Rahul Krishna Prasad

Works (3)

Updated: July 5th, 2023 15:37

2022 article

Methods for stabilizing models across large samples of projects (with case studies on predicting defect and project health)

Majumder, S., Xia, T., Krishna, R., & Menzies, T. (2022, May 23). 2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 566–578.

By: S. Majumder n, T. Xia n, R. Krishna n & T. Menzies n 

author keywords: Defect Prediction; Project Health; Bellwether; Hierarchical Clustering; Random Forest; Two Phase Transfer Learning; Transfer Learning
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software System Performance and Reliability
TL;DR: This paper provides a promising result showing such stable models can be generated using a new transfer learning framework called STABILIZER, and these case studies are the largest demonstration of the generalizability of quantitative predictions of project quality yet reported in the SE literature. (via Semantic Scholar)
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9. Industry, Innovation and Infrastructure (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: September 19, 2022

2017 article

Less is more: Minimizing code reorganization using XTREE

Krishna, R., Menzies, T., & Layman, L. (2017, March 27). Information and Software Technology, Vol. 88, pp. 53–66.

By: R. Krishna n, T. Menzies n  & L. Layman *

author keywords: Bad smells; Performance prediction; Decision trees
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software System Performance and Reliability
TL;DR: Before undertaking a code reorganization based on a bad smell report, use a framework like XTREE to check and ignore any such operations that are useless; i.e. ones which lack evidence in the historical record that it is useful to make that change. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2016 article

Too much automation? the bellwether effect and its implications for transfer learning

Krishna, R., Menzies, T., & Fu, W. (2016, August 25). 2016 31ST IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE), pp. 122–131.

By: R. Krishna n, T. Menzies n  & W. Fu n

author keywords: Defect Prediction; Data Mining; Transfer learning
topics (OpenAlex): Software Engineering Research; Machine Learning and Algorithms; Machine Learning and Data Classification
TL;DR: This bellwether method is a useful (and very simple) transfer learning method; “bellwethers” are a baseline method against which future transfer learners should be compared; sometimes, when building increasingly complex automatic methods, researchers should pause and compare their supposedly more sophisticated method against simpler alternatives. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

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