Jianfeng Chen

Works (9)

Updated: July 5th, 2023 15:37

2021 journal article

How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization)

EMPIRICAL SOFTWARE ENGINEERING, 26(3).

By: R. Shu n, T. Xia n, J. Chen n, L. Williams n & T. Menzies n 

author keywords: Hyperparameter Optimization; Data pre-processing; Security bug report
TL;DR: The SWIFT’s dual optimization of both pre-processor and learner is more useful than optimizing each of them individually, and this approach can quickly optimize models that achieve better recalls than the prior state-of-the-art. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 3, 2021

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

2019 journal article

Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(6), 1259–1276.

By: J. Wang *, S. Wang *, J. Chen n, T. Menzies n , Q. Cui, M. Xie *, Q. Wang *

author keywords: Crowdsourced testing; crowd worker recommendation; multi-objective optimization
TL;DR: Multi-Objective Crowd wOrker recoMmendation approach (MOCOM), which aims at recommending a minimum number of crowd workers who could detect the maximum number of bugs for a crowdsourced testing task, significantly outperforms five commonly-used and state-of-the-art baselines. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: June 28, 2021

2019 article

Predicting Breakdowns in Cloud Services (with SPIKE)

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. 916–924.

By: J. Chen n, J. Chakraborty n, P. Clark*, K. Haverlock*, S. Cherian* & T. Menzies n 

author keywords: Cloud; optimization; data mining; parameter tuning
TL;DR: SPIKE is a data mining tool which can predict upcoming service breakdowns, half an hour into the future, and performed relatively better than other widely-used learning methods (neural nets, random forests, logistic regression). (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: October 7, 2019

2018 journal article

"Sampling" as a Baseline Optimizer for Search-Based Software Engineering

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 45(6), 597–614.

By: J. Chen n, V. Nair n, R. Krishna  n & T. Menzies n 

author keywords: Search-based SE; sampling; evolutionary algorithms
TL;DR: This paper compares Sway versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 1, 2019

2018 article

Data-Driven Search-based Software Engineering

2018 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR), pp. 341–352.

By: V. Nair n, A. Agrawal n, J. Chen n, W. Fu n, G. Mathew  n, T. Menzies n , L. Minku *, M. Wagner *, Z. Yu n 

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 article

RIOT: a Stochastic-based Method for Workflow Scheduling in the Cloud

PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD), pp. 318–325.

By: J. Chen n & T. Menzies n 

author keywords: cloud computing; workflow scheduling; multiobjective optimization
TL;DR: RIOT (Randomized Instance Order Types), a stochastic based method for workflow scheduling that groups the tasks in the workflow into virtual machines via a probability model and then uses an effective surrogate based method to assess large amount of potential schedulings. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: January 21, 2019

2017 journal article

Beyond evolutionary algorithms for search-based software engineering

INFORMATION AND SOFTWARE TECHNOLOGY, 95, 281–294.

By: J. Chen n, V. Nair n & T. Menzies n 

TL;DR: This work builds a very large initial population which is then culled using a recursive bi-clustering chop approach, and evaluates this approach on multiple SE models, unconstrained as well as constrained, and compare its performance with standard evolutionary algorithms. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2016 article

An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE

SEARCH BASED SOFTWARE ENGINEERING, SSBSE 2016, Vol. 9962, pp. 96–111.

By: V. Nair n, T. Menzies n  & J. Chen n

author keywords: Search-based SE; Sampling; Evolutionary algorithms
TL;DR: Experiments with Software Engineering (SE) models shows that SWAY’s performance improvement is competitive with standard MOEAs while, terminating over an order of magnitude faster. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

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