Ms Parfenov.pptx

7/31/2019 Ms Parfenov.pptx

1/19

Sergii Parfenov,

Khmelnitsky National University

E-mail: [email protected]

2012 Sergii Parfenov 1

The method of the intelligent software quality

assessment at the design stage

National Safeware Engineering Network of Centresof Innovative Academia-Industry Handshaking

Project TEMPUS-SAFEGUARD

(Project 158886-TEMPUS-1-2009-1-UK-TEMPUS-JPCR)


2/19

Problem description

International standards (ISO/IEC 9126,

ISO/IEC 15504, ISO/IEC 15939, ISO/IEC

25000) not good for AI software

Problem with human experts:

Person effect

Personalization effect



3/19

AI SW Quality

=

AI SW Realization Quality



4/19

Intelligence SW

Lifecycle


Identification

Conceptual

modeling

Formalization Prototyping

Testing

Experimental

exploitation

Expert

system


5/19

Identification stage

Identify main goal of AI software non-

formal task description

Identify team members and their roles

Identify available resources knowledgesources, time resources, machine

resources, money



6/19

Conceptual

modeling stage

Analysis of problem area

Identification the terms and relationshipsbetween them

Identification the ways to solve the problems

Result model of problem area, which includesmain concepts and relationships



7/19

Formalization stage

Get expert`s knowledge

Knowledge systematization

Knowledge representation

Result knowledge base.



8/19

Prototyping stage



9/19


Identification

Conceptual

modeling


Testing

Experimental

exploitation

Expert

system


10/19


Identification

Conceptual

modeling


Testing

Experimental

exploitation

Expert

system


11/19


Identification

Conceptual

modeling


Testing

Experimental

exploitation

Expert

system


12/19

Team members

evaluation

Education level

Previous experience

Participation in previous versions ofsoftware development

Participation in other projects with sameproblem area



13/19

Competency of expert

(

)=1

=1

Competency of team

(

)=1

=1



14/19


Requirements

Competency Service

Desired

Minimum

Desired

Minimum


15/19


Identification

Conceptual

modeling


Testing

Experimental

exploitation

Expert

system

Competency

requirements


16/19


Identification

Conceptual

modeling


Testing

Experimental

exploitation

Expert

system

Service

requirements


17/19

Problems of

consistency

Redundancy: two rules have the same antecedent, and the conclusions ofone subsume those of the other (e.g., x -> y and x -> y ^ z).

Conflict: two rules have the same antecedent, but their conclusions arecontradictory (e.g., x -> y and x -> !y).

Subsumption: two rules have similar conclusions, but the antecedent ofone subsumes that of the other (e.g., x -> y and x ^ z -> y).

Unnecessary IF rules: two rules have the same conclusions, and their

antecedents contain contradictory clauses, but are otherwise the same

(e.g., x ^ z -> y and x ^ !z -> y).

Circularity: a set of rules forms a cycle.



18/19

Problems of

completeness

Unreferenced attribute values: some values in the set of possiblevalues for an object's attribute are not covered in the set of rules.

Illegal attribute values: a rule refers to an attribute value that is notin the set of legal values.

Unreachable conclusions: the conclusion of a rule should eithermatch a goal or an if condition in some other rule.

Dead-end goals and dead-end IF conditions: either the attributesof a goal must be askable (the system can request information formthe user) or the goal must match the conclusion of a rule. Similarconsiderations apply to the IF conditions in each rule.



19/19

Thank you!!!Sergii Parfenov

[email protected]

Khmelnitsky National University

System Programming Department, http://spr.khnu.km.ua


Documents

Ms Parfenov.pptx