Rashedi, DanielDanielRashediSchupp, SibylleSibylleSchupp2024-10-242024-10-242024-064th IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2024979-8-3503-7435-3979-8-3503-7434-6https://hdl.handle.net/11420/49859Fault localization in neural networks develops methods to improve a model's accuracy, resilience, and performance. Improving a model's accuracy without extensive gradient descent training loops has not been a goal, yet. In this paper, we propose a novel model repair approach identifying suspicious neurons to be selected for modifications of greater impact upon a single iteration avoiding extensive training processes with the gradient descent method yielding potential overfitting. Our approach uses Spectrum-Based Fault Localization to guide the repair process. Spectrum-Based Fault Localization is an established method that assigns software code a suspiciousness value indicating a likelihood for fault presence depending on test suite outcomes and the associated code's execution. We employ hit-spectrum analysis to assign neurons a suspiciousness score. Our algorithm selects especially suspicious neurons for repair by reducing their activation values, while increasing the activation values of less suspicious neurons. Experiments conducted on convolutional layer and dense layer image classification models confirm that, overall, our approach leads to better mean accuracies. In 2 scenarios, the repair approach has led to small accuracy improvements by increasing the corresponding models' mean accuracies by 0.68 and 0.46 percentage points respectively. Remarkably, our repair approach has also led to significant accuracy improvements by increasing a specific model's accuracy by 15.73 percentage points. In conclusion, our method provides a promising alternative to additional training epochs running the risk of overfitting or depending on larger databases for the training process.enhit-spectrum analysismodel pruningsuspiciousness scoresuspicious neuron detectionComputer Science, Information and General Works::004: Computer SciencesConference Paper10.1109/SEAI62072.2024.10674058Conference Paper