Advancing candidate link generation for requirements tracing: the study of methods

By Jane Huffman Hayes, Alex Dekhtyar, Senthil Karthikeyan Sundaram. IEEE Transactions on Software Engineering, 2006

524 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/e95dab0a11982386badec7df1298d66c6646c4a9

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Citations in the corpus

Semantically Enhanced Software Traceability Using Deep Learning Techniques (2017, methodology): “This dataset is considerably larger than those used in most previous studies on requirements traceability [5], [44], [34] and the task of creating links across such a large dataset represents a challenging industrial-strength tracing problem.”
Detection of Duplicate Defect Reports Using Natural Language Processing (2007, background): “Hayes, Dekhtyar and Sundaram [3][4] use NLP to support tracking of requirements to designs.”
Software traceability: trends and future directions (2014, methodology): “used the Vector Space Model (VSM) IR algorithm in conjunction with a thesaurus to esablish trace links [44].”
Research Directions in Requirements Engineering (2007, methodology): “Of particular interest are tools and techniques to ease, and partially automate, the task of identifying and documenting traceability links among requirements artifacts and between requirements and downstream artifacts [30, 81, 118, 146, 151].”
Is it a bug or an enhancement?: a text-based approach to classify change requests (2008, methodology): “This later family of techniques is not well suited for ofi-line classiflcation but has been successfully applied in traceability recovery [ 16 ].”
A survey of traceability in requirements engineering and model-driven development (2010, unknown): “Different research groups have tried to compare results achievable with the different models: [13,82,112,117], to enhance them using different sources of additional information [40, 202] or to combine the different approaches [48].”
Recovering traceability links in software artifact management systems using information retrieval methods (2007, background): “In particular, several researchers have recently applied Information Retrieval (IR) techniques [Baeza-Yates and Ribeiro-Neto 1999; Deerwester et al. 1990; Harman 1992] to the problem of recovering traceability links between artifacts of different types [Antoniol et al. 2000a; 2002; Cleland-Huang ”
On the relative value of cross-company and within-company data for defect prediction (2009, background): “Hayes, Dekhtyar, & Sundaram call this fraction selectivity and offer an extensive discussion of the merits of this measure (Hayes et al. 2006).”
Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval (2007, background): “…types identification [35], clone detection [36], traceability link recovery between the source code and an external documentation [30], [37], conceptual coupling [38] and cohesion [39], clustering [40], requirements tracing [41], establishing traceability links between artifacts [42], and so forth.”
Semantic clustering: Identifying topics in source code (2007, result): “compare the results of several information retrieval techniques in recovering links between document and source code to the results of a senior engineer [44].”