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SHARED TEST CASES
Team MembersDilip NarayananGaurav JalanNithya Janarthanan
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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
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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)
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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
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HYBRID APPROACH IDEAS
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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
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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
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PARSING V&V
Parsing V&V should be relatively easy because of its structure
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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
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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