1 / 14

AUTONOMOUS REQUIREMENTS SPECIFICATION PROCESSING USING NATURAL LANGUAGE PROCESSING

AUTONOMOUS REQUIREMENTS SPECIFICATION PROCESSING USING NATURAL LANGUAGE PROCESSING. - Vivek Punjabi. Overview. Motivation Background Proposed system design Architecture Parsing System Term Management System Conclusion. Motivation. Requirement artifacts Knowledge, experience, tools

Download Presentation

AUTONOMOUS REQUIREMENTS SPECIFICATION PROCESSING USING NATURAL LANGUAGE PROCESSING

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. AUTONOMOUS REQUIREMENTS SPECIFICATION PROCESSING USINGNATURAL LANGUAGE PROCESSING - Vivek Punjabi

  2. Overview • Motivation • Background • Proposed system design • Architecture • Parsing System • Term Management System • Conclusion

  3. Motivation • Requirement artifacts • Knowledge, experience, tools • Requirements Specification Document • Only knowledge • Missing important information • Consequences • 40 – 60 % software defects due to errors in requirement stage • Cost of correcting defects >> Cost to represent requirements correctly • Risk of misinterpretation

  4. Background • Use of formal languages for design • Still depends on knowledge • Less research due to ambiguity of natural language requirements • Semi-automated generation of ER diagrams for database modelling • Requirements supplemented by glossary – a-priori knowledge • Pre-processing and application specific

  5. System Design Figure 1: Assisted Requirements Analysis Process

  6. System Architecture Unique Noun terms tokens

  7. Syntactic Parsing • Syntactic parser based on a chart parsing technique with a context-free grammar (CFG) that is augmented with constraints. • Current prototype system • 32000 entries in Dictionary • 79 rules • An example of context free rule: • S (i.e. LHS)  NP VP (i.e. RHS) • well-formedness constraint (number-agreement NP VP) • “He see a car in the park” • Current limitations – compound noun terms, disambiguation module

  8. Syntactic Parsing (Contd.) • “A system requires entry of patient’s information” • (S (NP (DET “A”) (NOUN “system”)) • (VP (VERB “requires”) • (NP (NP (NOUN “entry”)) (PP (OF “of”) • (NP (POSSADJ “patient’s”) (NOUN “information”)))))) • “Dunedin Podiatry requires an information system that allows entry and retrieval of patient's details and their medical histories.” • “Dunedin Podiatry”, “information system”, “entry”, “retrieval”, “(patient’s) details”, and “(their medical) histories”

  9. Term Extraction by a Syntactic Parser

  10. Term Management System • Filter Entity • Manual option • Create classes • Entity, Attribute, Function • Manage • Knowledge base • (OBJECT (:TYPE FUNCTION) (:VALUE “entry”)) • (OBJECT (:TYPE ENTITY) (:VALUE “patient”)) • (OBJECT (:TYPE ATTRIBUTE) (:VALUE “age”))

  11. Future Work • Add disambiguation module • Compound noun analysis and Proper noun processing • anaphoric resolution and semantic interpretation of terms • enhance the process of term extraction and enable term relationship identification • “patient’s medical histories” • One-many relationship between “patient” and “medical histories”

  12. Conclusion • Utilize NLP to assist systems analysts in selecting and verifying objects and relationships of relevance to any given project • Save burden of analysis for system analyst • The toolset will be intelligent enough to automatically parse, select and relate the objects of interest from specification documents • Knowledge base helps in automatic generation of relevant design artifacts – object models, data models, etc.

  13. Questions?

  14. Thank You

More Related