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Documents authored by Gerostathopoulos, Ilias

Document
Technical Track Paper
DOI: 10.4230/LIPIcs.ESEM.2026.27
A Multivocal Review of AutoML Practices, Challenges, Opportunities and Open Issues

Authors: Keerthiga Rajenthiram, Faezeh Amou Najafabadi, Ilias Gerostathopoulos, and Patricia Lago

Published in: LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)

Abstract
Background. Automated Machine Learning (AutoML) is increasingly embedded in software and data engineering workflows, automating the design and optimization of machine learning (ML) pipelines. AutoML has been extensively synthesized through secondary studies that map tools, algorithms, and evaluation practices. However, these reviews predominantly reflect a research-centric perspective and provide limited evidence on how AutoML is actually used, adapted, and experienced in practice. Traditional surveys and interviews capture only a narrow slice of the practitioner community, leaving open how well academic claims about AutoML align with day-to-day engineering realities. Aims. This study aims to (i) assess to what extent AutoML practices and challenges reported in existing secondary studies reflect practitioners' experiences, and (ii) identify mismatches between research and practice by empirically comparing academic claims with practitioner experiences shared on YouTube. Method. Following established guidelines for multivocal literature reviews in software engineering, we conduct a multivocal evidence synthesis combining 16 peer-reviewed secondary studies (systematic and multivocal literature reviews) with 30 practitioner-created YouTube videos in which engineers, data scientists, and ML practitioners describe their AutoML workflows, workarounds, and pain points. Results. Our analysis reveals a marked imbalance: academic reviews emphasize algorithm selection, search strategies, and benchmarking, whereas practitioners foreground data quality and cleaning, resource and cost constraints, tool robustness, scalability, and integration into production pipelines. We also observe areas of alignment, for example around hyperparameter optimization and the need for reproducible workflows. Conclusions. Treating YouTube videos as curated gray literature under established multivocal review guidelines, our study shows that they provide complementary, practice-grounded evidence about AutoML adoption. The identified gaps and alignments suggest concrete directions for empirical AutoML research that more directly address operational and organizational constraints in real-world software engineering settings.
Cite as

Keerthiga Rajenthiram, Faezeh Amou Najafabadi, Ilias Gerostathopoulos, and Patricia Lago. A Multivocal Review of AutoML Practices, Challenges, Opportunities and Open Issues. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 27:1-27:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{rajenthiram_et_al:LIPIcs.ESEM.2026.27,
  author =	{Rajenthiram, Keerthiga and Najafabadi, Faezeh Amou and Gerostathopoulos, Ilias and Lago, Patricia},
  title =	{{A Multivocal Review of AutoML Practices, Challenges, Opportunities and Open Issues}},
  booktitle =	{20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)},
  pages =	{27:1--27:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-450-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{394},
  editor =	{Feldt, Robert and Paasivaara, Maria and Mendez, Daniel and Wagner, Stefan and Bar\'{o}n, Marvin Mu\~{n}oz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.27},
  URN =		{urn:nbn:de:0030-drops-279953},
  doi =		{10.4230/LIPIcs.ESEM.2026.27},
  annote =	{Keywords: Automated Machine Learning (AutoML), Multivocal Literature Review, YouTube Videos, Gray Literature, Empirical Study, Research–Practice Gap}
}
Document
DOI: 10.4230/DARTS.3.1.5
Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact)

Authors: Sanny Schmid, Ilias Gerostathopoulos, Christian Prehofer, and Tomas Bures

Published in: DARTS, Volume 3, Issue 1, Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017)

Abstract
This artifact supports our research in self-adaptation in large-scale software-intensive distributed systems. The main problem in making such systems self-adaptive is that their adaptation needs to consider the current situation in the whole system. However, developing a complete and accurate model of such systems at design time is very challenging. We are instead investigating a novel approach where the system model consists only of the essential input and output parameters and Big Data analytics is used to guide self-adaptation based on a continuous stream of operational data. In this artifact, we provide a concrete model problem that can be used as a case study for evaluating different self-adaptation techniques pertinent to complex large-scale distributed systems. We also provide an extensible tool-based framework for endorsing an arbitrary system with self-adaptation based on analysis of operational data coming from the system. The model problem (CrowdNav) and the framework (RTX) have been packaged together in this artifact, but can also work independently.
Cite as

Sanny Schmid, Ilias Gerostathopoulos, Christian Prehofer, and Tomas Bures. Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 5:1-5:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Article{schmid_et_al:DARTS.3.1.5,
  author =	{Schmid, Sanny and Gerostathopoulos, Ilias and Prehofer, Christian and Bures, Tomas},
  title =	{{Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact)}},
  pages =	{5:1--5:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2017},
  volume =	{3},
  number =	{1},
  editor =	{Schmid, Sanny and Gerostathopoulos, Ilias and Prehofer, Christian and Bures, Tomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.5},
  URN =		{urn:nbn:de:0030-drops-71435},
  doi =		{10.4230/DARTS.3.1.5},
  annote =	{Keywords: self-adaptation; Big Data analytics; model problem, tool, framework}
}
Document
DOI: 10.4230/DARTS.3.1.6
Intelligent Ensembles – a Declarative Group Description Language and Java Framework (Artifact)

Authors: Filip Krijt, Zbynek Jiracek, Tomas Bures, Petr Hnetynka, and Ilias Gerostathopoulos

Published in: DARTS, Volume 3, Issue 1, Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017)

Abstract
Smart cyber-physical systems (sCPS) is a growing research field focused on scenarios such as smart cities or smart mobility, where autonomous components are deployed in a physical environment, and are expected to cooperate with one another, as well as with humans. As these systems typically operate in a highly uncertain and dynamically changing environment, being able to cooperate and adapt in groups to cope with various (possibly unanticipated) situations becomes a crucial and challenging task. In this artifact, we respond to this challenge by presenting the Intelligent Ensembles framework, consisting of a high-level declarative language for describing dynamic cooperation groups, and a Java runtime library for automatically forming groups that best satisfy the given specification. The framework provides dynamic architecture adaptation (i.e., forming groups of components and exchanging data between them) based on the state of components and situation in their environment. Further, the framework can be used as a first step of a group-wise adaptation (i.e., identifying components that are to negotiate and coordinate in an adaptation). The framework is built on top of the Z3 SMT solver and the Eclipse Modelling Framework.
Cite as

Filip Krijt, Zbynek Jiracek, Tomas Bures, Petr Hnetynka, and Ilias Gerostathopoulos. Intelligent Ensembles – a Declarative Group Description Language and Java Framework (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 6:1-6:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Article{krijt_et_al:DARTS.3.1.6,
  author =	{Krijt, Filip and Jiracek, Zbynek and Bures, Tomas and Hnetynka, Petr and Gerostathopoulos, Ilias},
  title =	{{Intelligent Ensembles – a Declarative Group Description Language and Java Framework (Artifact)}},
  pages =	{6:1--6:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2017},
  volume =	{3},
  number =	{1},
  editor =	{Krijt, Filip and Jiracek, Zbynek and Bures, Tomas and Hnetynka, Petr and Gerostathopoulos, Ilias},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.6},
  URN =		{urn:nbn:de:0030-drops-71444},
  doi =		{10.4230/DARTS.3.1.6},
  annote =	{Keywords: Smart Cyber-physical Systems; Adaptive Architecture; Ensemble-based Component System; Group-wise Adaptation; Autonomic Systems}
}

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