Kewen Peng

College of Engineering

Works (7)

Updated: April 5th, 2024 13:53

2023 article

Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent

2023 23RD IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW 2023, pp. 1543–1550.

By: J. Liu n, Z. Mei n, K. Peng n & R. Vatsavai n 

TL;DR: A novel method is presented, PK-NCLI, that is able to accurately and efficiently identify relevant auxiliary information to improve the quality of conversational responses by learning the relevance among persona, chat history, and knowledge background through lowlevel normalized contextual latent interaction. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 18, 2024

2023 journal article

VEER: enhancing the interpretability of model-based optimizations

EMPIRICAL SOFTWARE ENGINEERING, 28(3).

By: K. Peng n, C. Kaltenecker *, N. Siegmund *, S. Apel * & T. Menzies n 

author keywords: Software analytics; Multi-objective optimization; Disagreement; Interpretable AI
TL;DR: A dimension reduction method called VEER is proposed that builds a useful one-dimensional approximation to the original N-objective space that improves the execution time, but also resolves the potential model disagreement problem. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: April 24, 2023

2022 journal article

FairMask: Better Fairness via Model-Based Rebalancing of Protected Attributes

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 2426–2439.

By: K. Peng n, J. Chakraborty n & T. Menzies n 

author keywords: Software fairness; explanation; bias mitigation
TL;DR: This work proposes a model-based extrapolation method that corrects the misleading latent correlation between the protected attributes and other non-protected ones and achieves significantly better group and individual fairness than benchmark methods. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 30, 2023

2021 article

Documenting Evidence of a Reuse of "'Why Should I Trust You?": Explaining the Predictions of Any Classifier'

PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1600–1600.

By: K. Peng n & T. Menzies n 

author keywords: Software analytics; Actionable analysis
TL;DR: The framework LIME, a local instance-based explanation generation framework that was originally proposed by Ribeiro et al. in their paper "'Why Should I Trust You?': Explaining the Predictions of Any Classifier", was reused by Peng et al.'s paper "Defect Reduction Planning (using TimeLIME). (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 7, 2022

2021 article

Documenting Evidence of a Reuse of 'What is a Feature? A Qualitative Study of Features in Industrial Software Product Lines'

PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1599–1599.

By: K. Peng n & T. Menzies n 

author keywords: Software analytics; Software product lines; Software configuration
TL;DR: An example of reuse is the paper "Dimensions of software configuration: on the configuration context in modern software development" by Siegmund et al. reused definitions and theories about configuration features in the original paper. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 7, 2022

2021 journal article

ON THE NOETHER BOUND FOR NONCOMMUTATIVE RINGS

PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY, 149(7), 2711–2725.

By: L. Ferraro *, E. Kirkman *, W. Moore * & K. Peng n

Source: Web Of Science
Added: June 10, 2021

2020 article

Making Fair ML Software using Trustworthy Explanation

2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020), pp. 1229–1233.

By: J. Chakraborty n, K. Peng n & T. Menzies n 

TL;DR: This work shows how the proposed method based on K nearest neighbors can overcome shortcomings and find the underlying bias of black box models and describes the future framework combining explanation and planning to build fair software. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: June 10, 2021

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