Works (12)
Updated: October 1st, 2024 10:54
2020 journal article
Better Data Labelling With EMBLEM (and how that Impacts Defect Prediction)
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 48(1), 278–294.
author keywords: Human-in-the-loop AI; data labelling; defect prediction; software analytics
TL;DR:
This approach, called EMBLEM, an AI tool first explore the software development process to label commits that are most problematic, and humans then apply their expertise to check those labels (perhaps resulting in the AI updating the support vectors within their SVM learner).
(via Semantic Scholar)
open access via arxiv.org (repository)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: January 11, 2022
2020 journal article
Better software analytics via "DUO": Data mining algorithms using/used-by optimizers
EMPIRICAL SOFTWARE ENGINEERING, 25(3), 2099–2136.
author keywords: Software analytics; Data mining; Optimization; Evolutionary algorithms
TL;DR:
It is possible, useful and necessary to combine data mining and optimization using DUO, and the era of papers that just use data miners is coming to an end.
(via Semantic Scholar)
Sources: NC State University Libraries, Web Of Science, ORCID
Added: May 8, 2020
2020 journal article
Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 48(5), 1676–1691.
author keywords: Software; Machine learning; Pattern recognition; Training; Computer hacking; Machine learning algorithms; Estimation; Technical debt; software engineering; machine learning; pattern recognition
TL;DR:
Jitterbug is proposed, a two-step framework for identifying SATDs that identifies the “easy to find” SATDs automatically with close to 100 percent precision using a novel pattern recognition technique and machine learning techniques are applied to assist human experts in manually identifying the remaining “hard to find
(via Semantic Scholar)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: May 17, 2022
2020 journal article
Understanding static code warnings: An incremental AI approach
EXPERT SYSTEMS WITH APPLICATIONS, 167.
author keywords: Actionable warning identification; Active learning; Static analysis; Selection process
TL;DR:
An incremental AI tool that watches humans reading false alarm reports can quickly learn to distinguish spurious false alarms from more serious matters that deserve further attention and can identify over 90% of actionable warnings in a priority order given by the algorithm.
(via Semantic Scholar)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: November 24, 2020
2019 journal article
Improving Vulnerability Inspection Efficiency Using Active Learning
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(11), 2401–2420.
author keywords: Inspection; Software; Tools; Security; Predictive models; Error correction; NIST; Active learning; security; vulnerabilities; software engineering; error correction
TL;DR:
HARMLESS is an incremental support vector machine tool that builds a vulnerability prediction model from the source code inspected to date, then suggests what source code files should be inspected next, then provides feedback on when to stop.
(via Semantic Scholar)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: November 12, 2021
2019 article
TERMINATOR: Better Automated UI Test Case Prioritization
ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 883–894.
author keywords: automated UI testing; test case prioritization; total recall
TL;DR:
A novel TCP approach is proposed, that dynamically re-prioritizes the test cases when new failures are detected, by applying and adapting a state of the art framework from the total recall problem.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: October 7, 2019
2018 journal article
An Alternating Direction Method of Multipliers Based Approach for PMU Data Recovery
IEEE TRANSACTIONS ON SMART GRID, 10(4), 4554–4565.
author keywords: Missing data recovery; alternating direction method of multipliers; low-rank matrix completion; phasor measurement units
TL;DR:
An efficient algorithm, alternating direction method of multipliers (ADMM), is proposed for solving the matrix completion problem of missing phasor measurement unit (PMU) data and provides better performance in computation complexity.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: July 8, 2019
2018 article
Data-Driven Search-based Software Engineering
2018 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR), pp. 341–352.
TL;DR:
It is argued that combining these two fields is useful for situations which require learning from a large data source or when optimizers need to know the lay of the land to find better solutions, faster.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 4, 2019
2018 journal article
FAST(2): An intelligent assistant for finding relevant papers
EXPERT SYSTEMS WITH APPLICATIONS, 120, 57–71.
author keywords: Active learning; Literature reviews; Text mining; Semi-supervised learning; Relevance feedback; Selection process
TL;DR:
It is shown that FAST2 robustly optimizes the human effort to find most (95%) of the relevant software engineering papers while also compensating for the errors made by humans during the review process.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 18, 2019
2018 journal article
Finding Faster Configurations Using FLASH
IEEE Transactions on Software Engineering, 46(7), 794–811.
author keywords: Software systems; Optimization; Throughput; Storms; Task analysis; Cloud computing; Performance prediction; search-based SE; configuration; multi-objective optimization; sequential model-based methods
TL;DR:
Flash is introduced, a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore, which reduces the effort required to find the better configuration.
(via Semantic Scholar)
Source: ORCID
Added: July 16, 2020
2018 journal article
Finding better active learners for faster literature reviews
EMPIRICAL SOFTWARE ENGINEERING, 23(6), 3161–3186.
author keywords: Active learning; Systematic literature review; Software engineering; Primary study selection
TL;DR:
This paper finds and implements FASTREAD, a faster technique for studying a large corpus of documents, combining and parametrizing the most efficient active learning algorithms.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
4. Quality Education
(OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: December 17, 2018
2018 article
Total Recall, Language Processing, and Software Engineering
PROCEEDINGS OF THE 4TH ACM SIGSOFT INTERNATIONAL WORKSHOP ON NLP FOR SOFTWARE ENGINEERING (NL4SE '18), pp. 10–13.
author keywords: Software engineering; active learning; natural language processing; information retrieval; total recall; literature review; vulnerabilities
TL;DR:
It is claimed that by applying and adapting the state of the art active learning and natural language processing algorithms for solving the total recall problem, two important software engineering tasks can also be addressed: supporting large literature reviews and identifying software security vulnerabilities.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: April 15, 2019
Employment
Updated: August 17th, 2020 14:01
2020 - present
Rochester Institute of Technology Rochester, NY, US
Assistant Professor
Software Engineering
Education
Updated: August 17th, 2020 14:00
2015 - 2020
North Carolina State University Raleigh, NC, US
Ph. D.
Computer Science
2011 - 2014
Shanghai Jiao Tong University Shanghai, CN
Master
Automation
2007 - 2011
Shanghai Jiao Tong University Shanghai, CN
Bachelor
Automation
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