There is a lack of knowledge concerning the low-code autoML (automated machine learning) frameworks that can be used to enrich data for several purposes concerning either data engineering or software engineering. In this paper, 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content. The PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory (Colab) and the BI (business intelligence) tool. Finally, the low-code autoML-augmented data pipeline from raw data to dashboards and low-code apps has been drawn based on the experiments concerned classifications of the “Census Income” dataset. The constructed pipeline preferred the same data to be a ground for different reports, dashboards, and applications. However, the constructed low-code autoML-augmented data pipeline contains changeable building blocks such as libraries and visualisations.
To identify the Open Source maintenance process two well known Open Source projects Apache HTTP server and Mozilla web browser were studied. The Open Source software maintenance process is formal even anyone can submit modifications or defect reports to Open Source software projects. We assume that the Open Source maintenance process is similar to the maintenance process defined by the ISO/IEC. In the case studies, four activities were found similar to the activities of the ISO/IEC Maintenance process. This paper presents the Open Source maintenance process framework. The framework is exemplified with the ISO/IEC Maintenance process framework.
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