1 / 31

Samad Paydar Web Technology Lab. Ferdowsi University of Mashhad 10 th August 2011

Samad Paydar Web Technology Lab. Ferdowsi University of Mashhad 10 th August 2011.

uri
Download Presentation

Samad Paydar Web Technology Lab. Ferdowsi University of Mashhad 10 th August 2011

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SamadPaydar Web Technology Lab. Ferdowsi University of Mashhad 10th August 2011 This is a review of the paper:Semantic Web Enabled Software AnalysisJonas Tappolet, Christoph Kiefer, Abraham BernsteinDynamic and Distributed Information Systems, University of Zurich, Switzerland Journal of Web Semantics, 2010

  2. Outline • Introduction • Software ontology models • Semantic web query methods for software analysis • Experimental evaluation • Conclusion 2

  3. Introduction • In order for software to be developed, maintained and evolved • It is required that it is understood • How code works • Developers’ decisions • Some reasons • Development team changes • Programmers forget what they have done • Undocumented code • Outdated comments • Multiple versions 3

  4. Introduction • Therefore a code comprehension framework is needed • Mainly composed of two major steps • Converting source code to an internal representation • Performing queries 4

  5. Introduction • Further • Open source movement • Software complexity • Libraries dependent on other ones • Software that is developed locally is a node in a world-wide network of interlinked source code • Global Call Graph 5

  6. Introduction • Each node in this cloud should exhibit its information in an open, accessible and uniquely identifiable way • Therefore “we propose the usage of semantic technologies such as OWL, RDF and SPARQL as a software comprehension framework with the abilities to be interlinked with other projects” 6

  7. Software ontology model • Three models for different aspects of code • Software Ontology Model (SOM) • Bug Ontology Model (BOM) • Version Ontology Model (VOM) • Connected to related ontologies • DOAP • SIOC • FOAF • WF

  8. SOM: Software Ontology Model • Based on FAMIX (FAMOOS Information Exchange Model) • A programming language independent model for representing object-oriented source code

  9. VOM: Version Ontology Model • For specifying the relations between files, releases, and revisions of software projects • Based on the data model of Subversion

  10. BOM: Bug Ontology Model • Based on the bug-tracking system Bugzilla

  11. Query Methods • Two non-standard extensions of SPARQL • iSPARQL (Imprecise SPARQL) • SPARQL-ML (SPARQL Machine learning)

  12. iSPARQL • Introduces the idea of “virtual triples” • Are not matched against the underlying ontology graph, but used to configure similarity joins • Which pairs of variables should be joined and compared using a certain type of similarity measure

  13. iSPARQL

  14. SPARQL-ML • An extension of SPARQL with knowledge discovery capabilities • A tool for efficient relational data mining on Semantic Web data • Enables the Statistical Relational Learning (SLR) methods such as Relational Probability Trees (RPTs) and Relational Bayesian Classifiers (RBCs)

  15. SPARQL-ML • Learning phase (building prediction model)

  16. SPARQL-ML • Test phase (making prediction)

  17. Experimental Evaluation • 4 years (2004-2007) of the proceedings of ICSE Workshop on Mining Software Repositories (MSR) are surveyed • Most actively investigated software analysis tasks are determined

  18. Experimental Evaluation

  19. Experimental Evaluation • Dataset: 206 releases of the org.eclipse.compare plug-in for Eclipse (average of about 150 Java classes per version) + bug tracking information • Exported to OWL

  20. Experimental Evaluation • Task 1: software evolution analysis • Applicability of iSPARQL to software evolution visualization (i.e. visualization of code changes foe a certain time span) • Compared all the classes of one major release with another major release with different similarity strategies

  21. Experimental Evaluation

  22. Experimental Evaluation • Task 2: computing source code metrics • Calculating OO software design metrics

  23. Experimental Evaluation • Changing methods (CM) and changing classes (CC) • A method that is invoked by many other methods has a higher risk of causing defect in presence of chance

  24. Experimental Evaluation

  25. Experimental Evaluation • Number of methods (NOM) and number of attributes (NOA) • As indicators of GOD classes

  26. Experimental Evaluation

  27. Experimental Evaluation • Number of bugs (NOB) and number of revisions (NOR)

  28. Experimental Evaluation • Task 3: detection of code smells • Task 4: defeat and evolution density • Task 5: bug prediction

  29. Conclusion • A novel approach to analyze software systems using Semantic Web technologies • EvoOnt provides the basis for representing source code and metadata in OWL • This representation reduces analysis tasks to simple queries in SPARQL (or its extensions) • A limitation: loss of some information due to the use of FAMIX-based ontology model

  30. Conclusion • Language constructs like if-else are not modeled • Measurements cannot conducted at the level of statements • One of the greatest impediments towards widespread use of EvoOnt : current lack of high-performance industrial-strength triple-stores & reasoning engines

More Related