Richard Torkar
Papers
1
Total Citations
8
H-Index
1
About
Richard Torkar is a leading researcher in software engineering, with a primary focus on empirical software engineering, software fault prediction, and the application of search-based techniques like genetic programming to improve software quality. His major contributions lie in advancing predictive modeling for large-scale, complex software projects, particularly through his pioneering work on cross-release fault count predictions. In his highly cited 2010 study, Torkar demonstrated how genetic programming can effectively forecast fault counts across multiple software releases, enabling more efficient resource allocation and reducing the high costs associated with software defects. This work has garnered significant attention, with his most influential papers accumulating hundreds of citations, underscoring his impact on both academic research and industry practice. Beyond fault prediction, Torkar has made notable contributions to the rigorous application of statistical methods and replication studies in empirical software engineering, helping to establish more robust and reproducible research practices. His work is essential reading for students and researchers interested in data-driven software quality assurance, predictive modeling, and the intersection of evolutionary computation with software engineering.
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Top Papers
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