Extracting facts from open source software

Ferenć, Rudolf; Siket, István; Gyimóthy, Tibor

doi:10.1109/icsm.2004.1357790

Cited by 42 publications

(27 citation statements)

References 11 publications

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“…We developed tools that automatically create corpora for MS Visual Studio projects [28] and Eclipse projects [29]. In addition, we also created corpus builder for large C++ projects, using srcML [30] and Columbus [31]. 2.…”

Section: Concept Location Using Concept Latticesmentioning

confidence: 99%

Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

Poshyvanyk

Marcus

2007

15th IEEE International Conference on Program Comprehension (ICPC '07)

166

101

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Section: Concept Location Using Concept Latticesmentioning

confidence: 99%

Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

Poshyvanyk

Marcus

2007

15th IEEE International Conference on Program Comprehension (ICPC '07)

166

101

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“…We developed tools that automatically create corpora for MS Visual Studio C++ projects [Poshyvanyk et al 2005] and Eclipse Java projects [Poshyvanyk et al 2006a;Savage et al 2010]. In addition, we also created a corpus builder for large C++ projects, using srcML [Maletic et al 2002] and Columbus [Ferenc et al 2004]. 2.…”

Section: Concept Location Using Concept Latticesmentioning

confidence: 99%

Concept location using formal concept analysis and information retrieval

Poshyvanyk

Gethers

Marcus

2012

ACM Trans. Softw. Eng. Methodol.

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________________________________________________________________________The paper addresses the problem of concept location in source code by proposing an approach that combines Formal Concept Analysis and Information Retrieval. In the proposed approach, Latent Semantic Indexing, an advanced Information Retrieval approach, is used to map textual descriptions of software features or bug reports to relevant parts of the source code, presented as a ranked list of source code elements. Given the ranked list, the approach selects the most relevant attributes from the best ranked documents, clusters the results, and presents them as a concept lattice, generated using Formal Concept Analysis.The approach is evaluated through a large case study on concept location in the source code on six opensource systems, using several hundred features and bugs. The empirical study focuses on the analysis of various configurations of the generated concept lattices and the results indicate that our approach is effective in organizing different concepts and their relationships present in the subset of the search results. In consequence, the proposed concept location method has been shown to outperform a standalone Information Retrieval based concept location technique by reducing the number of irrelevant search results across all the systems and lattice configurations evaluated, potentially reducing the programmers' effort during software maintenance tasks involving concept location.

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“…• AnalyzeManager -the module controls the Columbus analyzer tools [33,34,36] which we used to compute low-level source code metrics and other analysis results.…”

Section: The First Case Studymentioning

confidence: 99%

Advances in software product quality measurement and its applications in software evolution

Hegedűs

2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Extracting facts from open source software

Cited by 42 publications

References 11 publications

Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

Concept location using formal concept analysis and information retrieval

Advances in software product quality measurement and its applications in software evolution

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