Semantic vs. structural resemblance of classes
ACM SIGMOD Record
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
How well do experienced software developers predict software change?
Journal of Systems and Software
Placing search in context: the concept revisited
Proceedings of the 10th international conference on World Wide Web
Software Change Impact Analysis
Software Change Impact Analysis
Status Report: Software Reusability
IEEE Software
Semantic and schematic similarities between database objects: a context-based approach
The VLDB Journal — The International Journal on Very Large Data Bases
Constraining Model-Based Reasoning Using Contexts
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Out of context: computer systems that adapt to, and learn from, context
IBM Systems Journal
Y!Q: contextual search at the point of inspiration
Proceedings of the 14th ACM international conference on Information and knowledge management
Can LSI help Reconstructing Requirements Traceability in Design and Test?
CSMR '06 Proceedings of the Conference on Software Maintenance and Reengineering
Module connection analysis: a tool for scheduling software debugging activities
AFIPS '72 (Fall, part I) Proceedings of the December 5-7, 1972, fall joint computer conference, part I
An operational definition of context
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
An Empirical Evaluation of Similarity Coefficients for Binary Valued Data
International Journal of Data Warehousing and Mining
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The discovery of software artifacts (files, documents, and datasets) relevant to a change request, can increase software reuse and reduce the cost of software development and maintenance. However, traditional search techniques often fail to provide the relevant documents because they do not consider relationships between software artifacts. We propose the creation of Semantic Networks which convey such relationships and assist in automatically discovering not only the requested artifacts based on a user query, but additional relevant ones that the user may not be aware of. Subsequently, we increase the accuracy of the returned artifacts by applying appropriate contexts. Experimental results show that this approach leads to better recall and precision compared to existing full-text search approaches.