Works (8)
Updated: July 5th, 2023 15:37
2021 journal article
Whence to Learn? Transferring Knowledge in Configurable Systems Using BEETLE
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(12), 2956–2972.
author keywords: Performance optimization; SBSE; transfer learning; bellwether
TL;DR:
This paper proposes a novel transfer learning framework called BEETLE, which is a “bellwether”-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data.
(via Semantic Scholar)
arxiv.org (repository)
Sources: Web Of Science, ORCID
Added: January 3, 2022
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
Added: July 1, 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, ORCID
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, ORCID
Added: March 4, 2019
2018 article
Is One Hyperparameter Optimizer Enough?
PROCEEDINGS OF THE 4TH ACM SIGSOFT INTERNATIONAL WORKSHOP ON SOFTWARE ANALYTICS (SWAN'18), pp. 19–25.
author keywords: Defect Prediction; SBSE; Hyperparameter Tuning
TL;DR:
It is concluded that hyperparameter optimization is more nuanced than previously believed and, while such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.
(via Semantic Scholar)
Source: Web Of Science
Added: April 2, 2019
2018 article
Micky: A Cheaper Alternative for Selecting Cloud Instances
PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD), pp. 409–416.
TL;DR:
A collective-optimizer is created, MICKY, that reformulates the task of finding the near-optimal cloud configuration as a multi-armed bandit problem and can achieve on average 8.6 times reduction in measurement cost as compared to the state-of-the-art method while finding near-Optimal solutions.
(via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: January 21, 2019
2017 article
Using Bad Learners to Find Good Configurations
ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 257–267.
author keywords: Performance Prediction; SBSE; Sampling; Rank-based method
TL;DR:
This paper demonstrates that performance models that are cheap to learn but inaccurate can still be used rank configurations and hence find the optimal configuration and significantly reduce the cost as well as the time required to build performance models.
(via Semantic Scholar)
Sources: Web Of Science, ORCID
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.
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, ORCID
Added: August 6, 2018
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