2 research outputs found
Benchmarking Top-K Keyword and Top-K Document Processing with TK and TKD
Top-k keyword and top-k document extraction are very popular text analysis
techniques. Top-k keywords and documents are often computed on-the-fly, but
they exploit weighted vocabularies that are costly to build. To compare
competing weighting schemes and database implementations, benchmarking is
customary. To the best of our knowledge, no benchmark currently addresses these
problems. Hence, in this paper, we present TK, a top-k keywords and
documents benchmark, and its decision support-oriented evolution
TKD. Both benchmarks feature a real tweet dataset and queries
with various complexities and selectivities. They help evaluate weighting
schemes and database implementations in terms of computing performance. To
illustrate our bench-marks' relevance and genericity, we successfully ran
performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand,
and on different relational (Oracle, PostgreSQL) and document-oriented
(MongoDB) database implementations, on the other hand
Graph-Based Keyphrase Extraction for Software Traceability in Source Code and Documentation Mapping
Natural Language Processing (NLP) forms the basis of several computational tasks. However, when applied to the software system’s, NLP provides several irrelevant features and the noise gets mixed up while extracting features. As the scale of software system’s increases, different metrics are needed to assess these systems. Diagrammatic and visual representation of the SE projects code forms an essential component of Source Code Analysis (SCA). These SE projects cannot be analyzed by traditional source code analysis methods nor can they be analyzed by traditional diagrammatic representation. Hence, there is a need to modify the traditional approaches in lieu of changing environments to reduce learning gap for the developers and traceability engineers. The traditional approaches fall short in addressing specific metrics in terms of document similarity and graph dependency approaches. In terms of source code analysis, the graph dependency graph can be used for finding the relevant key-terms and keyphrases as they occur not just intra-document but also inter-document. In this work, a similarity measure based on context is proposed which can be employed to find a traceability link between the source code metrics and API documents present in a package. Probabilistic graph-based keyphrase extraction approach is used for searching across the different project files.