Team Members Dilip Narayanan Gaurav Jalan Nithya Janarthanan

T1 T2 T3 T4 T5 T6 T7 C1 C2 C3 C5 C7 C9 C4 C6 C8 Identified common usage context and customer needs July 6 th Identified possible approaches after preliminary ground Work - 20 th July Select an approach for further research – 28 th August Model Problem – September 11th Customer Review – September 14th Model Solution – October 2 nd Customer Review – August 24th T8 T9 T10 Customer Review – October 5 th Implementation and Evaluate Model – October 30 th Customer Review – November 2nd T10 Technical Evaluation Report – November 25 th Completed 40 test cases and approved all the suspects - 13 th July Completed 68 test cases - 20 th July Approved Common Test Cases – 27 th July Completed 137 test cases (50%) – 24 th August Approved common Test Cases (50%) – 31 st August Completed 205 Test Cases (75%) – 14 th September Approved 205 common Test Cases (75%) – 21 st September Completed 274 test Cases (75%) – 5 th October Approved common Test Cases (100%) – 12 th October Revised Macro Plan Achieved Milestones Remaining Milestones

ApproachAdvantageDisadvantage Natural Language Processing Allows processing of Natural Language text1.Complexity 2.Time Consuming 3.Still an Emerging field under research Ontology 1. Facilitates building of knowledge based systems 2. Enables building systems which avoid terminology confusion and language ambiguities 3. Gives a structured and formal representation of the domain 1. Not very suitable for dynamically changing systems 2. Requires a dedicated resource to maintain the ontology 3. Not many ontology experts 4. 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 Information Resource Definition Language(RDF) XML End User Hybrid Approach - I

 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

 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 should be relatively easy because of its structure

 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

 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