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Titlebook: Requirements Engineering: Foundation for Software Quality; 29th International W Alessio Ferrari,Birgit Penzenstadler Conference proceedings

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11#
發(fā)表于 2025-3-23 12:57:13 | 只看該作者
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發(fā)表于 2025-3-23 15:23:41 | 只看該作者
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發(fā)表于 2025-3-23 19:53:25 | 只看該作者
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發(fā)表于 2025-3-23 22:25:49 | 只看該作者
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
15#
發(fā)表于 2025-3-24 05:45:55 | 只看該作者
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
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發(fā)表于 2025-3-24 10:15:26 | 只看該作者
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發(fā)表于 2025-3-24 14:15:40 | 只看該作者
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發(fā)表于 2025-3-24 18:07:04 | 只看該作者
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
19#
發(fā)表于 2025-3-24 21:04:02 | 只看該作者
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
20#
發(fā)表于 2025-3-25 00:27:01 | 只看該作者
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
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