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Deep Learning (DL) based systems are utilized vastly. Developers update the code to fix the bugs in the system. How these code fixing techniques impacts the robustness of these systems has not been clear. Does fixing code increase the robustness? Do they deteriorate the learning capability of the DL based systems? To answer these questions, we studied 321 Stack Overflow posts based on a published dataset. In this study, we built a classification scheme to analyze how bug-fixes changed the robustness of the DL model and found that most of the bug-fixes can increase the robustness. We also found evidence of bug-fixing that decrease the robustness. Our preliminary result suggests that 12.5% and 2.4% of the bug-fixes in Stack Overflow posts caused the increase and the decrease of the robustness of DL models, respectively.

Does fixing bug increase robustness in deep learning?