Software Black Box: An Alternative Mechanism for Failure Analysis
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Active learning for automatic classification of software behavior
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Application of Maximum Entropy Principle to Software Failure Prediction
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
On the economics of requirements-based test case prioritization
EDSER '05 Proceedings of the seventh international workshop on Economics-driven software engineering research
Trace anomalies as precursors of field failures: an empirical study
Empirical Software Engineering
Characterizing the differences between pre- and post- release versions of software
Proceedings of the 33rd International Conference on Software Engineering
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Assessing the reliability of a software system has always been an elusive target. A program may work well for a number of years and suddenly become quite unreliable if its mission is changed. When executing a set of fault free modules, it will certainly execute indefinitely without any likelihood of failure. A program may execute a sequence of fault prone modules and still not fail. The faults may lie in a region of the code that is not likely to be expressed during the execution of that module. Thus, the reliability of the system is determined by the software is currently doing. Future reliability predictions will be bound in their precision by the degree of understanding of future execution patterns. We investigate a model that represents the program sequential execution of modules as a stochastic process. By analyzing the transitions between modules and their failure counts, we may learn exactly where the system is fragile and under which execution patterns a certain level of reliability can be guaranteed.