An empirical study on the usage of testability information to fault localization in software
Proceedings of the 2011 ACM Symposium on Applied Computing
Prioritizing tests for fault localization through ambiguity group reduction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Investigating unexpected outcomes through the application of statistical debuggers
Proceedings of the Winter Simulation Conference
A dynamic code coverage approach to maximize fault localization efficiency
Journal of Systems and Software
| Hi-index | 0.00 |
The resources available for testing and verifying software are always limited, and through sheer numbers an application's user community will uncover many flaws not caught during development. The Cooperative Bug Isolation Project (CBI) marshals large user communities into a massive distributed debugging army to help programmers find and fix problems that appear after deployment. Dynamic instrumentation based on sparse random sampling provides our raw data; statistical machine learning techniques mine this data for critical bug predictors; static program analysis places bug predictors back in context of the program under study. We discuss CBI's dynamic, statistical, and static views of postdeployment debugging and show how these three different approaches join together to help improve software quality in an imperfect world.