Works (9)
Updated: July 5th, 2023 15:37
2021 journal article
Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(6), 1259–1276.
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
2021 journal article
How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization)
EMPIRICAL SOFTWARE ENGINEERING, 26(3).
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)
arxiv.org (repository)
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).
Contributors: X. Yang n, 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
"Sampling" as a Baseline Optimizer for Search-Based Software Engineering
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 45(6), 597–614.
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
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.
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
Beyond evolutionary algorithms for search-based software engineering
INFORMATION AND SOFTWARE TECHNOLOGY, 95, 281–294.
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
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 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.
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)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: January 21, 2019
2016 article
An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE
SEARCH BASED SOFTWARE ENGINEERING, SSBSE 2016, Vol. 9962, pp. 96–111.
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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