Development, distribution and use of open source software comprise a market of data (source code, bug reports, documentation, number of downloads, etc.) from projects, developers and users. This large amount of data makes it difficult for people involved to make sense of implicit links between software projects, e.g., dependencies, patterns, licenses. This context raises the question of what techniques and mechanisms can be used to help users and developers to link related pieces of information across software projects. In this paper, we propose a framework for a marketplace enhanced using linked open data (LOD) technology for linking software artifacts within projects as well as across software projects. The marketplace provides the infrastructure for collecting and aggregating software engineering data as well as developing services for mining, statistics, analytics and visualization of software data. Based on cross-linking software artifacts and projects, the marketplace enables developers and users to understand the individual value of components, their relationship to bigger software systems. Improved understanding creates new business opportunities for software companies: users will be better able to analyze and compare projects, developers can increase the visibility of their products, hosts may offer plug-ins and services over the data to paying customers.

1. Laboratory of Advanced
Information Systems
Fernando Silva Parreiras

2. • 1 PhD Student (new!)
• ~8 MSc Students
• 3 Bachelor Students
• 1 Administrative Staff

3. Pasteur's Quadrant

4. Towards a Marketplace of Open
Source Software Data
Fernando Silva Parreiras

5. Current Open Source Software
Market
Poor Search!
Low
Visibility!

6. Future Market
Architecture?

7. Architecture
Visualization?
Manipulate
Applications Query
…
Expressiveness?
Mapping
Common Vocabulary
Extraction
Generic
Extractors?
Software Artifacts in Various Formats

8. Extraction

9. Introduction to
Metamodeling
Language
Designer
Language
User

10. Example: Process Modeling
Ecore Metametamodel
conformsTo
Ecore-based
Metamodel
Language
Designer conformsTo
Model
Language
User

11. Generic Extrator: OWLizer

12. OWLizer Example
Non-structured
Information?

13. Mapping and
Inferencing

14. Expressiveness of
Description Logics
● Description Logics (DLs) are logics designed to represent
and reason on structured knowledge
– The domain of interest is structured into (TBox):
– concepts, which correspond to classes, and denote sets of
individuals
– roles, which correspond to associations, and denote binary
relations on individuals
● The knowledge is asserted through so-called assertions
(ABox)
● They provide formal semantics
● DLs provide the foundations for standard ontology
languages, like OWL2

15. Applications
● Consistency of the diagram:
– Can the classes be populated?
● Classification to identify compatible
refinements.
● Equivalence among classes to discover
redundancy.

16. EvoOnt

17. Ontology Matching
● Study of techniques for reconciling multiple resources
on the web. Two steps:
– match and determine alignments
– merging and transforming. Performance!
● Matchings use multiple criteria: name of entities,
structure (relations between entities, cardinality),
background knowledge, e.g., existing ontologies or
wordnet. Techniques can be string-based or rely on
linguistic resources.
● More info on http://ontologymatching.org/

18. Querying

19. Semantic Project Explorer
● Integrated with Eclipse
● Sends Artifacts to the Cloud
● Query Answering
● Plug-in of the
TwoUse Toolkit

20. Semantically query UML models

21. SPE Component Diagram

22. SPE Context Menu

23. Querying Answering
Vizualization!

24. Marketplace for Financial
Information
Generalization?

25. Conclusions
We are setting up an Infrastructure based
on generic extractors and expressifull
common vocabularies for data
marketplaces.

26. Books

27. Papers

28. Tack!
fumec.br/lais