Empirical Evaluation of Mixed-Project Defect Prediction Models

2011 ◽  
Author(s):  
Burak Turhan ◽  
Ayse Tosun ◽  
Ayse Bener
Keyword(s):  
Prediction Models ◽  
Empirical Evaluation ◽  
Defect Prediction ◽  
Defect Prediction Models

scholarly journals Feature Subset Selection and Instance Filtering for Cross-project Defect Prediction - Classification and Ranking

2016 ◽  
Author(s):  
Faimison Porto ◽  
Adenilso Da Silva Simao
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Empirical Evaluation ◽  
Feature Subset Selection ◽  
Training Dataset ◽  
Defect Prediction ◽  
Feature Subset ◽  
Good Tool ◽  
Defect Prediction Models ◽  
Cross Project

The defect prediction models can be a good tool on organizing the project´s test resources. The models can be constructed with two main goals: 1) to classify the software parts - defective or not; or 2) to rank the most defective parts in a decreasing order. However, not all companies maintain an appropriate set of historical defect data. In this case, a company can build an appropriate dataset from known external projects - called Cross-project Defect Prediction (CPDP).The CPDP models, however, present low prediction performances due to the heterogeneity of data. Recently, Instance Filtering methods were proposed in order to reduce this heterogeneity by selecting the most similar instances from the training dataset. Originally, the similarity is calculated based on all the available dataset features (or independent variables).We propose that using only the most relevant features on the similarity calculation can result in more accurate filtered datasets and better prediction performances. In this study we extend our previous work. We analyse both prediction goals - Classification and Ranking. We present an empirical evaluation of 41 different methods by associating Instance Filtering methods with Feature Selection methods. We used 36 versions of 11 open source projects on experiments.The results show similar evidences for both prediction goals. First, the defect prediction performance of CPDP models can be improved by associating Feature Selection and Instance Filtering. Second, no evaluated method presented general better performances. Indeed, the most appropriate method can vary according to the characteristics of the project being predicted.

scholarly journals On the time-based conclusion stability of cross-project defect prediction models

2020 ◽  
Vol 25 (6) ◽  
pp. 5047-5083
Author(s):  
Abdul Ali Bangash ◽  
Hareem Sahar ◽  
Abram Hindle ◽  
Karim Ali
Keyword(s):  
Prediction Models ◽  
Defect Prediction ◽  
Defect Prediction Models ◽  
Cross Project

scholarly journals The Impact of Automated Parameter Optimization on Defect Prediction Models

2019 ◽  
Vol 45 (7) ◽  
pp. 683-711 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E. Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Parameter Optimization ◽  
Prediction Models ◽  
Defect Prediction ◽  
Defect Prediction Models ◽  
The Impact
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