Discovering Problem-Solving Knowledge in Business Emails
Traceability in Software Design Using Computer Mediated Communication
Rauscher Francois, Matta Nada and Atifi Hassan
Institute ICD/Tech-CICO, University of Technology of Troyes,
12 rue Marie Curie, BP. 2060, 10010 Troyes Cedex, France
Keywords: Knowledge Management, Traceability, Project Memory, Pragmatics, Problem Solving.
Abstract: This article addresses issues related to problem solving knowledge detection in business emails. The
objective is to discover if there are traces of knowledge left in emails and how to detect it. Finding patterns
and structure of interest in emails corpora is a difficult task, but it is a necessity for companies in order to
keep traces and learn lessons from previous projects. Especially in software development project that can be
very costly and time consuming. In this paper, we present a general method for discovering problem solving
requests in professional emails using mixed techniques from pragmatics analysis and knowledge
engineering. This study takes place in the project memory work aiming at the traceability and structuration
of knowledge in daily business environment.
1 INTRODUCTION
Computer mediated communication is ubiquitous in
Software design projects. Email is used for project
coordination, but also for design, implementation
and tests. Especially with currents agile development
methods, it is very common to interact through
computer meditated communication like email,
instant messaging and other collaborative tools in
order to express functional needs, notify of issues
and take appropriate decisions.
The knowledge produced in this types of
interactions is often buried inside email boxes, hence
being volatile and not easily reused. Project Memory
(Matta et al, 2000) aims at describing organizational
and cooperative dimensions of knowledge created
during the life-cycle of a project. Our objective is to
discover if we can structure and extract knowledge
from professional emails in order to trace some
phases and decisions inside a project memory. In
particular problem solving of a project. Altough
some works have been done on emails related to
topics classification or spam detection (Jindal,
2007), it’s rarely used in the context of knowledge
management. The use of email is often confined to
coordination and planning tasks (Wasiak et al,
2011), (Matta et al, 2010) or in case of legals issues.
Enron case turned into a rich corpus for research
about communication and social networks (Diesner,
2005).
The paper is organised as follows. Principles of
problem solving, software design methods, and
Project Memory are presented in Section 2, followed
by some related works on emails and pragmatics
analysis in Section 3. In Section 4, we propose an
hybrid method using Knowledge engineering, NLP
and pragmatic linguistic to discover meaningful
features and context from emails. Then evaluations
and results are reported in Section 5.
2 OVERVIEW
Problem solving plays a central role in software
design projects. At first in the initial analysis that
leads to specification documents, and then during
the life-cycle of the project with the implementation,
debugging and testing. This is especially true if the
development follow an agile method, with several
roundtrips from design to delivery.
Project Memory focus on keeping “project
definition, activities, history and results“ (Tourtier,
1995). Problem Solving is an essential part of design
rationale as it tackles with problem definition,
suggestions, and decision.
186
Francois R., Matta N. and Hassan A..
Discovering Problem-Solving Knowledge in Business Emails - Traceability in Software Design Using Computer Mediated Communication.
DOI: 10.5220/0005073601860193
In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2014), pages 186-193
ISBN: 978-989-758-050-5
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Face to face meetings are commonly used in
office to manage projects and do collaborative work,
but other mediated communications usages are
increasing like phone, emails or instant messaging.
However, information in emails is volatile,
unstructured and distributed among users email
boxes, making it difficult to trace and keep for
corporate memory.
2.1 Problem Solving
Theory of Human Problem Solving was developed
from the work of Newell and Simon (Newell et al,
1972) and provided the basis of much problem
solving research. According to Hardin (Hardin,
2002), “Any problem has at least three components:
givens, goal and operation”. This general definition
from problem solving theory is bringing keys
elements into light:
 Givens: information and facts presenting
context;
 Goal: desired end state;
 Operations : actions to be performed to reach
end state;
In our present study, related to software
development, we will focus more on givens and
goal, i.e. the “problem recognition” part, the
operations being part of the solution. When
designing software complex problem solving arise
more easily, because the tasks are abstracts and often
not well structured as opposed as designing a real
world artifact.
Problem solving in professional project aims at
transforming knowledge into business value (Gray,
2001). This usually involve two types of knowledge:
declarative (about facts, events, and objects) and
procedural (knowing how to do things).
2.2 Software Development Process
Methodologies in software development evolved
quickly in the last two decade from classic waterfall
model to Extreme Programming and Agile methods
(Beck et al, 2001).
Agile development is iterative and incremental
with continuous delivery. As a side effect roundtrips
between product-owner (contractor) and product-
manager (developer) are more frequent, leading to
increased communication and collaborative work.
Typically a software design cycle in agile is divided
into sprints, where the product-owner meet the
product-manager (developer) and validates recent
features, raises issues and express new needs.
Problem Solving sequences happen on weekly
(sometime daily) basis and implies all the actors of
the project, not only the development team. With the
new means of communication and project
management methods like Kanban (Ladas, 2009)
this occurs frequently through computer mediated
exchanges.
2.3 Project Memory
A Project Memory is similar to a corporate memory
of an organization at the scale of a project. As such,
it contains knowledge used and produced during the
project realization. The following elements and
relations between them constitute the Project
Memory:
 Organization: teams, members, tasks, roles,
competencies, etc.;
 Resources and constraints: rules, methods,
directives, time, budget, etc.
 Project realization: problem solving (problem
definition, suggestions, and decision), solution
evaluation (arguments, criteria), etc.;
 Project goals and objectives
The problem solving is part of the project design
rationale memory (Matta et al, 2000). Some technics
from knowledge engineering like REX (Malvache et
al, 1993) or MASK (Matta et al, 2002), aims at
knowledge capitalization, but others are more
oriented on the traceability of the design rationale. A
clear representation of the context and design
rationale can be found in (Bekhti et al, 2003) and is
presented in Figure 1
Figure 1: Problem solving and design rationale in context.
3 RELATED WORKS
Email has been analyzed by linguists like Baron
(Baron, 1998) because of its hybrid nature. It
DiscoveringProblem-SolvingKnowledgeinBusinessEmails-TraceabilityinSoftwareDesignUsingComputerMediated
Communication
187

borrows the form of written language, but allows
some kind of dialog due to its rapidity and the
structure of the exchanges. Differences lays in the
facts that it’s asynchronous and that the emitter and
receiver, not seeing each other are missing
nonverbal elements of communication. Herring
(Herring, 2004) shows that email is close enough to
dialog in order to apply computer mediated
discourse analysis methods to it.
Statistical Textmining methods are not well
adapted to emails analysis due to its short content.
Pragmatic analysis is a relevant approach for that but
the choice of the speech acts coding scheme is
crucial.
This justify the work of Carvalho (Carvalho et
al, 2006) that describes an NLP technic based on N-
grams to classify “email speech acts”, such as
"propose a meeting" or "commit to a task". This
custom coding scheme is based on the idea of
Searle’s speech acts theory but created from the
analysis of an academic emails corpora
(CSpace).Similarly we can find in De Felice works
on email (De Felice et al, 2012) another grid of
analysis coming from linguistic pragmatics and
psychology, the Verbal Response Mode (VRM)
from Stiles (Stiles, 1992). VRM is attractive because
of double coding form/intent of speech acts. But it’s
more suited for spoken discourse analysis and De
Felice switch later to a custom scheme when
analyzing Help Desk emails.
Another approach was taken by Kalia (Kalia et
al, 2013) working on Enron corpus, he used two
types of speech acts from Searle (Searle, 1969):
directive and promissive in order to identify tasks
and commitments.
Finally in our study we will use pragmatic
analysis on emails with a custom speech act grid
using context knowledge in a similar way as in
(Matta et al, 2010) to identify problem solving
interactions. In this latter work, pragmatics was used
on discussion forums to identify criteria that help
analyzing messages of coordination in design
project.
4 COLLECTING PROBLEM
SOLVING KNOWLEDGE
First we assemble and sanitize data, then in
consecutive steps we apply treatments on data to
identify relevant threads and finally analyze
messages.
4.1 Project Data
We consider in our study that the initial data would
be:
 Email corpus;
 Actors role, skills and competences;
 project phasing and specification documents;
These elements are likely to be found in numerous
software design projects. They belong to the project
context, which is important to represent in a project
memory.
4.2 Corpus Preparation
Business emails collected from a project in their raw
form are very redundant. In case of multiple replies
or forward, several parts of the messages are
repeated (e.g. quoted reply content). This occurs
typically in long threads, mediated equivalents to
spoken conversations, which are especially
interesting for our study. Some preprocessing steps
have to be performed in order to prepare messages
and threads for analysis. We chose a deliberately
simple method similar to Carvalho (Carvalho et al,
2004). The steps involved are:
 Remove all previous message text from reply;
 Keep previous message in case of first reply
of a thread or forwarded email cause it carries
context information;
 Remove signatures and disclaimers when
possible (identity of sender and receivers are
kept in email metadata);
This leaves us with a corpus of messages and
threads without too much duplicated or useless
information. For some treatments, the granularity at
message level is not sufficient, and it’s relevant to
split the messages into sentences. Here again, we use
a standard approach and split according to
punctuation and paragraph signs.
4.3 Messages Analysis
We proceed in 3 steps to narrow our research for
problem solving elements
4.3.1 Frequency
The first type of analysis is frequency based. We just
count the total number of message per day,
regardless of the quality of receivers (i.e. not making
a difference between to, cc and bcc). This overall
analysis will allow us to detect if some spikes of
KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing
188

activity do occur. More frequent or longer threads of
conversation are a good indicators of actors need to
collaborate, very often due to an issue. We analyze
only messages that have more than 4 answers; we
believe that knowledge can be extracted based on
interaction.
We align this frequency analysis to the phases of
the project, in order to detect boundaries and zones
that are meaningful for choices, delivery problem or
new needs.
4.3.2 Topics
We decide to make a very straightforward and
knowledge oriented classification of messages and
sentences. This steps is necessary in order to assert
to deal with messages concerning pure software
functionality knowledge and to filter project
coordination emails.
Our approach is to create a keywords dictionary
for the main topics of the project. This dictionary
can be built from the following sources:
 Project phasing and specifications documents ;
 an expert;
 domain ontology if available;
As in project memory context, we choose not to
rely on statistical NLP clustering like in (Cselle et al,
2007) but to use existing context knowledge. This
dictionary is voluntarily kept simple and have the
form:
Topic1: keywords1, keywords2... keywordsn.
Using this dictionary we classify messages into
weighted topics vector (same technic is applied to
sentences for a fine granularity analysis). In order to
do that we use a cosine similarity based algorithm.
We compute a Lucene (Gospodnetic, 2004) ranking
between our message and each topics. This give us a
topics matrix T where T
ij
represents weight of topic j
in message i.
As a side remark, keywords chosen in topics
shall not overlap too much to keep the results
significant.
4.3.3 Problem Solving and Pragmatics
The filtering technics used in Section 4.3.1 and 4.3.2
allow us to detect some threads of interest were
problem solving is likely to occur. At this point, we
switch to a sentence level analysis within one thread.
We have information metadata about
sender/receivers, their roles and skills, timestamps of
messages, and the project topics they are dealing
with. We then apply pragmatics technics in order to
find problem solving sentences.
As first work, we focus our speech act analysis
on a part of problem solving by identifying request.
In doing so we are restricting our search on finding
the given and goal parts of problem-solving theory
as seen in Section 2.1.
In further work, we will use subsequent
messages in same thread to discover informations
about operation and eventual solutions. Our analysis
is based first on pragmatics in order to characterize
request speech act, and that by identifying request
verbs and forms.
Traditionally there is a debate between linguists
and pragmatics points of view about question and
request. A question is more a request for information
(ask for a saying in (Benveniste, 1966)) and a
pragmatic request according to Searle is more a
request for action. In this study we consider a
request as a directive speech act whose purpose is to
get the hearer to do something in circumstances in
which it is not obvious that he/she will perform the
action in the normal course of events. This speech
act can be performed directly or indirectly, in a more
threatening or softening way (which is directly
related to politeness theory (Goffman, 1956)). The
request can be emphasized, either projecting toward
the speaker (Can I do X?) or the hearer (Can you do
X?). A direct request may use an imperative, a
performative, obligations and want or need
Table 1: Request Speech Act custom coding scheme.
Request
Form
Linguistic form Examples
Direct
request
Imperative Do x
Performative
I am asking you to do
x.
Want or Need
statements.
I need/want you to do
x
Obligation
statements
You have to do x
Indirect
request
Query questions
about
Can you do x?
ability of the hearer
to do X
Could you do x?
Query questions
about
Would you like to
do x?
Willingness of the
Hearer to do X
Statements about
the willingness
(desire) of the
speaker
I would like if you
can do X
I would appreciate if
you can do X
DiscoveringProblem-SolvingKnowledgeinBusinessEmails-TraceabilityinSoftwareDesignUsingComputerMediated
Communication
189

statements. The more socially brutal form being an
order. An Indirect request may use query questions
about ability, willingness, and capacity etc. of the
hearer to do the action or use statements about the
willingness (desire) of the speaker to see the hearer
doing x. In our coding scheme, a grammatical
utterance corresponds to only one speech act as in
Table 1.
5 EVALUATION AND RESULTS
For the purpose of this study, we use a real world
project in software development and apply our
method on data. Data were collected after the end of
the project that last nearly 2 years.
5.1 Project Description
A publishing group editing law-related codes (like
insurance code, labor code) hired a software
development company to create a workflow tool for
their journalists. Due to geographical constraints,
nearly all the communications during specification,
implementation, tests and delivery were done
through email. The development method was mixed
agile, with weekly deliveries after initial analysis.
5.1.1 Project Team
The team was split between the contractors and the
development team. Among these, various roles and
skills were present, but the main actors were:
 SRA: Chief editing manager (skill: law and
management, Role: Contractor);
 JBJ: Information System Manager (Skill:
Information system, Role: Contractor);
 FX: Information System Developer (Skill:
Software Engineering, Role: Development
manager);
 RT: Information System Developer (Skill:
Software Engineering, Role: Sub-contractor
5.1.2 Project Schedule and Topics
The project start in early 2009 and ended in late
2010. The different phases are summed up in Table
2.
Table 2: Project main phases.
Based on project specifications and code
deliveries, a small topics dictionary was built
according to Section 4.3.2 and the message topic
matrix was computed accordingly.
Table 3: Excerpt from Project Topic dictionary.
Topic Keywords
XML structuration, tag, tree, xsd, dtd,
schema, markup, xml
BDD Database, table, fields, editorial
part, code part
Workflow workflow, validation, collaboration
Code Law, legifrance, insurance, chapter,
article, annexes, labor code
User Interface UI, login, user management, front
end, search
5.2 Frequency
Our corpus represent 3000 messages in 800 threads
between 30 projects actors. Size of message
Table 4: Daily message frequency.
We identify 10 main actors during this project
that account for more than 80% of the messages.
Also the daily frequency in Table 4 show 3 relevant
spikes of activity matching critical time of the
project: the first delivery and second delivery and a
new features addition. We will reduce our
investigation to the first spike between 06/2009 and
Q109 Q209 Q309 Q409 Q110 Q210 Q310
XML Import

Document Database specification
and development
Workflow Specification and
development
User Interface
Ex port t o magazine and websit e
Web service specification and
development
Application test
0
20
40
60
1/21/2009 1/21/2010 1/21/2011
KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing
190

09/2009 where a lot of exchanges occurs and focus
on long threads showing the presence of a dialog
As an additional information, it is to be noted
that the Development Manager was the one
receiving in TO (direct receiver) the higher number
of messages, the Chief Editing Manager was the one
sending message the most and the Information
System Manager was the one receiving the most
message in CC. This global numbers are matching
their roles in the project, respectively executing,
requesting and supervising.
5.3 Threads and Messages
At this point, we have narrowed our research for
problem solving on some specific threads of interest,
where the topics are known, the date and role of
actors and discourse turn are fully qualified. We
visualize interaction turn and highlight differences
between to and cc receivers. An example of such
thread can be viewed in Table 5.
Table 5: Thread context.
This give us rich context to perform pragmatic
analysis. To analyze message text and find problem
solving elements, we used our pragmatics custom
grid to identify request speech acts. Then, following
the message thread, we look for answers related to
same topic which emitter possess the appropriate
role and skills.
For instance, in September 2009 in a thread
related to topics XML and code, we found that a
contractor editing role (SRA) is asking for a new
xml markup in an indirect manner, one possible
answer is given in the next message by the developer
manager (FX) based on his role, competence and the
topic. We also detect a false positive request by the
contractor Information system manager (JBJ), but it
will be discarded because not related to any topics
from dictionary. Table 6 shows this example
Table 6: Messages analysis example.
DATE THREAD SKILLS Topics
Law
15/09/200917:25 CC IS book,xml
TO IS
15/09/200917:34 TO Law xml
CC IS
15/09/200917:37 TO Law
TO IS
15/09/200917:52 TO Law code
CC IS
15/09/200917:54 TO Law
TO IS
16/09/200909:35 CC IS xml,layoutexport
TO IS
A=ContractorEditor
B=ContractorInformationSytem
C=Developper
From Sentences Topics
Problem
Solving
SRA
Ificouldputa<juri>
markupwithallthe
neededcontentinsideitin
thebooks,iwouldbe
happier.
book,xml
indirect
request
to: FX Thanks,
cc: JBJ
S.
FX
Hereyouare,iadded
<link>marku
p
also
xml (answer?)
to: SRA Sorryagainforthebankers
cc: JBJ
F.
JBJ to: FX
somefreedevelopment
dayswouldbewelcome;=)
indirect
request
SRA
FX to: JBJ
It'splanned:=)Don'tworry
1days/annexe=2extra
freedaysoncode
code (answer?)
cc: SRA
customersatisfactionis..
ourmotto;=)
F.
JBJ to: FX
What!!!I'mgoingtocallall
myothersubcontractors!
Theyowe
us400days!
SRA
SRA to: FX thanks. xml
cc: JBJ
Lastnight,ithoughtabout
anothermarkupthatcould
beusefultome:amarkup
xml
indirect
request
DiscoveringProblem-SolvingKnowledgeinBusinessEmails-TraceabilityinSoftwareDesignUsingComputerMediated
Communication
191

6 CONCLUSIONS
The objective of our research is to discover problem-
solving knowledge from computer mediated
communication within a professional project. In this
paper, we present a method using business emails
for this aim. Email was considered as a specific
asynchronous and not face to face kind of discourse
and we applied some pragmatics analysis to it.
Speech act analysis alone is a difficult problem, so
we consider it inside the project context: human
organization and skills, and project specifications.
Our questions were: Does emails contain any useful
trace of project related knowledge? And could we
identify an appropriate method to extract and
structure it?
The principle of our approach is to take into
account all the aspects of the mediated exchanges
during a project. Thus we have presented a general
guideline to detect interesting (related to problem
solving in software design) threads among a vast
amount of messages. We then have proposed a
custom coding scheme to detect request and clarify
ambiguity in sentence analysis. To illustrate this, we
have analyzed a professional project corpus and
detect some parts of problems and possible answers.
On the whole these results confirm the applicability
of the technique, and we will further investigate on
different types of project and other grid of analysis
like: decision making, negotiations, and design
rationale (Matta et al, 2011). It comfort us that when
emails is not only seen as way to broadcast
information, but as a media allowing online
exchanges, like discourses, it could turn into a
source of knowledge.
Our current work objective is to explore various
techniques from machine learning to implement
support algorithm for the projection of our features
vectors (topics, pragmatics and context) to problem-
solving knowledge model. Although related to the
works of Cleland-Huang (Cleland-Huang et al,
2006) on Requirement traceability in software
design, we will focused more on functional testing
and design detection. Finally, this study is a part of
our work on project memory: traceability and
structuration of knowledge in daily work realization
of project.
REFERENCES
Baron, N. S., 1998. Letters by phone or speech by other
means: The linguistics of email. In Language &
Communication, 18(2), p133-170.
Beck, K., Beedle, M., Van Bennekum, A., Cockburn, A.,
Cunningham, W., Fowler, M., Thomas, D., 2001.
Manifesto for agile software development.
Bekhti, S., Matta, N., 2003. Project memory: an approach
of modelling and reusing the context and the de design
rationale.In Proceedings of IJCAI’03 (International
Joint of Conferences of Artificial Intelligence),
Workshop on Knowledge Management and
Organisational Memory, Acapulco.
Benveniste E., 1966. Problèmes de linguistique générale,
t. I, Paris, Gallimard.
Carvalho, V., Cohen, W., 2004. Learning to extract
signature and reply lines from email. In Proceedings
of the Conference on Email and Anti-Spam, Palo Alto,
CA.
Carvalho, V.R., Cohen, W., 2006. Improving "email
speech acts" analysis via n-gram selection. In
Proceedings of the HLT-NAACL 2006 Workshop on
Analyzing Conversations in Text and Speech (ACTS
'09). Association for Computational Linguistics,
Stroudsburg, PA, USA, p35-41.
Cleland-Huang, J.; Settimi, R.; Xuchang Zou; Solc, P.,
2006. The Detection and Classification of Non-
Functional Requirements with Application to Early
Aspects. In Requirements Engineering, 14th IEEE
International Conference, p39-48.
Cselle, G., Albrecht, K., Wattenhofer, R., 2007.
BuzzTrack: topic detection and tracking in email.
In Proceedings of the 12th international conference on
Intelligent user interfaces, p190-197.
De Felice, R., Deane, P., 2012. Identifying speech acts in
emails: Toward automated scoring of the TOEIC®
email task. In ETS Research Report No. RR-12-16,
Princeton.
Diesner, J., 2005. Exploration of Communication
Networks from the Enron Email Corpus. In
Proceedings of Workshop on Link Analysis,
Counterterrorism and Security, SIAM International
Conference on Data Mining 2005, p3-14.
Gray, P. H., 2001. A problem-solving perspective on
knowledge management practice. In Decision Support
System 31, 1 (May 2001), p87-102.
Goffman, E., 1956. The Presentation of Self in Everyday
Life, University of Edinburgh Social Sciences
Research Centre Monographs, no2.
Gospodnetic, O., Hatcher, E., 2004. Lucene in Action.
Manning Publications.
Hardin, L. E. 2002. Problem Solving Concepts and
Theories. In Journal of Veterinary Medical Education,
30(3), p227-230.
Herring, S. C., 2004. An Approach to Researching Online
Behavior. Designing for virtual communities in the
service of learning. In S. A. Barab, R. Kling, J. H.
Gray (Eds.), designing for Virtual Communities in the
KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing
192

Service of Learning (p338-376). New York:
Cambridge University Press.
Jindal, N., Liu, B., 2007. Review spam detection. In
Proceedings of the 16th international conference on
World Wide Web, p1189-1190.
Kalia, A., Motahari Nezhad, H. R., Bartolini, C., Singh,
M., 2013. Identifying Business Tasks and
Commitments from Email and Chat Conversations. In
HP Labs Technical Report.
Ladas, C., 2009. Scrumban-essays on kanban systems for
lean software development. Lulu.Com
Malvache, P., Prieur, P., 1993. Mastering Corporate
Experience with the REX Method. In Proceedings of
ISMICK’93, International Synopsium on Management
of industrial and corporate knowledge, Compiegne.
Matta, N., Ribiere, M., Corby, O., Lewkowicz, M.,
Zacklad, M., 2000. Project memory in design. In
Rajkumar Roy, (Ed.), Industrial Knowledge
Management – A Micro Level Approach, Springer-
Verlag, London.
Matta N., Ermine, J-L., Aubertin, G., Trivin, J., 2002.
Knowledge Capitalization with a knowledge
engineering approach: the MASK method. In
Knowledge Management and Organizational
Memories, Dieng-Kuntz R., Matta N. (Eds.), Kluwer
Academic Publishers.
Matta, N., Atifi, H., Sediri, M., Sagdal, M., 2010. Analysis
of Interactions on Coordination for Design Projects. In
Signal-Image Technology and Internet-Based Systems
(SITIS), 2010 Sixth International Conference ,
p344,347.
Matta, N., Ducellier, G, Charlot, Y., Beldjoudi, M.R.,
Tribouillois, F., Hibon, E., 2011. Traceability of
design project knowledge using PLM. In IEEE
proceedings of International Conference on
Cooperation Technologies and sciences, Philadelphia,
May 2011.
Newell, A., Simon, H. A., 1972. Human Problem Solving.
New Jersey: Prentice-Hall, Inc.
Searle, J. R., 1969. Speech acts: An essay in the
philosophy of language (Vol. 626). Cambridge
university press.
Stiles, W. B., 1992. Describing Talk: a taxonomy of verbal
response modes. SAGE Series in Interpersonal
Communication. SAGE Publications. ISBN:
0803944659.
Tourtier, P.A., 1995. Analyse préliminaire des métiers et
de leurs interactions. In Rapport intermédiaire, projet
GENIE, INRIA-Dassault-Aviation.
Wasiak, J., Hicks, B., Newnes, L., Loftus, C., Dong, H.,
Burrow, L., 2011. Managing by E-Mail: What E-mail
Can Do for Engineering Project Carley Management.
In IEEE TRANSACTIONS ON ENGINEERING
MANAGEMENT, VOL. 58, NO. 3.
DiscoveringProblem-SolvingKnowledgeinBusinessEmails-TraceabilityinSoftwareDesignUsingComputerMediated
Communication
193