On Presuppositions
in Requirements
Lin Ma
Bashar Nuseibeh, Paul Piwek, Anne De Roeck, Alistair Willis
Department of Comput...
Research Aim and summary
 To discover tacit knowledge in requirements text by
tracking linguistic presuppositions.
 Spec...
Tacit Knowledge
 “We know more than we can tell” – Polanyi, 1963
 Janik (1988) argues that the term “tacit knowledge” is...
Presupposition
Presuppositions are background information or
assumptions that can be taken for granted.
Examples:
 The Ki...
Presupposition triggers
 Presupposition is believed to be signalled by certain
types of syntactical structure, which are ...
Presupposition triggers - cont.
The trigger types include:
 Definite descriptions, e.g. the device, its accessibility;
 ...
Our preliminary case study
 We studied a 20-page
requirements document for
integrated circuit chip design.
 Our study wa...
Examples found in document
 Noun phrases
Sentence: “Accessibility in the experimental hall is required for changing the
p...
Which presuppositions are “dangerous”?
 “Accessibility in the experimental hall is required for
changing the piggy board ...
Nocuously Tacit Knowledge
 “danger” is in the eye of the beholder (the reader).
 One way to determine this is by conduct...
Tracking presuppositions
 What we know:
 By using natural language processing techniques, definite
descriptions can easi...
Related work
 Automatically tracking presupposition by NLP
K. Wiemer-Hastings and P. Wiemer-Hastings, “DP: a detector for...
Future Work (Lin’s PhD research agenda!)
 Case study on behaviour and linguistic attributes of
presuppositions in more re...
Conclusion
 Our preliminary work has shown that tacit knowledge
can be extracted by tracking presuppositions in
requireme...
Thank you.
 Email:
 {L.Ma, B.Nuseibeh, P.Piwek, A.Deroeck, A.G.Willis}@open.ac.uk
 MaTREx Project:
 http://crc.open.ac...
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09 On Presuppositions in Requirements

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09 On Presuppositions in Requirements

  1. 1. On Presuppositions in Requirements Lin Ma Bashar Nuseibeh, Paul Piwek, Anne De Roeck, Alistair Willis Department of Computing The Open University, U.K. Acknowledgement: MaTREx Project (EPSRC Grant Number: EP/F069227/1)
  2. 2. Research Aim and summary  To discover tacit knowledge in requirements text by tracking linguistic presuppositions.  Specific focus:  “Nocuous tacit knowledge”  Determined by human judgement  Wider context:  MaTREx project on use of NLP techniques, such as ambiguity analysis and presuppositions
  3. 3. Tacit Knowledge  “We know more than we can tell” – Polanyi, 1963  Janik (1988) argues that the term “tacit knowledge” is used at two ways:  firstly, following Polanyi, tacit knowledge is knowledge inexpressible in words, and it is acquired by familiarity or practice such as smells and sounds;  secondly, tacit knowledge at a shallow level is knowledge not yet put into words such as craft knowledge and presuppositions.  We adopt Janek’s second perspective:  Tacit knowledge is knowledge that knowers know and could have articulated but omit doing so for some reason, perhaps because they simply were not asked.
  4. 4. Presupposition Presuppositions are background information or assumptions that can be taken for granted. Examples:  The King of France is Bald. Presupposition: There is king of France.  John knows that Susan is coming to the party. Presupposition : Susan is coming to the party.  Richard managed to pass the exam. Presupposition : Richard tried to pass the exam.
  5. 5. Presupposition triggers  Presupposition is believed to be signalled by certain types of syntactical structure, which are called presupposition triggers. Triggers: 1. Definite description: The King of France 2. Factive verb: know 3. Implicative verb: manage
  6. 6. Presupposition triggers - cont. The trigger types include:  Definite descriptions, e.g. the device, its accessibility;  Factive verbs, e.g. know, reveal;  Implicative verbs, e.g. avoid, intend;  Change of state verbs, e.g. continue, stop;  Clefts – it + be + noun + subordinate clause;  Stressed constituents – words in italic in texts;  Counter factual conditionals – what would be the case is something were true;  Expressions of repetition, e.g. also, too;  Temporal relations, e.g. since, after;  Comparisons, e.g. less/larger than
  7. 7. Our preliminary case study  We studied a 20-page requirements document for integrated circuit chip design.  Our study was mostly manual, although we automated the identification of noun phrases.  We recorded the kinds of presuppositions that appeared, and found the majority triggered by definite descriptions.
  8. 8. Examples found in document  Noun phrases Sentence: “Accessibility in the experimental hall is required for changing the piggy board where the device will be mounted.” Presuppositions: There is a piggy board. There is a device.  Factive verb Sentence: “…tests revealed that redundancy to Single Event Upsets is required.” Presupposition: Redundancy to Single Event Upsets is required.  Implicative verb Sentence: “…chambers shall avoid that two CMA share the same gas volume…” Presupposition: Two CMA may share the same gas volume.
  9. 9. Which presuppositions are “dangerous”?  “Accessibility in the experimental hall is required for changing the piggy board where the device will be mounted. ” A new device or “the piggy board”?  “...will have various interfaces for different groups of users. While the appearance of the user interface may be similar, the functionality of each user interface will be distinct...” “The user interface” refers to “various interfaces” or “each user interface”?
  10. 10. Nocuously Tacit Knowledge  “danger” is in the eye of the beholder (the reader).  One way to determine this is by conducting empirical studies to elicit human judgements (ala Chantree et al @ RE’06).  As with nocuous ambiguity, nocuous presuppositions are those that signal tacit knowledge who tacitness may have a negative impact on the reading interpretation of the requirements.
  11. 11. Tracking presuppositions  What we know:  By using natural language processing techniques, definite descriptions can easily be found.  Where we are:  Currently, there are only a few representative example words or constructions of presupposition triggers. They can only be found by hand.  What we need to do:  Detect more presupposition triggers based on natural language processing techniques, and try to relate these to significant tacit knowledge.
  12. 12. Related work  Automatically tracking presupposition by NLP K. Wiemer-Hastings and P. Wiemer-Hastings, “DP: a detector for presuppositions in survey questions,” Proceedings of the sixth conference on Applied natural language processing, 2000, pp. 90–96.D. Clausen and C.D. Manning, “Presupposed Content and Entailments in Natural Language Inference,” ACL-IJCNLP 2009, p. 70.  Nothing in RE?
  13. 13. Future Work (Lin’s PhD research agenda!)  Case study on behaviour and linguistic attributes of presuppositions in more requirements documents with the help of NLP.  Discovery of nocuous presuppositions by collecting human judgments from stakeholders  Building a system to automatically highlight presuppositions that have negative impact on communication in requirement documents.
  14. 14. Conclusion  Our preliminary work has shown that tacit knowledge can be extracted by tracking presuppositions in requirements documents.  With the help of NLP techniques and the involvement of human judgements, tracking presuppositions in requirements can make some elements of tacit knowledge explicit.
  15. 15. Thank you.  Email:  {L.Ma, B.Nuseibeh, P.Piwek, A.Deroeck, A.G.Willis}@open.ac.uk  MaTREx Project:  http://crc.open.ac.uk/matrex  http://www.comp.lancs.ac.uk/research/projects/matrex/  http://gow.epsrc.ac.uk/ViewPanelROL.aspx?PanelId=4612&RankingListId=6037

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