Software traceability with topic modeling
By Hazeline U. Asuncion, Arthur U. Asuncion, Richard N. Taylor. 2010 ACM/IEEE 32nd International Conference on Software Engineering, 2010
359 citations recorded by the source corpus.
Subfields: Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/4550fcf64869132a1852fd421081520ae4341a33
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Jane Cleland-Huang
- Hamed Jelodar
- Yongli Wang
- Denys Poshyvanyk
- Jane Huffman Hayes
- Jin L.C. Guo
- Jinghui Cheng
- Patrick Mäder
In Software
- Denys Poshyvanyk
- Jane Cleland-Huang
- Jane Huffman Hayes
- Hazel Victoria Campbell
- Rocco Oliveto
- Abram Hindle
- Andrea De Lucia
- Apurva Shah
Citations in the corpus
- Topic Modeling Using Latent Dirichlet allocation (2021, methodology): “worked for the trace the software development activities by implementing software traceability using topic modeling [5].”
- Latent Dirichlet Allocation (2003, methodology): “[2] applied LDA on textual documentation and source-code modules and used the topic document matrix to indicate traceability between the two.”
- Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “[89] 2007 Mining software and extracted concepts from code SourceForge and apache (1,555 projects) [46] 2010 Identifying latent topics and find their relationships in source code Thirteen open source software systems [6] 2010 Generating traceability links ArchStudio software project [19] 2012 Find r”
- Analysis of user comments: An approach for software requirements evolution (2013, neutral): “In software engineering, researchers have applied topic modeling to various aspects of software projects, including traceability to follow the life of requirements [5], source code to help support understanding and find reusable components [6], and documentation to explore the evolving stream of top”
- How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms (2013, methodology): “labeling [13], aspect identification [14], expert identification [25], software traceability [9], [26], test case prioritization [27], and evolution analysis [28], [16].”
- Software traceability: trends and future directions (2014, methodology): “Latent Semantic Indexing [19] and Latent Dirichlet Allocation (LDA) [22, 6] were applied in an attempt to understand the semantics context of elements in the artifacts, thus leading to higher quality links.”
- 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.”