Paper Info
Title
Mining Attribute Lifecycle to Predict Faults and Incompleteness in Database Applications.
Abstract
In a database application, for each attribute, a value is created initially via insertion. Then, the value can be referenced or updated via selection and updating respectively. Eventually, when the record is deleted, the values of the attributes are also deleted. These occurrences of events are associated with the states to constitute the attribute lifecycle. Our empirical studies discover that faults and incompleteness in database applications are highly associated with the attribute lifecycle. Consequently, we propose a novel approach to automatically extract the attribute lifecycle out of a database application from its source code through inter-procedural static program analysis. Data mining methods are applied to predict faults and incompleteness in database applications. Experiments on PHP systems give evidence to support applicability and accuracy of the proposed method.
Year
DOI
Venue
2013
10.1109/APSEC.2013.39
Asia-Pacific Software Engineering Conference
Keywords
Field
DocType
Fault prediction,incompleteness prediction,data mining,attribute lifecycle
Static program analysis,Data mining,Source code,Computer science,Support vector machine,Database application,Empirical research,Database
Conference
ISSN
Citations 
PageRank 
1530-1362
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Kaiping Liu1135.33
Hee Beng Kuan Tan248945.05