Automated Change Impact Analysis for Deep Learning Programs - Dataset

Deep learning is the core technology underlying many Artificial Intelligence (AI) applications. Maintainingdeep learning software programs presents unique challenges due to their complex, interdependent structures.Traditional change impact analysis methods, while effective for conventional software, fall short whenthey are applied to deep learning programs due to the intricate interdependencies within neural networks,particularly those related to the unique architectural layers and hyperparameters. This paper presents anovel approach to change impact analysis which is specifically designed for deep learning programs. Weintroduce a comprehensive taxonomy that systematically categorizes changes in deep learning architectures,hyperparameters, and regularization techniques. Our approach leverages this taxonomy to map changesautomatically, thereby enabling structured and efficient impact analysis in complex deep learning programs.To predict the potential impact of initial changes, our framework incorporates co-change pattern mining toidentify and generate co-change rules, capturing dependencies based on historical co-change patterns. Wealso designed domain-expert rules derived from deep learning literature and best practices. Our evaluationresults demonstrate that our co-change mining approach effectively balances recall and precision, with theintegration of co-change patterns and domain-expert rules offering additional complementary benefits.