3 Search Results for "Pattabiraman, Karthik"

Document
Academic Track
DOI: 10.4230/OASIcs.SAIA.2024.1
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)

Authors: Afef Awadid and Boris Robert

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

Abstract
Due to their uncertainty and vulnerability to adversarial attacks, machine learning (ML) models can lead to severe consequences, including the loss of human life, when embedded in safety-critical systems such as autonomous vehicles. Therefore, it is crucial to assess the empirical robustness of such models before integrating them into these systems. ML model robustness refers to the ability of an ML model to be insensitive to input perturbations and maintain its performance. Against this background, the Confiance.ai research program proposes a methodological framework for assessing the empirical robustness of ML models. The framework encompasses methodological processes (guidelines) captured in Capella models, along with a set of supporting tools. This paper aims to provide an overview of this framework and its application in an industrial setting.
Cite as

Afef Awadid and Boris Robert. On Assessing ML Model Robustness: A Methodological Framework (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 1:1-1:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{awadid_et_al:OASIcs.SAIA.2024.1,
  author =	{Awadid, Afef and Robert, Boris},
  title =	{{On Assessing ML Model Robustness: A Methodological Framework}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{1:1--1:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.1},
  URN =		{urn:nbn:de:0030-drops-227410},
  doi =		{10.4230/OASIcs.SAIA.2024.1},
  annote =	{Keywords: ML model robustness, assessment, framework, methodological processes, tools}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2018.18
ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications

Authors: Julien Gascon-Samson, Kumseok Jung, Shivanshu Goyal, Armin Rezaiean-Asel, and Karthik Pattabiraman

Published in: LIPIcs, Volume 109, 32nd European Conference on Object-Oriented Programming (ECOOP 2018)

Abstract
The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. As IoT devices become increasingly powerful, they can run more and more complex applications written in higher-level languages, such as JavaScript. However, by their nature, IoT devices are subject to resource constraints, which require applications to be dynamically migrated between devices (and the cloud). Further, IoT applications are also becoming more stateful, and hence we need to save their state during migration transparently to the programmer. In this paper, we present ThingsMigrate, a middleware providing VM-independent migration of stateful JavaScript applications across IoT devices. ThingsMigrate captures and reconstructs the internal JavaScript program state by instrumenting application code before run time, without modifying the underlying Virtual Machine (VM), thus providing platform and VM-independence. We evaluated ThingsMigrate against standard benchmarks, and over two IoT platforms and a cloud-like environment. We show that it can successfully migrate even highly CPU-intensive applications, with acceptable overheads (about 30%), and supports multiple migrations.
Cite as

Julien Gascon-Samson, Kumseok Jung, Shivanshu Goyal, Armin Rezaiean-Asel, and Karthik Pattabiraman. ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications. In 32nd European Conference on Object-Oriented Programming (ECOOP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 109, pp. 18:1-18:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{gasconsamson_et_al:LIPIcs.ECOOP.2018.18,
  author =	{Gascon-Samson, Julien and Jung, Kumseok and Goyal, Shivanshu and Rezaiean-Asel, Armin and Pattabiraman, Karthik},
  title =	{{ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications}},
  booktitle =	{32nd European Conference on Object-Oriented Programming (ECOOP 2018)},
  pages =	{18:1--18:33},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-079-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{109},
  editor =	{Millstein, Todd},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2018.18},
  URN =		{urn:nbn:de:0030-drops-92236},
  doi =		{10.4230/LIPIcs.ECOOP.2018.18},
  annote =	{Keywords: JavaScript, Code Migration, Closures, IoT, Node.js}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2015.321
Hybrid DOM-Sensitive Change Impact Analysis for JavaScript

Authors: Saba Alimadadi, Ali Mesbah, and Karthik Pattabiraman

Published in: LIPIcs, Volume 37, 29th European Conference on Object-Oriented Programming (ECOOP 2015)

Abstract
JavaScript has grown to be among the most popular programming languages. However, performing change impact analysis on JavaScript applications is challenging due to features such as the seamless interplay with the DOM, event-driven and dynamic function calls, and asynchronous client/server communication. We first perform an empirical study of change propagation, the results of which show that the DOM-related and dynamic features of JavaScript need to be taken into consideration in the analysis since they affect change impact propagation. We propose a DOM-sensitive hybrid change impact analysis technique for Javascript through a combination of static and dynamic analysis. The proposed approach incorporates a novel ranking algorithm for indicating the importance of each entity in the impact set. Our approach is implemented in a tool called Tochal. The results of our evaluation reveal that Tochal provides a more complete analysis compared to static or dynamic methods. Moreover, through an industrial controlled experiment, we find that Tochal helps developers by improving their task completion duration by 78% and accuracy by 223%.
Cite as

Saba Alimadadi, Ali Mesbah, and Karthik Pattabiraman. Hybrid DOM-Sensitive Change Impact Analysis for JavaScript. In 29th European Conference on Object-Oriented Programming (ECOOP 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 37, pp. 321-345, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{alimadadi_et_al:LIPIcs.ECOOP.2015.321,
  author =	{Alimadadi, Saba and Mesbah, Ali and Pattabiraman, Karthik},
  title =	{{Hybrid DOM-Sensitive Change Impact Analysis for JavaScript}},
  booktitle =	{29th European Conference on Object-Oriented Programming (ECOOP 2015)},
  pages =	{321--345},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-86-6},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{37},
  editor =	{Boyland, John Tang},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2015.321},
  URN =		{urn:nbn:de:0030-drops-52280},
  doi =		{10.4230/LIPIcs.ECOOP.2015.321},
  annote =	{Keywords: Change impact analysis, JavaScript, hybrid analysis}
}
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