Works (7)
Updated: July 5th, 2023 15:34
2021 journal article
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When
IEEE Transactions on Software Engineering, 48(8), 1–1.
Contributors: *, X. Yang n, R. Agrawal n, R. Yedida n , X. Shen n & T. Menzies n
author keywords: Software analytics; hyperparameter optimization; defect prediction; bad smell detection; issue close time; bug reports
TL;DR:
The simple DODGE works best for data sets with low “intrinsic dimensionality” and very poorly for higher-dimensional data; nearly all the SE data seen here was intrinsically low-dimensional, indicating that DODGE is applicable for many SE analytics tasks.
(via Semantic Scholar)
open access via arxiv.org (repository)
Sources: Web Of Science, Crossref, NC State University Libraries, ORCID
Added: June 12, 2021
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: Web Of Science, NC State University Libraries, ORCID
Added: May 8, 2020
2018 article
Characterizing the Influence of Continuous Integration
PROCEEDINGS OF THE 4TH ACM SIGSOFT INTERNATIONAL WORKSHOP ON SOFTWARE ANALYTICS (SWAN'18), pp. 8–14.
author keywords: Continuous Integration; DevOps; GitHub; Mining Software Repositories; Software Development Practice
TL;DR:
The findings indicate that only adoption of CI might not be enough to the improve software development process, and recommend industry practitioners to adopt the best practices of CI to reap the benefits of CI tools for example, making frequent commits.
(via Semantic Scholar)
Source: Web Of Science
Added: April 2, 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
Finding Trends in Software Research
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 1397–1410.
author keywords: Software engineering; Conferences; Software; Analytical models; Data models; Predictive models; Testing; bibliometrics; topic modeling; text mining
TL;DR:
While there is no overall gender bias in SE authorship, it is noted that women are under-represented in the top-most cited papers in the authors' field and a previously unreported dichotomy between software conferences and journals is shown.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 30, 2023
2018 article
Is "Better Data" Better Than "Better Data Miners"? On the Benefits of Tuning SMOTE for Defect Prediction
PROCEEDINGS 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), pp. 1050–1061.
author keywords: Search based SE; defect prediction; classification; data analytics for software engineering; SMOTE; imbalanced data; preprocessing
TL;DR:
For software analytic tasks like defect prediction, data pre-processing can be more important than classifier choice, ranking studies are incomplete without such pre- Processing, and SMOTUNED is a promising candidate for pre- processing.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: January 21, 2019
2018 journal article
What is wrong with topic modeling? And how to fix it using search-based software engineering
INFORMATION AND SOFTWARE TECHNOLOGY, 98, 74–88.
author keywords: Topic modeling; Stability; LDA; Tuning; Differential evolution
TL;DR:
LDADE, a search-based software engineering tool which uses Differential Evolution (DE) to tune the LDA’s parameters, is used to provide a method in which distributions generated by LDA are more stable and can be used for further analysis.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018
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