Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
gIBIS: a hypertext tool for exploratory policy discussion
ACM Transactions on Information Systems (TOIS)
A probability distribution model for information retrieval
Information Processing and Management: an International Journal - Modeling data, information and knowledge
Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Information retrieval
Information Retrieval
Information Retrieval: Uncertainty and Logics: Advanced Models for the Representation and Retrieval of Information
An Information Retrieval Approach to Concept Location in Source Code
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Feature Identification: A Novel Approach and a Case Study
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
Feature Identification: An Epidemiological Metaphor
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Introduction to Information Retrieval
Introduction to Information Retrieval
Sando: an extensible local code search framework
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Concept location using program dependencies and information retrieval (DepIR)
Information and Software Technology
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When different types of test are performed on software, from unit test, to component test to system test many bugs can be detected and recorded in bug reports. Developers must then fix them one by one. However, an important job before fixing bugs is to locate them in source code. Given a large scale software project with hundreds of bugs, it is a tedious job to locate the problems in source code. Feature location is a solution of this problem. Feature location seeks to identify pieces of source code corresponding to a specific feature, where a feature is defined as a function in software. Since bugs have the same attributes as features, they can be treated as features. In this paper, we provide a technique to achieve feature location. The approach uses a combination of lexical information and structural information. We combine Latent Semantic Indexing with Call Graphs to on a small test case to assist in feature location. Comparing our approach to an approach that uses LSI shows improved accuracy ad effectiveness.