1,007 research outputs found
minkull/A12-Effect-Size: Standard and Temporal A12
No full text<p>This release contains one standard implementation of A12 effect size, for instance used in the following paper:</p>
<p>MINKU, L.; HOU, S.. "Clustering Dycom: An Online Cross-Company Software Effort Estimation Study", Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE), November 2017.</p>
<p>It also contains two temporal versions of A12, where A12 is computed multiple times across time. The window-based version (a12temporal2.m) was used in the following paper:</p>
<p>MINKU, L.L.; YAO, X. . "Which Models of the Past Are Relevant to the Present? A software effort estimation approach to exploiting useful past models", Automated Software Engineering Journal, v. 24, n. 7, p. 499-542, September 2017, doi: 10.1007/s10515-016-0209-7.</p>
minkull/OATES:
No full textThis release contains OATES code used in the following paper, after renaming some of the variables and class names:
MINKU, L.L. Multi-Stream Online Transfer Learning For Software Effort Estimation -- Is It Necessary? International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE) 2021
michaelchiucw/DiversityPool: DP-IJCNN2018
No full textThis version of Diversity Pool is used in [1].
[1] CHIU, C.W.; MINKU, L.L. . "Diversity-Based Pool of Models for Dealing with Recurring Concepts", IEEE International Joint Conference on Neural Networks, p. 2759-2766, July 2018
minkull/CommitGuru-Chinese: First release
No full textFirst release of the extension of the Commit Guru tool to enable using Chinese language.
The keywords used to identify corrective commits in Chinese were chosen as explained in:
TABASSUM, S.; MINKU, L.L.; FENG, D.; CABRAL, G.; SONG, L. . "An Investigation of Cross-Project Learning in Online Just-In-Time Software Defect Prediction", 2020 International Conference on Software Engineering (ICSE), 2020 (accepted).
These keywords are in the file CommitGuru-Chinese/classifier/Categories/corrective.csv
They are used by the classifier in CommitGuru-Chinese/classifier/classifier.p
sadiaTab/CPJITSDP: Online-CPJITSDP-v1.0
No full textImplementation of Online CPJITSDP proposed and used in [1] and [2].
[1] Tabassum, S., Minku, L.L., Feng, D., Cabral, G.G. and Song, L., 2020, October. An investigation of cross-project learning in online just-in-time software defect prediction. In 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) (pp. 554-565). IEEE.
[2] Sadia Tabassum, Leandro L. Minku, Senior Member, IEEE, Danyi Feng, "Cross-Project Online Just-In-Time Software Defect Prediction", (accepted in TSE, 2022
GustavoHFMO/OGMMF-VRD: OGMMF-VRD First Release.
No full textAlgorithms used in the following paper: Oliveira, Gustavo, Leandro Minku, and Adriano Oliveira. "Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model." arXiv preprint arXiv:2102.05983 (2021)
minkull/SoftwareProjectScheduling:
No full textRelease supporting book chapter "Artificial Intelligence in Software Project Management" from the book "Optimising the Software Development Process with Artificial Intelligence"
minkull/UOB-OOB-MultiClass: First release
No full textThe multi-class version of Oversampling and Undersampling Online Bagging for class imbalanced data stream learning. It can be applied both to multi-class and binary classification problems
How to Make Best Use of Cross-Company Data for Web Effort Estimation?
Full text link[Context]: The numerous challenges that can hinder software companies from gathering their own data have motivated over the past 15 years research on the use of cross-company (CC) datasets for software effort prediction. Part of this research focused on Web effort prediction, given the large increase worldwide in the development of Web applications. Some of these studies indicate that it may be possible to achieve better performance using CC models if some strategy to make the CC data more similar to the within-company (WC) data is adopted. [Goal]: This study investigates the use of a recently proposed approach called Dycom to assess to what extent Web effort predictions obtained using CC datasets are effective in relation to the predictions obtained using WC data when explicitly mapping the CC models to the WC context. [Method]: Data on 125 Web projects from eight different companies part of the Tukutuku database were used to build prediction models. We benchmarked these models against baseline models (mean and median effort) and a WC base learner that does not benefit of the mapping. We also compared Dycom against a competitive CC approach from the literature (NN-filtering). We report a company-by- company analysis. [Results]: Dycom usually managed to achieve similar or better performance than a WC model while using only half of the WC training data. These results are also an improvement over previous studies that investigated the use of different strategies to adapt CC models to the WC data for Web effort estimation. [Conclusions]: We conclude that the use of Dycom for Web effort prediction is quite promising and in general supports previous results when applying Dycom to conventional software datasets
GustavoHFMO/OGMMF-VRD: v1.0.1
No full textAlgorithms used in the following paper: G. Oliveira, L. L. Minku and A. L. I. Oliveira, "Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model Approach," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2021.3099690
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