Using Semantic Networks and Context in Search for Relevant Software Engineering Artifacts

The discovery of relevant software artifacts can increase software reuse and reduce the cost of software development and maintenance. Furthermore, change requests, which are a leading cause of project failures, can be better classified and handled when all relevant artifacts are available to the decision makers. However, traditional full-text and similarity search techniques often fail to provide the full set of relevant documents because they do not take into consideration existing relationships between software artifacts. We propose a metadata approach with semantic networks which convey such relationships. Our approach reveals additional relevant artifacts that the user might have not been aware of. We also apply contextual information to filter out results unrelated to the user contexts, thus, improving the precision of the search results. Experimental results show that the combination of semantic networks and context significantly improve the precision and recall of the search results.