SHARED TEST CASES

Team MembersDilip NarayananGaurav JalanNithya Janarthanan

T1

T2

T3

T4

T5

T6

T7

C1

C2

C3

C5

C7

C9

C4

C6

C8

Identified common usage context and customer needsJuly 6th

Identified possible approaches after preliminary ground Work - 20th July

Select an approach for further research – 28th August

Model Problem – September 11th

Customer Review – September 14th

Model Solution – October 2nd

Customer Review – August 24th

T8

T9

T10

Customer Review – October 5th

Implementation and Evaluate Model – October 30th

Customer Review – November 2nd

T10

Technical Evaluation Report – November 25th

Completed 40 test cases and approved all the suspects- 13th July

Completed 68 test cases- 20th July

Approved Common Test Cases – 27th July

Completed 137 test cases (50%) – 24th August

Approved common Test Cases (50%) – 31st August

Completed 205 Test Cases (75%) – 14th September

Approved 205 common Test Cases (75%) – 21st September

Completed 274 test Cases (75%) – 5th October

Approved common Test Cases (100%) – 12th October

Revised Macro Plan

Achieved Milestones

Remaining Milestones

Approach Advantage Disadvantage

Natural Language Processing

Allows processing of Natural Language text

1. Complexity2. Time Consuming3. Still an Emerging field under

research

Ontology 1. Facilitates building of knowledge based systems2. Enables building systems which avoid terminology confusion and language ambiguities3. Gives a structured and formal representation of the domain

1. Not very suitable for dynamically changing systems2. Requires a dedicated resource to maintain the ontology 3. Not many ontology experts4. Usability less

Vector Space Model 1. Allows the user to do a search on a repository of documents for a particular search criteria

2. Does not consume as much time as NLP or Ontology

3. There are existing systems for reference

4. Customer will find it easy to use this system

1. Not suitable if it is applied to a system that has different documents with some content but different vocabulary

2. Involves a great deal of work in preparing the Corpus(Document Repository)

Knowledge Base/Domain Model

Developed using Ontology

Search Application built using Vector Space Approach

(JAVA Application)

Domain Informatio

n

Resource Definition

Language(RDF)

XML

End User

Hybrid Approach - I

HYBRID APPROACH IDEAS

OVERVIEW

Identify attributes within each test case (proceedure)

Determine the values of these attributes within each test case

Find candidate matches for a given test case by comparing its attributes and values with those of other test cases

Compute degrees of similarity and confidence Filter candidate matches Domain model assistance in each of the above

computations

DOMAIN MODEL

Mapping of each attribute with its state space

Model domain terms (like door, brake, etc.) and perhaps their relationships.

Relationships may be generalization/ specialization or other kind

Rule model (behavioural and other) Synonymous terms, abbreviations,

context

PARSING V&V

Parsing V&V should be relatively easy because of its structure

PARSING V&V

Limited number of unique ‘states’ System can provide intelligent

suggestions for attributes and values associated with each test case

System operator can review these Need operator assistance only for

unique states

PARSING FAT

Map each attribute with state space Scan each test procedure for these

attributes Compute set of possible attribute values Scan each test procedure for these values Use distance between value and attribute

to figure out values of attributes Account for noise (articles, etc.) Relative word frequencies