Rima Hazra
Indian Institute of Technology Kharagpur
12 Papers
7 Citations
Rima Hazra is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Artifact (software development) & Hyperlink. The author has an hindex of 2, co-authored 8 publications. Previous affiliations of Rima Hazra include National Institute of Technology, Durgapur.
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Papers
The Rise and Rise of Interdisciplinary Research: Understanding the Interaction Dynamics of Three Major Fields - Physics, Mathematics and Computer Science.
Rima Hazra,Mayank Singh,Pawan Goyal,Bibhas Adhikari,Animesh Mukherjee +4 more
- 04 Nov 2019
TL;DR: This article investigates a collection of more than 1.2 million papers from three different scientific disciplines and shows how over a timescale the citation patterns from the core science fields to the applied and fast-growing field of Computer Science have drastically increased.
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Joint Autoregressive and Graph Models for Software and Developer Social Networks.
Rima Hazra,Hardik Aggarwal,Pawan Goyal,Animesh Mukherjee,Soumen Chakrabarti +4 more
- 28 Mar 2021
TL;DR: In this paper, the authors propose a method to integrate network-derived features and demonstrate that their method brings additional benefits, including the ability to identify packages that are most likely to be troubled by bugs in the immediate future and recommend developers to packages for the next development cycle.
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An efficient technique for author name disambiguation
Rima Hazra,Anomitra Saha,Shubhra Baran Deb,Debasis Mitra +3 more
- 10 Mar 2016
TL;DR: This empirical study identifies three very common and easy to access attributes and proposes a two-step hierarchical clustering technique to solve name ambiguity using these attributes only and shows that the proposed method achieves significantly high level of accuracy for most of the instances.
7
Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platforms
Rima Hazra,Agnik Saha,Animesh Mukherjee +2 more
TL;DR: This investigation reveals a correlation between the time taken to yield the first response to a question and several variables: the metadata, the formulation of the questions, and the level of interaction among users.
Is this bug severe? A text-cum-graph based model for bug severity prediction
Rima Hazra,Arpit Dwivedi,Animesh Mukherjee +2 more
- 01 Jul 2022
TL;DR: This paper takes up the task of predicting the severity of bugs in the near future with Contextualized neural models built on the text description of a bug and the user comments about the bug to achieve reasonably good performance.
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