Alapfogalmak
The author presents a novel fault localization technique using conditional probability and grouping to improve software debugging efficiency significantly.
Kivonat
The content discusses the importance of fault localization in software development, highlighting various existing techniques and introducing a new approach that combines conditional probability statistics with grouping for better results. The proposed method, CGFL, outperforms other contemporary fault localization methods by 24.56% on average across eleven open-source datasets. The article provides detailed insights into the methodology, experimental results, and comparisons with existing techniques.
Statisztikák
Our obtained results show that on average, the proposed CGFL method is 24.56% more effective than other contemporary fault localization methods such as D∗, Tarantula, Ochiai, Crosstab, BPNN, RBFNN, DNN, and CNN.
By examining only 2% of the program statements CGFL(Best) localizes bugs in 44.66% of faulty versions.
To localize bugs in all the faulty versions present in the Siemens suite, CGFL examines 11.24% of program code.
On average, to localize bugs in all the faulty versions present in the Siemens suite, CGFL examines 11.24%, RBFNN examines 21.63%, and BPNN examines 16.43% of program code respectively.
For most of the EXAM Score points, CGFL(Best) performs better than both DNN and CNN.