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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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