Pinset: A DSL for extracting datasets from models for data mining-based quality analysis
Proceedings - 2018 International Conference on the Quality of Information and Communications Technology, QUATIC 2018, Page: 83-91
2018
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Data mining techniques have been successfully applied to software quality analysis and assurance, including quality of modeling artefacts. Before such techniques can be used, though, data under analysis commonly need to be formatted into two-dimensional tables. This constraint is imposed by data mining algorithms, which typically require a collection of records as input for their computations. The process of extracting data from the corresponding sources and formatting them properly can become error-prone and cumbersome. In the case of models, this process is mostly carried out through scripts written in a model management language, such as EOL or ATL. To improve this situation, we present Pinset, a domain-specific language devised for the extraction of tabular datasets from software models. Pinset offers a tailored syntax and built-in facilities for common activities in dataset extraction. For evaluation, Pinset has been used on UML class diagrams to calculate metrics that can be employed as input for several fault-prediction algorithms. The use of Pinset for this calculations led to more compact and high-level specifications when compared to equivalent scripts written in generic model management languages.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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