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- Software Engineering for [[Artificial Intelligence]].
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- e additional social and environmental factors come into play
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- role of input testing as a early indicator of the real-world performance of deep learning models in the context of image recognition
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- Nonetheless, when input testing is applied, the performance of the model drastically drops (reaching ≈30%), possibly highlighting the need for revisiting image recognition [[MODELS]].
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- researchers and practitioners have been focusing on object recognition and image classification, developing a large amount of [[AI]] systems with good performance 
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- Their results indicated poor performance due to socio-environmental factors that impacted the in-vivo experimentation.
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- ecological validity
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- we aim to understand how generalizable the experimental results previously presented would be in a real-case scenario
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- altering the inputs of the model to simulate an in-vivo experimentation [9].
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- discover that the performance of the same model drastically drops when input testing is applied, hence suggesting that (1) the currently available [[MODELS]] would not properly [[WORK]] in practice and (2) input testing may provide insights to machine learning engineers on the generalizability of the model in practice, hence possibly informing their design actions.
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- gainst datasets using validation strategies such as percentage split or cross-fold validation.
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- o the best of our knowledge, there is no study that attempted to provide indications of the ecological validity of the models.
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- although accuracy is one of the most analyzed metrics for understanding the effectiveness of an [[AI]] model, it is not an appropriate measure for unbalanced datasets since it does not distinguish between the numbers of correctly classified examples of different classes, leading to erroneous conclusions [ 21 ].
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- perspective
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- researchers 
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- practitioners
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- application of input testing methods impact the performance of an engineered Convolution Neural Network 
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- Convolutional Neural Network (CNN) 
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- Once we had established a baseline,
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- potential behavior of the CNN in a real-world context.
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- identify potential issues in the training set data.
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- different noises on the test set data to simulate a real-world scenario, e.g, rain or fog.
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- Our replication study corroborates previous findings in the field of image recognition through [[AI]]. The performance of the CNN model is over 90% in terms of accuracy, F-Measure, Recall, and Precision.
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- Our preliminary results indicated that the application of input testing methods lets the performance of the CNN decrease up to 60% with respect to what reported in literature. The overall performance ranged, indeed, between 19% to 33% in terms of precision, recall, F-Measure, and accuracy.
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