CHOIR
发表于 2025-3-23 12:57:13
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细菌等
发表于 2025-3-23 15:23:41
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Bureaucracy
发表于 2025-3-23 19:53:25
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勉励
发表于 2025-3-23 22:25:49
Using Language Models for Enhancing the Completeness of Natural-Language Requirementstively discovering omissions in requirements and the level of noise in the predictions. Our second contribution is devising a machine learning-based filter that post-processes predictions made by BERT to further reduce noise. We empirically evaluate our solution over 40 requirements specifications d
Compass
发表于 2025-3-24 05:45:55
Requirement or Not, That is the Question: A Case from the Railway Industryhow that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower sampl
不能平静
发表于 2025-3-24 10:15:26
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谷物
发表于 2025-3-24 14:15:40
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漂亮才会豪华
发表于 2025-3-24 18:07:04
Requirements Classification Using FastText and BETO in Spanish Documentsataset, but BETO outperformed other classifiers on prediction performance in a dataset with different origins. .: Our evaluation provides a quantitative analysis of the classification performance of fastTest and BETO in comparison with ML/DL algorithms, the external validity of trained models on ano
逃避现实
发表于 2025-3-24 21:04:02
Exploring Requirements for Software that Learns: A Research Previewunique characteristics of software requirements that are specific to ML components. To this end, we collect and examine requirements from both academic and industrial sources. . To the best of our knowledge, this is the first work that presents real-life, industrial patterns of requirements for ML c
Blanch
发表于 2025-3-25 00:27:01
An Investigation of Challenges Encountered When Specifying Training Data and Runtime Monitors for Saovides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to over