1. On the Representation of Intangible Objects such as Software in a Measurement Context Miguel Lopez Valérie Paulus Grégory Seront Simon Alexandre

2. Questions  Are software so intangible that measurement of such abstract beings is impossible?  Is the nature of software so impalpable?  How can we formulate the software representation problem in order to allow measurement?

3. More Questions  Is it relevant to measure software representations in order to make decisions concerning this software?  Are the representations of software relevant and suitable for measurement?

4. Software Models  To measure, we need a representation of the software because of its intangible nature.  The representation of a software is called a model.

5. Different Software Models  In the literature, a lot of languages for representing software can be found.  UML  Graphs  Source Code  ….

6. Software Meta-Model  The concepts and their relations used for building a model are explained in a meta-model.  So, meta-models exist for UML, graphs, and so on.

7. Software Meta-Model Problem  How can we select a relevant and suitable meta-model in measurement context?  Is that always necessary to use meta- models in order to measure a software?  For an external measure, do we have to work with meta-models?

8. Meta-model for Measurement  Measure Definition:  Mapping between an empirical set onto a mathematical set.  A mapping between an empirical set of objects A and a mathematical set of objects B. Set A Set B

9. Set A Problem  Set A is a set of empirical objects.  By definition, tangible objects.  Are software tangible objects?  Is all the software intangible?  Or, just some parts, some attributes?  Some software attributes are intangible.  Coupling, cohesion, complexity,efficiency,…

10. Set A Problem  So, to perceive those attributes, they need representation or model.  Set A is replaced by a set A’ which contains representations of objects of set A.  The transformation A -> A’ transforms intangible objects into tangible objects.

11. Set A Problem  Does the transformation A -> A’ keeps the structure of Set A?  When measuring an element of A’, are we measuring the corresponding element of A? Set A Set A’ Set B

12. Build Set A’  To build the set of representations or models, we need to define the elements used for modeling.  These elements are formalized in a meta-model.

13. Meta-Model  So, the meta-model must be relevant and suitable in regard with:  Measurement Goal  Entity: products, projects, process  Attribute: properties of the entity: efficiency, coupling,…

14. Measurement Method  A measurement method must at least define:  Its goal  The considered entity  The attribute to be measured  In regard with these three elements, a suitable and relevant meta-model must be built or selected.

15. Entity & Meta-model  In this presentation, only the entity software product is considered.  The meta-modelisation aims at modeling the entity.  Several types of software product entities exist:  Requirements Document  Design Document  Source Code  Executable

16. Entity & Meta-model  The meta-model depends on the type of entity.  For example:  Design Document:  Meta-model of UML: class, relations,…  Meta-model of graphs: nodes, edges,…

17. Entity, Meta-model, & Attribute  But, the meta-model also depends on the type of attribute.  In regard with the attribute, some meta- models are more relevant and suitable than others.

18. Entity, Meta-model, & Attribute  For example, measuring the functional size of a software product with Cosmic- FFP:  Entity: Requirements Document  Attribute: Functional size

19. Entity, Meta-model, & Attribute  Meta-Model: Cosmic-FFP

20. How to verify the meta-model?  Two methods:  Expert-based: the relevance and the suitability of the meta-model is evaluated by a set of experts.  Empirical: the relevance and the suitability is of the meta-model is evaluated within a set of entities coming real projects.

21. How to verify the meta-model?  Problems with these two methods:  Expert-based:  Reliability of the experts judgment.  Representativity of the experts sample.  Empirical:  What does mean suitable and relevant in operational terms?  How can we make representative samples?

22. Advantages to explicit the meta-model  An explicit meta-model allows to better understand the entity and the attribute to be measured.  To meta-model an entity like design, source code in terms of a given attribute (coupling,…), it is important to understand and clearly define these terms.

23. Advantages to explicit the meta-model  An explicit meta-model allows to identify early in the measurement method, the limits of measurement:  Cosmic-FFP meta-model does not take into account the complexity of the data movement.  So, the software with complex algorithms are not sized by COSMIC-FFP

24. Meta-model for all types of measurement  Meta-model in external measures are often implicit.  It is not necessary to explicit the meta- model of the entity for external measures.  Response time: which is the meta- model of the entity?

25. Conclusion  Does the entity meta-model a critical activity in software measurement?  Must this activity be explored in details?

26. Questions ??