Sanjay Podder

TL;DR: This work proposes a retrieval and classification model using Siamese Convolutional Neural Networks and Long Short Term Memory for accurate detection and retrieval of duplicate and similar bugs and illustrates the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.

TL;DR: This work presents the solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers and developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application.

TL;DR: This paper proposes an extensible framework based on standard provenance model specifications and blockchain technology for capturing, storing, exploring, and analyzing software provenance data and demonstrates the utility of the proposed framework using open source project data.

TL;DR: This work uses the recent advances in representation learning to propose a neural architecture for the problem of natural language inference that achieves better accuracy numbers than all published models in literature.