Paper Info
Title
Quality evaluation and improvement framework for database schemas - using defect taxonomies
Abstract
Just like any software artefact, database schemas can (or should) be evaluated against quality criteria such as understandability, expressiveness, maintainability and evolvability. Most quality evaluation approaches rely on global metrics counting simple pattern instances in schemas. Recently, we have developed a new approach based on the identification of semantic classes of definite patterns. The members of a class are proved to be semantically equivalent (through the use of semantics preserving transformations) but are assigned different quality scores according to each criteria. In this paper, we explore in more detail the concept of bad pattern by proposing an intuitive taxonomy of defective patterns together with, for each of them, a better alternative. We identify four main classes of defects, namely complex constructs, redundant constructs, foreign constructs and irregular constructs. For each of them, we develop some representative examples and we discuss ways of improvement against three quality criteria: simplicity, expressiveness and evolvability. This taxonomy makes it possible to apply the framework to quality assessment and improvement in a simple and intuitive way.
Year
DOI
Venue
2011
10.1007/978-3-642-21640-4_40
CA(i)SE
Keywords
Field
DocType
intuitive taxonomy,simple pattern instance,database schema,definite pattern,bad pattern,better alternative,quality criterion,quality assessment,defect taxonomy,quality evaluation approach,improvement framework,different quality score,defective pattern
Data mining,Computer science,Evolvability,Database schema,Software,Semantic equivalence,Schema (psychology),Semantics,Maintainability,Expressivity
Conference
Citations 
PageRank 
References 
2
0.37
12
Authors
2
Name
Order
Citations
PageRank
Jonathan Lemaitre181.15
Jean-Luc Hainaut2901254.54