Debugging in a well-known, important concept while developing software - discussed below are some topics that arise when one talks about debugging in scientific environments.
Defects in software (e.g. bugs) can take many forms. Defects are generally described as causing unexpected or unintended behavior including outright crashes, incorrect or invalid results, missing or disabled functionality and unexpected time and/or space performance. <!-- time and/or space performance. -->
The first most important step in debugging is to develop a reproducer. That is, the recipe by which the defective behavior can be reliably observed. It is common for a defect to manifest only under certain software configurations which includes such things as the operating system, compiler, third party libraries and various user-specific controls such as preferences, input gestures and commands and input data. In complex situations involving large collections of interacting software components, reproducers can often be burdensome to develop. It is best when users and developers alike share this burden. This is unnavoidable when the defect manifests in configurations that are inaccessible to the software developers. Perhaps the most challenging of all defects to reproduce are those that manifest only at large scale parallelism.
Once a reproducer is available, the process of finding the cause can involve various creative strategies, including the use of debugging tools such as gdb or TotalView (which require the software to have been compiled with debugging symbols included), performance assesement tools such as Valgrind or gprof, intrusive code modifications (e.g. printf), and even simply eye-balling code to identify possible candidate code paths that could lead to the observed defective behavior.
When UNclassified codes manifest defects in Classified environments, the debugging may be done in the Classified environment but the correction still needs to be implemented in the UNclassified environment. Defects arising out of unintended time and/or space performance can be particularly difficult to diagnose and correct. For example, an O(n^2) algorithm deep in the bowels of a large and complex application with many dependencies may manifest noticeable performance behavior only for extremely large n, not typically encountered in routine testing. When its practical to do so, it is good practice for developers to add the reproducer recipe to the software's routine testing so as to ensure the defect won't creep back into the software in later versions. This practice is typically called regression testing.