As concurrent and distributive applications are becoming more common and debugging such applications is very difficult, practical tools for automatic debugging of concurrent applications are in demand. In previous work, we applied automatic debugging to noise-based testing of concurrent programs. The idea of noise-based testing is to increase the probability of observing the bugs by adding, using instrumentation, timing "noise" to the execution of the program. The technique of finding a small subset of points that causes the bug to manifest can be used as an automatic debugging technique. Previously, we showed that Delta Debugging can be used to pinpoint the bug location on some small programs. In the work reported in this paper, we create and evaluate two algorithms for automatically pinpointing program locations that are in the vicinity of the bugs on a number of industrial programs. We discovered that the Delta Debugging algorithms do not scale due to the non-monotonic nature of the concurrent debugging problem. Instead we decided to try a machine learning feature selection algorithm. The idea is to consider each instrumentation point as a feature, execute the program many times with different instrumentations, and correlate the features (instrumentation points) with the executions in which the bug was revealed. This idea works very well when the bug is very hard to reveal using instrumentation, correlating to the case when a very specific timing window is needed to reveal the bug. However, in the more common case, when the bugs are easy to find using instrumentation points ranked high by the feature selection algorithm is not high enough. We show that for these cases, the important value is not the absolute value of the evaluation of the feature but the derivative of that value along the program execution path. As a number of groups expressed interest in this research, we built an open infrastructure for automatic debugging algorithms for concurrent applications, based on noise injection based concurrent testing using instrumentation. The infrastructure is described in this paper.