Modeling Quality and Machine Learning Pipelines through Extended Feature Models

The recently increased complexity of Machine Learning (ML) methods, led to the necessity to lighten both the research and industry development processes. ML pipelines have become an essential tool for experts of many domains, data scientists and researchers, allowing them to easily put together several ML models to cover the full analytic process starting from raw datasets. Over the years, several solutions have been proposed to automate the building of ML pipelines, most of them focused on semantic aspects and characteristics of the input dataset. However, an approach taking into account the new quality concerns needed by ML systems (like fairness, interpretability, privacy, etc.) is still missing. In this paper, we first identify, from the literature, key quality attributes of ML systems. Further, we propose a new engineering approach for quality ML pipeline by properly extending the Feature Models meta-model. The presented approach allows to model ML pipelines, their quality requirements (on the whole pipeline and on single phases), and quality characteristics of algorithms used to implement each pipeline phase. Finally, we demonstrate the expressiveness of our model considering the classification problem

Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development

The problem of predicting the training time of machine learning (ML) models has become extremely relevant in the scientific community. Being able to predict a priori the training time of an ML model would enable the automatic selection of the best model both in terms of energy efficiency and in terms of performance in the context of, for instance, MLOps architectures. In this paper, we present the work we are conducting towards this direction. In particular, we present an extensive empirical study of the Full Parameter Time Complexity (FPTC) approach by Zheng et al., which is, to the best of our knowledge, the only approach formalizing the training time of ML models as a function of both dataset's and model's parameters. We study the formulations proposed for the Logistic Regression and Random Forest classifiers, and we highlight the main strengths and weaknesses of the approach. Finally, we observe how, from the conducted study, the prediction of training time is strictly related to the context (i.e., the involved dataset) and how the FPTC approach is not generalizable

Data-Driven Analysis of Gender Fairness in the Software Engineering Academic Landscape

Gender bias in education gained considerable relevance in the literature over the years. However, while the problem of gender bias in education has been widely addressed from a student perspective, it is still not fully analysed from an academic point of view. In this work, we study the problem of gender bias in academic promotions (i.e., from Researcher to Associated Professor and from Associated to Full Professor) in the informatics (INF) and software engineering (SE) Italian communities. In particular, we first conduct a literature review to assess how the problem of gender bias in academia has been addressed so far. Next, we describe a process to collect and preprocess the INF and SE data needed to analyse gender bias in Italian academic promotions. Subsequently, we apply a formal bias metric to these data to assess the amount of bias and look at its variation over time. From the conducted analysis, we observe how the SE community presents a higher bias in promotions to Associate Professors and a smaller bias in promotions to Full Professors compared to the overall INF community

Physical and Functional Interaction of CARMA1 and CARMA3 with Iκ Kinase γ-NFκB Essential Modulator

CARMA proteins are scaffold molecules that contain a caspase recruitment domain and a membrane-associated guanylate kinase-like domain. CARMA1 plays a critical role in mediating activation of the NFkappaB transcription factor following antigen receptor stimulation of both B and T lymphocytes. However, the biochemical mechanism by which CARMA1 regulates activation of NFkappaB remains to be determined. Here we have shown that CARMA1 and CARMA3 physically associate with Ikappa kinase gamma/NFkappaB essential modulator (IkappaKgamma-NEMO) in lymphoid and non-lymphoid cells. CARMA1 participates to an inducible large molecular complex that contains IkappaKgamma/NEMO, Bcl10, and IkappaKalpha/beta kinases. Expression of the NEMO-binding region of CARMA3 exerts a dominant negative effect on Bcl10-mediated activation of NFkappaB. Thus, our results provide direct evidence for physical and functional interaction between CARMA and the IkappaK complex and offer a biochemical framework to understand the molecular activities controlled by CARMA-1, -2, and -3 and Bcl10