138 citations · 477 across the 59 of their papers we have counts for
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AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models
Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated to defect models. Prior…
The Impact of IR-based Classifier Configuration on the Performance and the Effort of Method-Level Bug Localization
Chakkrit Tantithamthavorn, Surafel Lemma Abebe, Ahmed E. Hassan +2
Context: IR-based bug localization is a classifier that assists developers in locating buggy source code entities (e.g., files and methods) based on the content of a bug report. Su…
The Impact of Correlated Metrics on Defect Models
Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Ahmed E. Hassan
Defect models are analytical models that are used to build empirical theories that are related to software quality. Prior studies often derive knowledge from such models using inte…
The Impact of Automated Parameter Optimization on Defect Prediction Models
Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan +1
Defect prediction models---classifiers that identify defect-prone software modules---have configurable parameters that control their characteristics (e.g., the number of trees in a…
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models
Chakkrit Tantithamthavorn, Ahmed E. Hassan, Kenichi Matsumoto
Defect prediction models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to pr…