Cyber systems depend on a number of parameters to configure the system properly. Incorrect setting of these parameters is known to be responsible for an overwhelming percentage of failures, poor service, and exploitation by hackers for cyberattacks. At the same time, diagnosing misconfigurations is a slow, largely manual process that routinely takes days or weeks because of poor understanding of the relationship between configuration parameters and system's response and interdependencies between the parameters. This project explores the capabilities required to automatically compose tests, run them, collect data, and analyze it to simplify the job of finding the root cause of the problem.<br/><br/>The project will build basic diagnosability capabilities in some commonly used services in the data center including domain name service, routing, and active directory along with suitable access control. The project will also explore how the diagnosis goals can be specified at a high-level and translated into a graph of basic tests connected via input-output relationships and further limited by access permissions, and probing locations. The diagnosis infrastructure will build and run the test, collect data and provide a systematic way of analyzing the data to isolate the problematic hardware/software components as much as possible so as to substantially accelerate the testing and root cause analysis. The infrastructure will also provide capabilities to store, rank, and reuse designed tests to make them more effective over time. <br/><br/>The infrastructure built under this project is expected to substantially reduce the cost, time, and effort for diagnosing the systems via automation of many aspects of the diagnosis. If successful, the approach can be applied to emerging cyber-physical systems where misconfiguration problems are likely to be even more critical in nature. <br/><br/>The project will enhance existing open-source software with basic diagnosability and develop additional software for building and running complex tests, introducing errors, and collecting/analyzing the results. The software and the data will be stored in a local archival system at Temple and will be preserved for at least three years beyond the award period. The data will be linked through the project webpage located at http://www.kkant.net/diagnosis<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.