Authors:
Sarah A. Dahab
;
Juan Jose Hernandez Porras
and
Stephane Maag
Affiliation:
Telecom SudParis, France
Keyword(s):
Software Metrics, Formal Measurement, Software Measurement, Measurement Plan, Formal Software Measurement, SVM, Big Data.
Related
Ontology
Subjects/Areas/Topics:
Cross-Feeding between Data and Software Engineering
;
Model-Driven Engineering
;
Software Engineering
;
Software Engineering Methods and Techniques
;
Software Metrics
;
Software Project Management
Abstract:
The software measurement is an integral part of the software engineering process. With the rise of the software system and their complexity distributed through diverse development phases, the software measurement process has to deal with more management and performance constraints. In fact, the current software measurement process is fixed and manually planned at the beginning of the project and has to manage a huge amount of data resulting from the complexity of the software. Thereby, measuring software becomes costly and heavy. In addition, the implementation of the measures is dependent on the developer and reduce the scalability, maintainability and the interoperability of the measurement process. It becomes expert-dependent and thus more costly. In order to tackle these difficulties, first, we propose in this paper a formal software measurement implementation model based on the standard measurement specification SMM. Then, a software measurement plan suggestion framework based o
n a learning-based automated analysis.
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