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

 Introduction  Software ontology models  Semantic web query methods for software analysis  Experimental evaluation  Conclusion 2 2

 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decisions  Some reasons  Development team changes  Programmers forget what they have done  Undocumented code  Outdated comments  Multiple versions 3 3

 Therefore a code comprehension framework is needed  Mainly composed of two major steps ▪ Converting source code to an internal representation ▪ Performing queries 4 4

 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 5

 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 6

 Three models for different aspects of code 1. Software Ontology Model (SOM) 2. Bug Ontology Model (BOM) 3. Version Ontology Model (VOM)  Connected to related ontologies  DOAP  SIOC  FOAF  WF 7

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 Based on FAMIX (FAMOOS Information Exchange Model)  A programming language independent model for representing object-oriented source code 9

 For specifying the relations between files, releases, and revisions of software projects  Based on the data model of Subversion 10

 Based on the bug-tracking system Bugzilla 11

 Two non-standard extensions of SPARQL  iSPARQL (Imprecise SPARQL)  SPARQL-ML (SPARQL Machine learning) 12

 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

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 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

 Learning phase (building prediction model) 16

 Test phase (making prediction) 17

 4 years ( ) of the proceedings of ICSE Workshop on Mining Software Repositories (MSR) are surveyed  Most actively investigated software analysis tasks are determined 18

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 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

 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

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 Task 2: computing source code metrics  Calculating OO software design metrics 23

 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

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 Number of methods (NOM) and number of attributes (NOA)  As indicators of GOD classes 26

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 Number of bugs (NOB) and number of revisions (NOR) 28

 Task 3: detection of code smells  Task 4: defeat and evolution density  Task 5: bug prediction 29

 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

 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 31