlow tutorials and compared these artifacts to analyze content and design differences. We also analyzed Stack Overflow questions related to TensorFlow documentation to understand the types of questions and the backgrounds of the developers asking them. Further, we developed two taxonomies based on the nature and triggers of the questions for machine learning software. Our findings showed no significant differences in the content or the nature of the questions across different tutorials. Our results show that 24.9% of the questions concern errors and exceptions, while 64.3% relate to inadequate and non-generalizable examples in the documentation. Despite efforts to create customized documentation, our analysis indicates that current TensorFlow documentation does not effectively support its target users.

cs.SE↗

arXiv2021

HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks

Deep Neural Networks (DNN) applications are increasingly becoming a part of our everyday life, from medical applications to autonomous cars. Traditional validation of DNN relies on accuracy measures, however, the existence of adversarial examples has highlighted the limitations of these accuracy measures, raising concerns especially when DNN are integrated into safety-critical systems. In this paper, we present HOMRS, an approach to boost metamorphic testing by automatically building a small optimized set of high order metamorphic relations from an initial set of elementary metamorphic relations. HOMRS' backbone is a multi-objective search; it exploits ideas drawn from traditional systems testing such as code coverage, test case, path diversity as well as input validation. We applied HOMRS to MNIST/LeNet and SVHN/VGG and we report evidence that it builds a small but effective set of high-order transformations that generalize well to the input data distribution. Moreover, comparing to similar generation technique such as DeepXplore, we show that our distribution-based approach is more effective, generating valid transformations from an uncertainty quantification point of view, while requiring less computation time by leveraging the generalization ability of the approach.

cs.LG↗

arXiv2021

How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review

Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aeronautic has proven to be very challenging, since the shift in paradigm that ML brings completely changes traditional certification approaches. Objective: This paper aims to elucidate challenges related to the certification of ML-based safety-critical systems, as well as the solutions that are proposed in the literature to tackle them, answering the question 'How to Certify Machine Learning Based Safety-critical Systems?'. Method: We conduct a Systematic Literature Review (SLR) of research papers published between 2015 to 2020, covering topics related to the certification of ML systems. In total, we identified 217 papers covering topics considered to be the main pillars of ML certification: Robustness, Uncertainty, Explainability, Verification, Safe Reinforcement Learning, and Direct Certification. We analyzed the main trends and problems of each sub-field and provided summaries of the papers extracted. Results: The SLR results highlighted the enthusiasm of the community for this subject, as well as the lack of diversity in terms of datasets and type of models. It also emphasized the need to further develop connections between academia and industries to deepen the domain study. Finally, it also illustrated the necessity to build connections between the above mention main pillars that are for now mainly studied separately. Conclusion: We highlighted current efforts deployed to enable the certification of ML based software systems, and discuss some future research directions.

cs.LG↗