Works (6)
Updated: August 21st, 2023 08:59
2022 journal article
Predicting health indicators for open source projects (using hyperparameter optimization)
EMPIRICAL SOFTWARE ENGINEERING, 27(6).
author keywords: Hyperparameter optimization; Project health; Machine learning
TL;DR:
This is the largest study yet conducted, using recent data for predicting multiple health indicators of open-source projects, and finds that traditional estimation algorithms make many mistakes.
(via Semantic Scholar)
arxiv.org (repository)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 5, 2022
2018 article
Applications of Psychological Science for Actionable Analytics
ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 456–467.
author keywords: Decision trees; heuristics; software analytics; psychological science; empirical studies; defect prediction
TL;DR:
Assessment of Fast-and-Frugal Trees for software analytics finds that FFTs are remarkably effective in that their models are very succinct (5 lines or less describing a binary decision tree) while also outperforming result from very recent, top-level, conference papers.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 25, 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
Heterogeneous Defect Prediction
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 44(9), 874–896.
author keywords: Defect prediction; quality assurance; heterogeneous metrics; transfer learning
Sources: Web Of Science, ORCID, NC State University Libraries
Added: October 16, 2018
2016 article
Too Much Automation? The Bellwether Effect and Its Implications for Transfer Learning
2016 31ST IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE), pp. 122–131.
author keywords: Defect Prediction; Data Mining; Transfer learning
TL;DR:
This bellwether method is a useful (and very simple) transfer learning method; “bellwethers” are a baseline method against which future transfer learners should be compared; sometimes, when building increasingly complex automatic methods, researchers should pause and compare their supposedly more sophisticated method against simpler alternatives.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018
2016 journal article
Tuning for software analytics: Is it really necessary?
Information and Software Technology, 76, 135–146.
author keywords: Defect prediction; CART; Random forest; Differential evolution; Search-based software engineering
TL;DR:
This paper finds that it is no longer enough to just run a data miner and present the result without conducting a tuning optimization study, and that standard methods in software analytics need to change.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, Crossref
Added: August 6, 2018
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