Samad PaydarWeb Technology Lab.Ferdowsi University of Mashhad10th August 2011

Introduction Software ontology modelsSemantic web query methods for

software analysis Experimental evaluationConclusion

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Some reasons Development team changes Programmers forget what they have done Undocumented code Outdated comments Multiple versions

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Therefore a code comprehension framework is needed Mainly composed of two major steps▪ Converting source code to an internal

representation▪ Performing queries

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Further Open source movement Software complexity Libraries dependent on other onesSoftware that is developed locally is a

node in a world-wide network of interlinked source code

Global Call Graph

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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”

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Three models for different aspects of code1. Software Ontology Model (SOM)2. Bug Ontology Model (BOM)3. Version Ontology Model (VOM)

Connected to related ontologies DOAP SIOC FOAF WF

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Based on FAMIX (FAMOOS Information Exchange Model)

A programming language independent model for representing object-oriented source code

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For specifying the relations between files, releases, and revisions of software projects

Based on the data model of Subversion

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Based on the bug-tracking system Bugzilla

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

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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

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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)

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Learning phase (building prediction model)

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Test phase (making prediction)

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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

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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

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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

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Task 2: computing source code metrics

Calculating OO software design metrics

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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

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Number of methods (NOM) and number of attributes (NOA)

As indicators of GOD classes

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

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Task 3: detection of code smellsTask 4: defeat and evolution density Task 5: bug prediction

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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

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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

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