Making root cause analysis feasible for large code bases: a solution approach for a climate model
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Applications that simulate complex physical processes can be composed of millions of lines of code. For such complex model codes, finding the source of a bug or discrepancy in output (e.g., from a previous version, alternative hardware or supporting software stack) is non-trivial at best. Although there are many tools for program comprehension through debugging or slicing, few (if any) scale to a complicated model such as the Community Earth System Model (CESM^TM). Therefore, to enable developers to trace a problem detected in the model output to its source, we devise a series of techniques to reduce the search space to a tractable size. The first step determines which CESM output variables are most affected by a given bug. To find where these output variables are computed in the code, we construct a directed graph of internal variable relationships. We introduce a form of hybrid program slicing which integrates static, backward slicing with code coverage information to extract a subgraph consisting of relevant variable relationships. Finally, we partition the subgraph into communities, and identify nodes in each community that are central to information flow. After reducing the search space and ranking nodes by centrality, runtime variable sampling becomes feasible. We use examples of simulation output from CESM to illustrate how sampling can be performed as part of an efficient iterative refinement procedure to locate error sources. This process is also effective in complex scenarios such as sensitivity to CPU instructions.
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