Abstract: The bug prediction effectiveness reasonably contributes towards enhancing quality of software. Bug indicators contribute significantly in determining the bug prediction approaches and help in achieving software reliability. Various comparative research studies have indicated that Depth of Inheritance (DIT), Weighted Method per Class (WMC), Coupling between Objects (CBO) and Lines of Code (LoC) have significantly established themselves as reliable bug indicators for comprehensive bug predictions. The researchers have carried out a quantitative research and have developed prediction models using above bug indicators as models input and have applied these models on open source projects (Camel and Ant). During this research, the results demonstrates that there is significant correlation between size oriented metrics (bug indicators) such as DIT, WMC, CBO, LoC and bugs. Overall, DIT takes dominance in achieving better impact on predicting bugs than WMC, CBO and LoC. The outcomes of the present research study would be of significance to software quality practitioners worldwide and would help them in prioritizing the efforts involved in bug prediction.

Varuna Gupta, Dr. N. Ganeshan and Dr. Tarun K. Singhal, “Developing Software Bug Prediction Models Using Various Software Metrics as the Bug Indicators” International Journal of Advanced Computer Science and Applications(IJACSA), 6(2), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060209

@article{Gupta2015,
title = {Developing Software Bug Prediction Models Using Various Software Metrics as the Bug Indicators},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060209},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060209},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {2},
author = {Varuna Gupta and Dr. N. Ganeshan and Dr. Tarun K. Singhal}
}