Recovering documentation-to-source-code traceability links using latent semantic indexing

By Andrian Marcus, Jonathan I. Maletic. 25th International Conference on Software Engineering, 2003. Proceedings., 2003

793 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/deaace1da763ed0cb4004625665e58c43e2f540e

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Citations in the corpus

Advancing candidate link generation for requirements tracing: the study of methods (2006, methodology): “Most research in the area of requirements tracing has focused on models of requirements tracing [40] or has looked at recall and precision to assess the accuracy of the applied linking algorithms [3], [31].”
Classifying Software Changes: Clean or Buggy? (2008, methodology): “These projects have goals other than predicting bugs, including classifying software into broad functional categories [19], clustering related software project documents [20], [28], and associating the source code with other artifacts such as design documents [29].”
How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms (2013, background): “…to support software engineering tasks: feature location [4], change impact analysis [5], bug localization [6], clone detection [7], traceability link recovery [8], [9], expert developer recommendation [10], code measurement [11], [12], artifact summarization [13], and many others [14], [15], [16].”
Autofolding for Source Code Summarization (2014, neutral): “Marcus et al. [26, 27] used Latent Semantic Indexing (LSI)[6] for identifying traceability links and concept location in source code.”
Recovering traceability links in software artifact management systems using information retrieval methods (2007, methodology): “This is the reason why information retrieval based traceability recovery methods are semi-automatic methods and require the interaction of the software engineer [Antoniol et al. 2002; Huffman Hayes et al. 2003; Marcus and Maletic 2003].”
An information retrieval approach to concept location in source code (2004, methodology): “For example, in [ 23 ] parts of documentation are automatically mapped to elements of the source code.”
A Hidden Markov Model to detect coded information islands in free text (2013, methodology): “For example, recovering the traceability between source code and software documentation stored in email has been tackled with methods based on vector of terms [6], [7].”
Collaboration in Software Engineering: A Roadmap (2007, methodology): “XLinkit performs automated consistency checks across a project [41], while [ 42 ] describes an approach for automatically inferring documentation to source code links using information retrieval techniques.”
Retrieval from software libraries for bug localization: a comparative study of generic and composite text models (2011, methodology): “It is therefore not surprising that IR based methods are being applied in other areas of software maintenance and program comprehension [19] [6] [21] [9] [13] [18].”
On the Use of Automated Text Summarization Techniques for Summarizing Source Code (2010, methodology): “They have also been used to index software corpora in a variety of comprehension applications [13-16].”
Semantic clustering: Identifying topics in source code (2007, methodology): “The same authors also used LSI to recover links between external documentation and source code by querying the source code with queries from documentation [40].”
IssueExec: A Test-Driven Approach for Localizing Software Engineering Issues (2026, unknown): “However, as noted in classic studies, IR-based methods remain largely static proxies for relevance and continue to struggle with the "vocabulary mismatch" problem where informal issue descriptions and formal code syntax lack sufficient overlap [33].”