Automatic Detection of Previously-Unseen Application States for Deployment Environment Testing and Analysis
Christian Murphy; Moses Vaughan; Waseem Ilahi; Gail E. Kaiser
- Automatic Detection of Previously-Unseen Application States for Deployment Environment Testing and Analysis
Kaiser, Gail E.
- Technical reports
- Computer Science
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- Columbia University Computer Science Technical Reports
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- Department of Computer Science, Columbia University
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- New York
- For large, complex software systems, it is typically impossible in terms of time and cost to reliably test the application in all possible execution states and configurations before releasing it into production. One proposed way of addressing this problem has been to continue testing and analysis of the application in the field, after it has been deployed. The theory behind this "perpetual testing" approach is that over time, defects will reveal themselves given that multiple instances of the same application may be run globally with different configurations, in different environments, under different patterns of usage, and in different system states. A practical limitation of many automated approaches to deployment environment testing and analysis is the potentially high performance overhead incurred by the necessary instrumentation. However, it may be possible to reduce this overhead by selecting test cases and performing analysis only in previously-unseen application states, thus reducing the number of redundant tests and analyses that are run. Solutions for fault detection, model checking, security testing, and fault localization in deployed software may all benefit from a technique that ignores application states that have already been tested or explored. In this paper, we apply such a technique to a testing methodology called "In Vivo Testing", which conducts tests in deployed applications, and present a solution that ensures that tests are only executed in states that the application has not previously encountered. In addition to discussing our implementation, we present the results of an empirical study that demonstrates its effectiveness, and explain how the new approach can be generalized to assist other automated testing and analysis techniques.
- Computer science
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