2 Search Results for "Mitra, Subrata"

Document
DOI: 10.4230/OASIcs.SLATE.2025.13
Mining GitHub Software Repositories to Look for Programming Language Cocktails

Authors: João Loureiro, Alvaro Costa Neto, Maria João Varanda Pereira, and Pedro Rangel Henriques

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract
In light of specific development needs, it is common to concurrently apply different technologies to build complex applications. Given that lowering risks, costs, and other negative factors, while improving their positive counterparts is paramount to a better development environment, it becomes relevant to find out what technologies work best for each intended purpose in a project. In order to reach these findings, it is necessary to analyse and study the technologies applied in these projects and how they interconnect and relate to each other. The theory behind Programming Cocktails (meaning the set of programming technologies - Ingredients - that are used to develop complex systems) can support these analysis. However, due to the sheer amount of data that is required to construct and analyse these Cocktails, it becomes unsustainable to manually obtain them. From the desire to accelerate this process comes the need for a tool that automates the data collection and its conversion into an appropriate format for analysis. As such, the project proposed in this paper revolves around the development of a web-scraping application that can generate Cocktail Identity Cards (CIC) from source code repositories hosted on GitHub. Said CICs contain the Ingredients (programming languages, libraries and frameworks) used in the corresponding GitHub repository and follow the ontology previously established in a larger research project to model each Programming Cocktail. This paper presents a survey of current Source Version Control Systems (SVCSs) and web-scrapping technologies, an overview of Programming Cocktails and its current foundations, and the design of a tool that can automate the gathering of CICs from GitHub repositories.
Cite as

João Loureiro, Alvaro Costa Neto, Maria João Varanda Pereira, and Pedro Rangel Henriques. Mining GitHub Software Repositories to Look for Programming Language Cocktails. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 13:1-13:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{loureiro_et_al:OASIcs.SLATE.2025.13,
  author =	{Loureiro, Jo\~{a}o and Costa Neto, Alvaro and Pereira, Maria Jo\~{a}o Varanda and Henriques, Pedro Rangel},
  title =	{{Mining GitHub Software Repositories to Look for Programming Language Cocktails}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{13:1--13:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.13},
  URN =		{urn:nbn:de:0030-drops-236933},
  doi =		{10.4230/OASIcs.SLATE.2025.13},
  annote =	{Keywords: Software Repository Mining, Source Version Control, GitHub Scraping, Programming Cocktails}
}
Document
DOI: 10.4230/LIPIcs.ESA.2022.91
Correlated Stochastic Knapsack with a Submodular Objective

Authors: Sheng Yang, Samir Khuller, Sunav Choudhary, Subrata Mitra, and Kanak Mahadik

Published in: LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)

Abstract
We study the correlated stochastic knapsack problem of a submodular target function, with optional additional constraints. We utilize the multilinear extension of submodular function, and bundle it with an adaptation of the relaxed linear constraints from Ma [Mathematics of Operations Research, Volume 43(3), 2018] on correlated stochastic knapsack problem. The relaxation is then solved by the stochastic continuous greedy algorithm, and rounded by a novel method to fit the contention resolution scheme (Feldman et al. [FOCS 2011]). We obtain a pseudo-polynomial time (1 - 1/√e)/2 ≃ 0.1967 approximation algorithm with or without those additional constraints, eliminating the need of a key assumption and improving on the (1 - 1/∜e)/2 ≃ 0.1106 approximation by Fukunaga et al. [AAAI 2019].
Cite as

Sheng Yang, Samir Khuller, Sunav Choudhary, Subrata Mitra, and Kanak Mahadik. Correlated Stochastic Knapsack with a Submodular Objective. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 91:1-91:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{yang_et_al:LIPIcs.ESA.2022.91,
  author =	{Yang, Sheng and Khuller, Samir and Choudhary, Sunav and Mitra, Subrata and Mahadik, Kanak},
  title =	{{Correlated Stochastic Knapsack with a Submodular Objective}},
  booktitle =	{30th Annual European Symposium on Algorithms (ESA 2022)},
  pages =	{91:1--91:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-247-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{244},
  editor =	{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.91},
  URN =		{urn:nbn:de:0030-drops-170296},
  doi =		{10.4230/LIPIcs.ESA.2022.91},
  annote =	{Keywords: Stochastic Knapsack, Submodular Optimization, Stochastic Optimization}
}
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