Towards a Marketplace of Open Source Software Data

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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.

Technology

Laboratory of Advanced
Information Systems
Fernando Silva Parreiras

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

Towards a Marketplace of Open
Source Software Data
Fernando Silva Parreiras

Current Open Source Software
Market
Poor Search!

Low
Visibility!

Architecture
Visualization?

Manipulate
Applications Query
…

Expressiveness?

Mapping

Common Vocabulary
Extraction

Generic
Extractors?
Software Artifacts in Various Formats

Introduction to
Metamodeling

Language
Designer

Language
User

Example: Process Modeling

Ecore Metametamodel

conformsTo

Ecore-based
Metamodel

Language
Designer conformsTo

Model

Language
User

OWLizer Example

Non-structured
Information?

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

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

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/

Semantic Project Explorer

● Integrated with Eclipse
● Sends Artifacts to the Cloud
● Query Answering
● Plug-in of the
TwoUse Toolkit

Marketplace for Financial
Information

Generalization?

Conclusions

We are setting up an Infrastructure based

on generic extractors and expressifull
common vocabularies for data
marketplaces.

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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject. We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup. Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved. The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring. The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise. By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.

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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?

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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