An empirical study on predictability of software maintainability using imbalanced data

2020 ◽  
Vol 28 (4) ◽  
pp. 1581-1614
Author(s):  
Ruchika Malhotra ◽  
Kusum Lata
Keyword(s):  
Empirical Study ◽  
Imbalanced Data ◽  
Software Maintainability

An Empirical Study of Boosting Methods on Severely Imbalanced Data

2014 ◽  
Vol 513-517 ◽  
pp. 2510-2513 ◽  
Author(s):  
Xu Ying Liu
Keyword(s):  
Empirical Study ◽  
Real World ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real World Applications ◽  
Under Sampling ◽  
The Difference ◽  
Imbalance Learning ◽  
Class Imbalance Learning ◽  
F Measure

Nowadays there are large volumes of data in real-world applications, which poses great challenge to class-imbalance learning: the large amount of the majority class examples and severe class-imbalance. Previous studies on class-imbalance learning mainly focused on relatively small or moderate class-imbalance. In this paper we conduct an empirical study to explore the difference between learning with small or moderate class-imbalance and learning with severe class-imbalance. The experimental results show that: (1) Traditional methods cannot handle severe class-imbalance effectively. (2) AUC, G-mean and F-measure can be very inconsistent for severe class-imbalance, which seldom appears when class-imbalance is moderate. And G-mean is not appropriate for severe class-imbalance learning because it is not sensitive to the change of imbalance ratio. (3) When AUC and G-mean are evaluation metrics, EasyEnsemble is the best method, followed by BalanceCascade and under-sampling. (4) A little under-full balance is better for under-sampling to handle severe class-imbalance. And it is important to handle false positives when design methods for severe class-imbalance.

An empirical study on the impact of static typing on software maintainability

2013 ◽  
Vol 19 (5) ◽  
pp. 1335-1382 ◽  
Author(s):  
Stefan Hanenberg ◽  
Sebastian Kleinschmager ◽  
Romain Robbes ◽  
Éric Tanter ◽  
Andreas Stefik
Keyword(s):  
Empirical Study ◽  
Software Maintainability ◽  
The Impact ◽  
Static Typing

An empirical study for software change prediction using imbalanced data

2017 ◽  
Vol 22 (6) ◽  
pp. 2806-2851 ◽  
Author(s):  
Ruchika Malhotra ◽  
Megha Khanna
Keyword(s):  
Empirical Study ◽  
Imbalanced Data ◽  
Software Change ◽  
Change Prediction
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