Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
Tracking down software bugs using automatic anomaly detection
Proceedings of the 24th International Conference on Software Engineering
A Rigorous Model of Service Component Architecture
Electronic Notes in Theoretical Computer Science (ENTCS)
A rigorous approach towards test case generation
Information Sciences: an International Journal
Online Monitoring of Software System Reliability
EDCC '10 Proceedings of the 2010 European Dependable Computing Conference
Reliable performance prediction for multigrid software on distributed memory systems
Advances in Engineering Software
Port-Based Reliability Computing for Service Composition
IEEE Transactions on Services Computing
| Hi-index | 0.07 |
Reliability is an essential software quality requirement, especially for online service software. Without an accurate prediction of service reliability, any unexpected failure can disrupt service. The majority of existing models use static data collected prior to the release of the software. These types of models may predict the reliability of the software as it was during the data collection phase. However, online service software is continuously evolving, and their behaviors can be changed by the runtime usage. Thus, the prediction made by static data can be inaccurate. We present an approach to tackle this challenge by taking into account software runtime behavior in our reliability prediction. We used a data mining tool, Daikon, to collect likely invariants of the software to capture its states in the runtime. This runtime information is then used to compute the reliability of the software by using our port-based reliability model.