A Conceptual Dependency Graph Based Keyword Extraction Model for Source Code to API Documentation Mapping
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The document presents a conceptual dependency graph based model for mapping source code to API documentation. It proposes using a weighted graph to model dependencies between code elements and filter candidate sets for contextual similarity computation. The methodology involves preprocessing source code and documents, building a dependency graph, and computing contextual similarity. Results show the proposed method achieves higher accuracy than existing techniques. Future work includes applying the approach to larger open source projects.
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![Proposed
Methodology
Phase 1: Source Code and API documents Pre-processing
Step 1: Read project source codes S.
Step 2: Read project API documents D.
Step 3: for each code Ci in S[]
Do
Parse source code Ci with methods M and Fields F.
Mi=ExtractMethods(Ci)
Fi=ExtractFields(Ci)
Mapping (Mi , Fi) to Ci
C1 (M1,F1)
C2 (M2,F2)
… …..
Cn (Mn,Fn)
done
Step 4: // Remove the duplicate methods and fields in each class
For each code Ci
Do
i i j
i i j
M Pr ob(M M / C);i j
F Pr ob(F F / C);i j
If( Mi!=0 AND Fi!=0)
Then
Remove Mi in Ci or Cj
Remove Fi in Ci or Cj
End if
Done](https://image.slidesharecdn.com/module-1-200213084013/85/A-Conceptual-Dependency-Graph-Based-Keyword-Extraction-Model-for-Source-Code-to-API-Documentation-Mapping-14-320.jpg)
![References
Amir Hossein Rasekh, Amir Hossein Arshia, “Mining and discovery of hidden relationships between
software source codes and related textual Documents”, Digital Scholarship in the Humanities ,
Published by Oxford University Press on behalf of EADH., doi:10.1093/llc/fqx052,
Chun Yong Chong , Sai Peck Lee , Automatic Clustering Constraints Derivation from Object-Oriented
Software Using Weighted Complex Network with Graph Theory Analysis, The Journal of Systems &
Software (2017), doi: 10.1016/j.jss.2017.08.017
Anh Tuan Nguyen, Tien N. Nguyen, Graph-based Statistical Language Model for Code, 2015
IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE), 2015, Florence,
Italy, Page 858-862.
Lars Ackermann, Bernhard Volz, “model[NL]generation: Natural Language Model Extraction”,
DCM’13: Proceedings of the 2013 workshop on Domain Specific Modeling: ACM New York,USA.
F Meziane, N. Athanasakis, S. Ananiadou, "Generating Natural Lanuage Specifications from UML
Class diagrams", Requirement Engineering Journal, 13(1):1-18, Springer-Verlag, London.
Fabian Friedrich, Jan Mendling, Frank Puhlmann, “Process Model Generation from Natural
Language Text”, In Advanced Information Systems Engineering, Eds. Lecture Notes in Computer
Science. Springer Berlin Heidelberg, Berlin, Heidelberg, 482-496.](https://image.slidesharecdn.com/module-1-200213084013/85/A-Conceptual-Dependency-Graph-Based-Keyword-Extraction-Model-for-Source-Code-to-API-Documentation-Mapping-17-320.jpg)
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- 1. A Conceptual Dependency Graph Based Keyword Extraction Model for Source Code to API Documentation Mapping Prepared By Nakul Sharma Under Guidance of Dr. Prasanth Yalla Professor, Department of Computer Science and Engineering. Koneru Laxmiah Education Foundation. Vijayawada, Andhra Pradesh India
- 2. Table of Contents Introduction Background Mathematical Foundations Genesis of Research Proposed Methodology Results and Discussion Future Scope and Conclusion References
- 3. Introduction Traditional key feature extraction techniques • use terms or sentences from the project source codes to form a unique code structure. Almost all traditional document key phrase extraction techniques • represent a document collection as the phrase or sentence matrix in which each row denotes the phrase or sentence-id and corresponding column represents the frequency
- 4. Introduction (Continued) Main problem with the existing systems is that they ignore the context based textual information. Contextual Information hold more relevance especially when undertaking any software change which effects not just the current phase of project but also the previous phases and the next phases. Source Code Analysis also aids in checking the effect of change on code. In the proposed model, a weighted graph dependency model is used to filter the candidate sets among the vertices for contextual similarity computation.
- 5. Background • Source Code Analysis • Text Mining • Document Representation • Clustering • NLP/CL
- 6. Mathematical Framework • Centrality Measures • Document Clustering • Document Metrics • Source Code Metrics
- 7. Genesis of Research Work Done in Text Mining and its related fields Research conducted by various authors
- 8. Related Work Sr. No. Name of Authors Work Done in Brief 1 S. Mohammadi et.al new approach is presented to extract the knowledge of dependency between artifacts in the source code. 2 V. U. Gómez, et.al U. Gómez, et.al, proposed a semantic model on the visually characterizing source code modifications 3 S. L. Abebe et.al S. L. Abebe et.al has introduced a new extraction scheme that is sufficiently effective to extract domain concepts from the source code. 4 S. Bajracharya, et al, S. Bajracharya, et al, developed a new SCA framework to collect and analyze open source code on a large scale 5 A. S. Yumaganov A. S. Yumaganov proposed to compare different search models for similarity with limitations on the source code
- 9. Related Work Sr. No. Name of Authors Work Done in Brief 1 Dimitriou et.al A. Dimitriou et.al, introduced a new keyword search of top-k-size on tree structured data 2 W. Ding W. Ding proposed a review of software documentation process knowledge-based techniques 3 Hussain et. al. Hussain et.al proposed a new software design pattern classification and selection scheme. 4 Ibrahim et. al. Ibrahim et.al presented a scientometric re- ranking technique 5 L. H. Lee et. al. L. H. Lee, et.al, used Bayesian text classification to introduce high relevance keyword extraction process
- 10. Related Work (Related to Software Metrics) Sr. No. Name of Authors Work Done in Brief 1 Dimitriou et.al A. Dimitriou et.al, introduced a new keyword search of top-k- size on tree structured data 2 W. Ding W. Ding proposed a review of software documentation process knowledge-based techniques 3 Hussain et. al. Hussain et.al proposed a new software design pattern classification and selection scheme. 4 Ibrahim et. al. Ibrahim et.al presented a scientometric re-ranking technique 5 L. H. Lee et. al. L. H. Lee, et.al, used Bayesian text classification to introduce high relevance keyword extraction process
- 11. Observations on Related Work Large open source projects not considered in SCA systems and tools developed Existing system also do not take into consideration the contextual keyphrases in providing traceability links. The current work proposes an alternative contextual dependency graph based software metrics in form of contextual similarity.
- 12. Proposed Methodology Figure 1: Module-1 Project source codes Class parsing Project API documentation Text pre-processing Filtered API documents Code dependency Graph Proposed Contextual dependency graph similarity
- 13. Pre-processing of API Documents
- 14. Proposed Methodology Phase 1: Source Code and API documents Pre-processing Step 1: Read project source codes S. Step 2: Read project API documents D. Step 3: for each code Ci in S[] Do Parse source code Ci with methods M and Fields F. Mi=ExtractMethods(Ci) Fi=ExtractFields(Ci) Mapping (Mi , Fi) to Ci C1 (M1,F1) C2 (M2,F2) … ….. Cn (Mn,Fn) done Step 4: // Remove the duplicate methods and fields in each class For each code Ci Do i i j i i j M Pr ob(M M / C);i j F Pr ob(F F / C);i j If( Mi!=0 AND Fi!=0) Then Remove Mi in Ci or Cj Remove Fi in Ci or Cj End if Done
- 15. Results and Discussion Project LDA ONTOSE Proposed Method Apache Pluto 0.846 0.835 0.9436 Apache Commons Collections 0.736 0.753 0.879 JEuclid 0.794 0.825 0.962 JFreeChart 0.773 0.874 0.921 Kyro 0.874 0.915 0.948
- 16. Future Scope and Conclusion The current paper proposed a novel approach to find the relationship between the source code to API documents using the contextual dependency graph. A two pronged approach is used in the proposed method. The project source code is scanned for the relevant metrics. On the other hand, from the API documentation, necessary information is extracted. Here, the dependency graph is used to compute the contextual similarity computation between the source code metrics and its API documents
- 17. References Amir Hossein Rasekh, Amir Hossein Arshia, “Mining and discovery of hidden relationships between software source codes and related textual Documents”, Digital Scholarship in the Humanities , Published by Oxford University Press on behalf of EADH., doi:10.1093/llc/fqx052, Chun Yong Chong , Sai Peck Lee , Automatic Clustering Constraints Derivation from Object-Oriented Software Using Weighted Complex Network with Graph Theory Analysis, The Journal of Systems & Software (2017), doi: 10.1016/j.jss.2017.08.017 Anh Tuan Nguyen, Tien N. Nguyen, Graph-based Statistical Language Model for Code, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE), 2015, Florence, Italy, Page 858-862. Lars Ackermann, Bernhard Volz, “model[NL]generation: Natural Language Model Extraction”, DCM’13: Proceedings of the 2013 workshop on Domain Specific Modeling: ACM New York,USA. F Meziane, N. Athanasakis, S. Ananiadou, "Generating Natural Lanuage Specifications from UML Class diagrams", Requirement Engineering Journal, 13(1):1-18, Springer-Verlag, London. Fabian Friedrich, Jan Mendling, Frank Puhlmann, “Process Model Generation from Natural Language Text”, In Advanced Information Systems Engineering, Eds. Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg, 482-496.