Extraction of domain concepts from the source code

Abebe, Surafel Lemma; Tonella, Paolo

doi:10.1016/j.scico.2014.09.012

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…It integrates the text-based information along with visual representation on several metrics. S. L. Abebe et.al has introduced a new extraction scheme that is sufficiently effective to extract domain concepts from the source code [3]. Program understanding can be defined as a process whereby domain concepts are mapped to the code elements.…”

Section: Related Workmentioning

confidence: 99%

A Conceptual Dependency Graph Based Keyword Extraction Model for Source Code to API Documentation Mapping

Sharma¹,

Yalla²

2019

IJRTE

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Natural language processing on software systems usually contain high dimensional noisy and irrelevant features which lead to inaccurate and poor contextual similarity between the project source code and its API documentation. Most of the traditional source code analysis models are independent of finding and extracting the relevant features for contextual similarity. As the size of the project source code and its related API documentation increases, these models incorporate the contextual similarity between the source code and API documentation for code analysis. One of the best solutions for this problem is finding the essential features using the source code dependency graph. In this paper, the dependency graph is used to compute the contextual similarity computation between the source code metrics and its API documents. A novel contextual similarity measure is used to find the relationship between the project source code metrics to the API documents. Proposed model is evaluated on different project source codes and API documents in terms of pre-processing, context similarity and runtime. Experimental results show that the proposed model has high computational efficiency compared to the existing models on the large size datasets

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Section: Related Workmentioning

confidence: 99%

A Conceptual Dependency Graph Based Keyword Extraction Model for Source Code to API Documentation Mapping

Sharma¹,

Yalla²

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Based on a feature model (a notion used in product-line engineering and software variability modelling), the proposed system tries to match the description with relevant features in order to recommends the reuse of existing source code from open-source repositories. [12] proposes to use natural language parsing to automatically extract an ontology from source code. Starting from a lightweight ontology (a.k.a, concept map), the authors develop a more formal ontology based on axioms.…”

Section: Related Workmentioning

confidence: 99%

Discovering Program Topoi via Hierarchical Agglomerative Clustering

et al. 2018

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“…Based on a feature model (a notion used in product-line engineering and software variability modeling), the proposed system tries to match the description with relevant features in order to recommends the reuse of existing source code from open-source repositories. (Abebe and Tonella 2015) proposes natural language parsing to automatically extract an ontology from source code. Starting from a lightweight ontology (a.k.a, concept map), the authors develop a more formal ontology based on axioms.…”

Section: Related Workmentioning

confidence: 99%

Discovering Program Topoi Through Clustering

Ieva

Gotlieb

Kaci

et al. 2018

AAAI

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Understanding source code of large open-source software projects is very challenging when there is only little documentation. New developers face the task of classifying a huge number of files and functions without any help. This paper documents a novel approach to this problem, called FEAT, that automatically extracts topoi from source code by using hierarchical agglomerative clustering. Program topoi summarize the main capabilities of a software system by presenting to developers clustered lists of functions together with an index of their relevant words. The clustering method used in FEAT exploits a new hybrid distance which combines both textual and structural elements automatically extracted from source code and comments. The experimental evaluation of FEAT shows that this approach is suitable to understand open-source software projects of size approaching 2,000 functions and 150 files, which opens the door for its deployment in the open-source community.

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Extraction of domain concepts from the source code

Cited by 16 publications

References 6 publications

A Conceptual Dependency Graph Based Keyword Extraction Model for Source Code to API Documentation Mapping

A Conceptual Dependency Graph Based Keyword Extraction Model for Source Code to API Documentation Mapping

Discovering Program Topoi via Hierarchical Agglomerative Clustering

Discovering Program Topoi Through Clustering

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