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-- Software Engineering for [[Artificial Intelligence]].
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-  id:: 6453df5c-c0c0-4e35-92e7-43ff9ced7b58
-  hl-stamp:: 1683218295711
-- e additional social and environmental factors come into play
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-  id:: 6453df66-63a3-4572-bd75-50197199fed7
-  hl-stamp:: 1683218293105
-- 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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-  hl-page:: 1
-  hl-color:: purple
-  id:: 6453df73-2a0a-413e-9624-48425243e7e0
-- 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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-  hl-page:: 1
-  hl-color:: purple
-  id:: 6453df9e-231f-4e52-9f7d-909f1ff998c9
-  hl-stamp:: 1683218337426
-- 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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-  id:: 6453dfb7-09d8-4648-a74c-7db4128ec340
-  hl-stamp:: 1683218361425
-- Their results indicated poor performance due to socio-environmental factors that impacted the in-vivo experimentation.
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-  hl-page:: 1
-  hl-color:: purple
-  id:: 6453dfe7-0700-4df0-a1de-e4dc4d7528e8
-  hl-stamp:: 1683218410790
-- ecological validity
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-  id:: 6453dff9-debe-4939-b741-9676db58bca8
-- we aim to understand how generalizable the experimental results previously presented would be in a real-case scenario
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-  id:: 6453e003-ae4a-4e13-8ff5-24d8a585e808
-- altering the inputs of the model to simulate an in-vivo experimentation [9].
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-  id:: 6453e019-586e-4f99-b895-ad46389550bc
-- 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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-  id:: 6453e03f-e76a-470f-8a2b-f9980b90a56f
-  hl-stamp:: 1683218498040
-- gainst datasets using validation strategies such as percentage split or cross-fold validation.
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-  hl-page:: 3
-  hl-color:: green
-  id:: 6453e17c-dd74-416b-906c-bc8b414a4c46
-  hl-stamp:: 1683218817227
-- 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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-  hl-page:: 3
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-  id:: 6453e187-9402-4147-8336-728a65cf1822
-- 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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-  hl-page:: 3
-  hl-color:: green
-  id:: 6453e1b4-c5a4-4b55-b68c-a6ce99b491aa
-  hl-stamp:: 1683218871061
-- perspective
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-- researchers 
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-- practitioners
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-  id:: 6453e1e2-501e-4e35-98bc-7ab0e1679946
-- 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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-  id:: 6453e278-5ca7-4a88-8045-0faefbd45f0d
-  hl-stamp:: 1683219065836
-- Once we had established a baseline,
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-  id:: 6453e326-7ee7-44c9-a95d-7223e809e6d3
-- potential behavior of the CNN in a real-world context.
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-  hl-page:: 3
-  hl-color:: purple
-  id:: 6453e386-6eca-4e1a-889a-dff25c89743f
-  hl-stamp:: 1683219336471
-- identify potential issues in the training set data.
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-  hl-page:: 3
-  hl-color:: purple
-  id:: 6453e3c0-8868-490b-befa-edc66fbd9a23
-- different noises on the test set data to simulate a real-world scenario, e.g, rain or fog.
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-  hl-page:: 3
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-  id:: 6453e3cd-e8c4-40dd-a28c-26578f1e411d
-  hl-stamp:: 1683219408594
-- 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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-  hl-page:: 5
-  hl-color:: yellow
-  id:: 6453e437-a1c2-478a-883b-d245eaed4d69
-  hl-stamp:: 1683219520161
-- 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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