Works (38)

Updated: April 4th, 2024 14:53

2023 journal article

(Re)Use of Research Results (Is Rampant)

COMMUNICATIONS OF THE ACM, 66(2), 75–81.

Contributors: M. Baldassarre*, N. Ernst*, B. Hermann*, T. Menzies n & R. Yedida n

UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: February 27, 2023

2023 journal article

An expert system for redesigning software for cloud applications

EXPERT SYSTEMS WITH APPLICATIONS, 219.

By: R. Yedida n, R. Krishna, A. Kalia, T. Menzies n, J. Xiao & M. Vukovic

Contributors: R. Yedida n, R. Krishna, A. Kalia, T. Menzies n, J. Xiao & M. Vukovic

author keywords: Software engineering; Microservices; Deep learning; Hyper-parameter optimization; Refactoring
TL;DR: This paper proposes DEEPLY, a new algorithm that extends the CO-GCN deep learning partition generator with a novel loss function and some hyper-parameter optimization, and generally outperforms prior work across multiple datasets and goals. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 27, 2023

2023 journal article

How to Find Actionable Static Analysis Warnings: A Case Study With FindBugs

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 2856–2872.

By: R. Yedida n, H. Kang*, H. Tu*, X. Yang n, D. Lo* & T. Menzies n

Contributors: R. Yedida n, H. Kang*, H. Tu*, X. Yang n, D. Lo* & T. Menzies n

author keywords: Codes; Computer bugs; Static analysis; Training; Source coding; Measurement; Industries; Software analytics; static analysis; false alarms; locality; hyperparameter optimization
TL;DR: It is shown here that effective predictors of static code warnings can be created by methods that locally adjust the decision boundary (between actionable warnings and others), and these methods yield a new high water-mark for recognizing actionablestatic code warnings. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: May 30, 2023

2022 article

How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs

ArXiv.

By: R. Yedida*, H. Kang, H. Tu, X. Yang, D. Lo & T. Menzies

Contributors: R. Yedida*, H. Kang, H. Tu, X. Yang, D. Lo & T. Menzies

Source: ORCID
Added: February 1, 2024

2022 article

How to Improve Deep Learning for Software Analytics (a case study with code smell detection)

2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 156–166.

By: R. Yedida n & T. Menzies n

Contributors: R. Yedida n & T. Menzies n

author keywords: code smell detection; deep learning; autoencoders
TL;DR: The results of this paper show that the method can achieve better than state-of-the-art results on code smell detection with fuzzy oversampling, and suggest other lessons for other kinds of analytics. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: September 19, 2022

2022 article

How to Improve Deep Learning for Software Analytics (a case study with code smell detection)

ArXiv.

By: R. Yedida* & T. Menzies

Contributors: R. Yedida* & T. Menzies

Source: ORCID
Added: February 1, 2024

2022 article

TEXT MINING OF THE PEOPLE'S PHARMACY RADIO SHOW TRANSCRIPTS CAN IDENTIFY NOVEL DRUG REPURPOSING HYPOTHESES

MedRxiv.

By: R. Yedida*, J. Beasley*, D. Korn*, S. Abrar*, C. Melo-Filho*, E. Muratov*, J. Graedon*, T. Graedon*, R. Chirkova, A. Tropsha*

Contributors: R. Yedida*, J. Beasley*, D. Korn*, S. Abrar*, C. Melo-Filho*, E. Muratov*, J. Graedon*, T. Graedon*, R. Chirkova, A. Tropsha*

TL;DR: Text mining of social media is increasingly used to uncover novel relationships between semantic concepts corresponding to biomedical concepts, however, mining audio transcripts of specialized podcast shows has not been explored previously for this purpose. (via Semantic Scholar)
Source: ORCID
Added: February 1, 2024

2021 article

An Expert System for Redesigning Software for Cloud Applications

ArXiv.

By: R. Yedida*, R. Krishna, A. Kalia, T. Menzies, J. Xiao & M. Vukovic

Contributors: R. Yedida*, R. Krishna, A. Kalia, T. Menzies, J. Xiao & M. Vukovic

Source: ORCID
Added: February 1, 2024

2021 article

Beginning with machine learning: a comprehensive primer

Yedida, R., & Saha, S. (2021, July 21). EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, Vol. 7.

By: R. Yedida n & S. Saha*

Contributors: R. Yedida n & S. Saha*

TL;DR: This primer provides an introduction to the subject that is accessible, yet covers all the mathematical details, and provides implementations of most algorithms in Python, to provide a well-rounded understanding of each algorithm. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: August 2, 2021

2021 report

Crowdsourcing the State of the Art(ifacts

(ArXiv Preprint No. 2108.06821).

By: M. Baldassarre, N. Ernst, B. Hermann, T. Menzies & R. Yedida

Source: NC State University Libraries
Added: January 13, 2022

2021 article

Crowdsourcing the state of the art(ifacts)

ArXiv.

By: M. Baldassarre, N. Ernst, B. Hermann, T. Menzies & R. Yedida

Contributors: M. Baldassarre, N. Ernst, B. Hermann, T. Menzies & R. Yedida

Source: ORCID
Added: February 1, 2024

2021 article

Documenting Evidence of a Reuse of 'A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks'

PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1595–1595.

By: R. Yedida n & T. Menzies n

Contributors: R. Yedida n & T. Menzies n

author keywords: reuse; replication; oversampling; defect prediction
TL;DR: The reuse of oversampling, and modifications to the basic approach, used in a recent TSE ’21 paper by YedidaMenzies is reported, which is the oversampled technique studied by Buda et al. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: ORCID, Web Of Science, NC State University Libraries
Added: January 4, 2022

2021 article

Documenting Evidence of a Reuse of 'On the Number of Linear Regions of Deep Neural Networks'

PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1596–1596.

By: R. Yedida n & T. Menzies n

Contributors: R. Yedida n & T. Menzies n

author keywords: reuse; replication; deep learning; defect prediction
TL;DR: The reuse of theoretical insights from deep learning literature is reported here, used in a recent TSE '21 paper by Yedida & Menzies, and the reuse of Theorem 4 from Montufar et al. is documented. (via Semantic Scholar)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: January 4, 2022

2021 journal article

Learning to recognize actionable static code warnings (is intrinsically easy)

EMPIRICAL SOFTWARE ENGINEERING, 26(3).

By: X. Yang n, J. Chen n, R. Yedida n, Z. Yu n & T. Menzies n

Contributors: X. Yang n, J. Chen n, R. Yedida n, Z. Yu n & T. Menzies n

author keywords: Static code analysis; Actionable warnings; Deep learning; Linear SVM; Intrinsic dimensionality
TL;DR: It is found that data mining algorithms can find actionable warnings with remarkable ease and is concluded that learning to recognize actionable static code warnings is easy, using a wide range of learning algorithms, since the underlying data is intrinsically simple. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: May 17, 2021

2021 conference paper

Lessons learned from hyper-parameter tuning for microservice candidate identi cation

Proceedings of the thirty-sixth IEEE/ACM International Conference on Automated Software Engineering (ASE). Presented at the 36th IEEE/ACM International Conference on Automated Software Engineering, (Virtual).

By: R. Yedida, R. Krishna, A. Kalia, T. Menzies, J. Xiao & M. Vukovic

Event: 36th IEEE/ACM International Conference on Automated Software Engineering at (Virtual) on November 14-20, 2021

Source: NC State University Libraries
Added: January 13, 2022

2021 article

Lessons learned from hyper-parameter tuning for microservice candidate identification

ArXiv.

By: R. Yedida*, R. Krishna, A. Kalia, T. Menzies, J. Xiao & M. Vukovic

Contributors: R. Yedida*, R. Krishna, A. Kalia, T. Menzies, J. Xiao & M. Vukovic

Source: ORCID
Added: February 1, 2024

2021 conference paper

Lessons learned from hyper-parameter tuning for microservice candidate identification

Proceedings - 2021 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, 1141–1145.

By: R. Yedida*, R. Krishna, A. Kalia, T. Menzies, J. Xiao & M. Vukovic

Contributors: R. Yedida*, R. Krishna, A. Kalia, T. Menzies, J. Xiao & M. Vukovic

TL;DR: Using a set of open source Java EE applications, it is shown that hyperparameter optimization can significantly improve microservice partitioning and an open issue for future work is how to find which optimizer works best for different problems. (via Semantic Scholar)
Source: ORCID
Added: February 1, 2024

2021 journal article

LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence

APPLIED INTELLIGENCE, 51(3), 1460–1478.

By: R. Yedida n, S. Saha* & T. Prashanth*

Contributors: R. Yedida n, S. Saha* & T. Prashanth*

author keywords: Lipschitz constant; Adaptive learning; Machine learning; Deep learning
TL;DR: A novel theoretical framework for computing large, adaptive learning rates makes minimal assumptions on the activations used and exploits the functional properties of the loss function and shows that the inverse of the Lipschitz constant is an ideal learning rate. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: October 19, 2020

2021 article

Old but Gold: Reconsidering the value of feedforward learners for software analytics

ArXiv.

By: R. Yedida*, X. Yang & T. Menzies

Contributors: R. Yedida*, X. Yang & T. Menzies

Source: ORCID
Added: February 1, 2024

2021 journal article

On the Value of Oversampling for Deep Learning in Software Defect Prediction

IEEE Transactions on Software Engineering, 48(8), 1–1.

By: R. Yedida n & T. Menzies n

Contributors: R. Yedida n & T. Menzies n

author keywords: Deep learning; Tuning; Predictive models; Standards; Prediction algorithms; Training; Tools; Defect prediction; oversampling; class imbalance; neural networks
TL;DR: The results present a cogent case for the use of oversampling prior to applying deep learning on software defect prediction datasets, which can do significantly better than the prior DL state of the art in 14/20 defect data sets. (via Semantic Scholar)
Sources: Web Of Science, ORCID, Crossref, NC State University Libraries
Added: June 12, 2021

2021 journal article

Simpler Hyperparameter Optimization for Software Analytics: Why, How, When

IEEE Transactions on Software Engineering, 48(8), 1–1.

By: A. Agrawal*, X. Yang n, R. Agrawal n, R. Yedida n, X. Shen n & T. Menzies n

Contributors: A. Agrawal*, X. Yang n, R. Agrawal n, R. Yedida n, X. Shen n & T. Menzies n

author keywords: Software analytics; hyperparameter optimization; defect prediction; bad smell detection; issue close time; bug reports
TL;DR: The simple DODGE works best for data sets with low “intrinsic dimensionality” and very poorly for higher-dimensional data; nearly all the SE data seen here was intrinsically low-dimensional, indicating that DODGE is applicable for many SE analytics tasks. (via Semantic Scholar)
Sources: Web Of Science, ORCID, Crossref, NC State University Libraries
Added: June 12, 2021

2021 report

When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time

(ArXiv Preprint No. 2101.06319).

By: R. Yedida, X. Yang & T. Menzies

Source: NC State University Libraries
Added: June 12, 2021

2020 review

Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets

[Review of ]. EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 229(16), 2629–2738.

By: S. Saha*, N. Nagaraj*, A. Mathur, R. Yedida n & H. Sneha

Contributors: S. Saha*, N. Nagaraj*, A. Mathur, R. Yedida n & S. H R

TL;DR: This work presents analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classification of exoplanets, and implements the activation functions in plain vanilla feed-forward neural network to classify ex planets. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 30, 2020

2020 article

How to Recognize Actionable Static Code Warnings (Using Linear SVMs)

ArXiv.

By: X. Yang n, J. Chen n, R. Yedida n, Z. Yu n & T. Menzies n

Contributors: X. Yang n, J. Chen n, R. Yedida n, Z. Yu n & T. Menzies n

Source: ORCID
Added: February 1, 2024

2020 article

On the value of oversampling for deep learning in software defect prediction

ArXiv.

By: R. Yedida* & T. Menzies

Contributors: R. Yedida* & T. Menzies

Source: ORCID
Added: February 1, 2024

2020 chapter

Optimizing Inter-nationality of Journals: A Classical Gradient Approach Revisited via Swarm Intelligence

In Communications in Computer and Information Science: Vol. 1290. Modeling, Machine Learning and Astronomy (Vol. 1290, pp. 3–14).

By: L. Khaidem*, R. Yedida n & A. Theophilus*

Contributors: L. Khaidem*, R. Yedida n & A. Theophilus*

Event: Communications in Computer and Information Science at Bangalore, India on November 22-23, 2019

Sources: Crossref, NC State University Libraries, ORCID
Added: June 12, 2021

2020 conference paper

Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets

2020 International Joint Conference on Neural Networks (IJCNN), 1–8.

By: S. Sridhar*, S. Saha*, A. Shaikh*, R. Yedida n & S. Saha*

Contributors: S. Sridhar*, S. Saha*, A. Shaikh*, R. Yedida n & S. Saha*

Event: 2020 International Joint Conference on Neural Networks (IJCNN)

TL;DR: This work leveraged the functional property of Mean Square Error, which is Lipschitz continuous to compute learning rate in shallow neural networks, and claims that this approach reduces tuning efforts, especially when a significant corpus of data has to be handled. (via Semantic Scholar)
Sources: Crossref, NC State University Libraries, ORCID
Added: June 12, 2021

2020 article

Parsimonious computing: A minority training regime for effective prediction in large microarray expression data sets

ArXiv.

Contributors: .. Sridhar, .. Saha, .. Shaikh, .. Yedida* & .. Saha

Source: ORCID
Added: February 1, 2024

2020 report

Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets

(ArXiv Preprint No. 2011.07959).

By: R. Yedida, S. Abrar, C. Melo-Filho, E. Muratov, R. Chirkova & A. Tropsha

Source: NC State University Libraries
Added: June 12, 2021

2020 article

Text mining to identify and extract novel disease treatments from unstructured datasets

ArXiv.

By: R. Yedida*, S. Abrar, C. Melo-Filho, E. Muratov, R. Chirkova & A. Tropsha

Contributors: R. Yedida*, S. Abrar, C. Melo-Filho, E. Muratov, R. Chirkova & A. Tropsha

Source: ORCID
Added: February 1, 2024

2019 article

Evolution of novel activation functions in neural network training with applications to classification of exoplanets

ArXiv.

By: S. Saha, N. Nagaraj, A. Mathur & R. Yedida

Contributors: S. Saha, N. Nagaraj, A. Mathur & R. Yedida

Source: ORCID
Added: February 1, 2024

2019 article

LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence

ArXiv.

By: R. Yedida* & S. Saha

Contributors: R. Yedida* & S. Saha

Source: ORCID
Added: February 1, 2024

2019 article

Simpler hyperparameter optimization for software analytics: Why, how, when?

ArXiv.

By: A. Agrawal, X. Yang, R. Agrawal, R. Yedida, X. Shen & T. Menzies

Contributors: A. Agrawal, X. Yang, R. Agrawal, R. Yedida, X. Shen & T. Menzies

Source: ORCID
Added: February 1, 2024

2018 speech

An Introduction to Data Analysis

Presented at the PES University, Bangalore, India.

By: R. Yedida

Event: PES University at Bangalore, India

Source: NC State University Libraries
Added: January 13, 2022

2018 article

Employee attrition prediction

ArXiv. http://www.scopus.com/inward/record.url?eid=2-s2.0-85095619264&partnerID=MN8TOARS

By: R. Yedida, R. Reddy, R. Vahi, R. J., Abhilash & D. Kulkarni

Contributors: R. Yedida, R. Reddy, R. Vahi, J. Rahul, Abhilash & D. Kulkarni

Source: ORCID
Added: February 1, 2024

2018 speech

How to design a Flappy Bird game

Presented at the PES University, Bangalore, India.

By: R. Yedida

Event: PES University at Bangalore, India

Source: NC State University Libraries
Added: January 13, 2022

2018 speech

Machine Learning

Presented at the PES University, Bangalore, India.

By: R. Yedida

Event: PES University at Bangalore, India

Source: NC State University Libraries
Added: January 13, 2022

2017 speech

Complexity Classes and NP-Completeness

Presented at the PES University, Bangalore, India.

By: R. Yedida

Event: PES University at Bangalore, India

Source: NC State University Libraries
Added: January 13, 2022

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