SIPPING WG                                              R.G. Crespo
Internet-Draft                                          UTL
<draft-cl-sipping-fi-problem-00.txt>                    L. Logrippo
Intended Status: Informational                          UQO
Expires April 14, 2009                                  October 14, 2008

                 Feature Interactions Problem Statement
                  <draft-cl-sipping-fi-problem-00.txt>

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Abstract

Internet applications have been enhanced with many features.
A feature may be defined as a unit of functionality in a system, having
a self-contained role.
However, the introduction and modification of features may result in
undesired behaviors, and this effect is known as feature interaction-
FI for short.

This document provides a description of the FI problem in Internet, the
main problems to be solved in the FI resolution, and compares some
solutions for FI identification and resolution explored that have been
discussed in the literature.





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Table of Contents

   1. Introduction  . . . . . . . . . . . . . . . . . . . . . . . . .  2
      1.1. Conventions of this document . . . . . . . . . . . . . . .  3
      1.2. Terminology  . . . . . . . . . . . . . . . . . . . . . . .  3
   2. FI detection  . . . . . . . . . . . . . . . . . . . . . . . . .  4
      2.1. Taxonomies . . . . . . . . . . . . . . . . . . . . . . . .  4
      2.2. Representation schemes . . . . . . . . . . . . . . . . . .  5
      2.3. Detection algorithms . . . . . . . . . . . . . . . . . . .  6
   3. FI resolution . . . . . . . . . . . . . . . . . . . . . . . . .  7
      3.1. Knowledge location . . . . . . . . . . . . . . . . . . . .  7
      3.2. Models of relationships between features . . . . . . . . .  7
      3.3. Resolution strategies  . . . . . . . . . . . . . . . . . .  7
   4. Security Considerations . . . . . . . . . . . . . . . . . . . .  8
   5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . .  9
   6. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . .  9
   7. References  . . . . . . . . . . . . . . . . . . . . . . . . . .  9
   Author Address . . . . . . . . . . . . . . . . . . . . . . . . . . 11
   Intellectual Property and Copyright Statements . . . . . . . . . . 12

1. Introduction

   The purpose of this draft is to document the Feature Interaction (FI)
   problem as it relates to Internet applications. It aims to introduce
   the Internet community to the range of the current views and proposed
   solutions in this reasearch area.  This draft is meant to serve as an
   introduction to a companion document Distributed Resolution of
   Feature Interactions for Internet Applications, where a specific FI
   resolution method is proposed.

   The FI problem manifests itself in several distinct aspects, and
   these will be classified and documented.  There are also three
   distinct, but compatible, means of addressing the problem with
   multiple techniques within each of these means. These means and
   techniques will also be documented.

   Internet applications provide a set of basic services. For example,
   the Email protocol [4] provides two basic functionalities: sending a
   message to another user and reading a message from another user.

   Internet applications have been enhanced with many features, each
   defined as a unit of functionality existing in a system and each
   perceived as having a self-contained role.  For example,
   ForwardMessage and AutoResponder are popular Email features.
   ForwardMessage forwards all incoming Email messages to another user.
   AutoResponder reads the incoming message and sends automatically a
   notification message to the Email originator, e.g. the recipient is
   absent on vacation and will read the message after return.



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   In feature-based applications, interactions are necessary and
   expected occurrences.  On a given node, several features and basic
   functionalities may be available.  Usually a priority order is
   implemented among them, with the basic functionalities having the
   lowest priority.  For example, when an incoming or outgoing message
   is generated, which triggers both a feature and a basic
   functionality, only the feature is executed.

   Features, which work fine alone, may result in undesired behaviors
   when interacting with another features, and this effect is known as
   feature interaction (FI).

   As an example of FI, suppose that Bob instructs the Email server to
   execute the ForwardMessage feature, forwarding all messages to Carl.
   A message that Alice sends to Bob is sent again to Carl.  Suppose
   also that Carl subscribes to the AutoResponder feature.  The Email
   server of Carl accepts Alice message and sends a notification message
   to Bob, not to the message originator (Alice).  This result goes
   against the initial goal of AutoResponder feature, to notify the
   originator that Carl is away.  The Email server of Bob, when
   receiving the notification message, forwards it back to Carl. The
   Email server of Carl detects a loop, another undesired behavior, and
   discards the notification message.

   The FI detection problem, first identified in circuit-switched
   networks [5], has been studied in many Internet applications, such as
   Email [6], VoIP [7], WWW [8] and networked home appliances [9].

   The increasing number of FIs, and the inconvenience they are causing,
   has led industry and researchers to identify basic concepts in
   feature handling.  A Workshop series was created as a forum for
   discussing this problem: the Feature Interaction Workshop (FIW),
   which has now become the International Conference on Feature
   Interaction (ICFI).  This series has been going on since 1992 and its
   proceedings have been regularly printed by IOS Press.  Already at the
   2nd International Workshop on Feature Interactions in
   Telecommunication Systems three basic concepts were identified: FI
   detection, FI avoidance and FI resolution.


     * Detection aims at the identification of FIs, with suitable meth-
     ods.

     * Avoidance means to intervene at the protocol or design stages to
     prevent FIs, before features are executed.

     * In the resolution, actions are exercised at run time over already
     detected FIs to mediate their undesired properties.



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   The distributed nature of Internet, with multi-vendor and multi-
   provider environments, and the end user capability to program and
   tailor features, makes it impossible to rely on avoidance.  In this
   draft we list the main problems encountered in the detection and res-
   olution tasks. For each problem, we compare solution approaches
   explored so far.

1.1. Conventions of this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL NOT", "SHOULD",
   "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL", are to be inter-
   preted as described in RFC 2119 [2] and indicate requirement levels
   for compliant implementations.

1.2 Terminology


   FI:    Feature interaction
   FOL:   First order logic
   IETF:  Internet Engineering Task Force
   IRAdv: Interaction Resolver Adviser
   MUMC:  Multiple User Multiple Component
   MUSC:  Multiple User Single Component
   STR:   State Transition Rule
   SUMC:  Single User Multiple Component
   SUSC:  Single User Single Component
   TL:    Temporal logics
   VoIP:  Voice over Internet
   WWW:   World Wide Web

2. FI detection

   In the FI detection task two major issues have been studied, FI clas-
   sification and FI detection. Algorithms have been designed to solve
   these problems.


     * FI classification, or taxonomy, groups FIs according to shared
     characteristics.

     * In FI detection algorithms, a scheme for representing features is
     selected and an algorithm identifies feature pairs that are likely
     to interact.

   2.1. Taxonomies

   Several taxonomies have been produced to classify different types of
   FI.  In the most commonly used benchmark of [10], FIs are classified



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   by the nature of the interaction, as well by the cause of the inter-
   action.

   Concerning the nature of the interaction, three dimensions are iden-
   tified:

     * the number of users involved in the interaction (SU-single user
     or MU-multiple user),

     * the number of network components involved in the interaction (SC-
     single component or MC-multiple component), and

     * the kind of features involved in the interaction (custom or sys-
     tem).

   Causes of FI, listed in [10], are: violation of feature assumptions,
   limited network support, and problems in distributed systems.  This
   list is not complete, and the emergence of new Internet applications
   may generate other causes of FIs.

   SUSC (Single User, Single Component) feature interactions can occur
   when incompatible features are simultaneously in use by one single
   user in a single node.  As an example of SUSC feature interaction,
   suppose that one party subscribes to the AutoResponder and FilterMes-
   sage Email features.  AutoResponder is described in section 1.  Fil-
   terMessage screens the message initiator and the message is read only
   if the initiator does not belong to a screening list.  If Bob sub-
   scribes to AutoResponder and FilterMessage features, and Alice is
   included to the screening list, her message will be rejected.  How-
   ever, AutoResponder may send to Alice a notification that Bob is
   absent and this outcome is incoherent with the FilterMessage goal.

   SUMC (Single User, Multiple Component) feature interactions occur
   when features are deployed in different network components.  As an
   example of SUMC feature interaction, suppose that one party sub-
   scribes to the AddressBook and FilterDestination Email features.
   AddressBook considers the target address to be an alias, and replaces
   it by a real address.  FilterDestination screens the message destina-
   tion and the message is sent only if the destination does not belong
   to a screening list.  However, it is possible that the original
   address is in the screening list, but not the new one, and the mes-
   sage is sent if the feature checks if the destination address is in
   the list after translation.

   MUSC (Multiple User, Single Component) feature interactions can occur
   when several users are involved in several features executing on the
   same node.  As an example of MUSC feature interaction, suppose that
   one party subscribes to the FilterDestination and ForwardMessage



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   Email features.  The FilterDestination is described in the SUMC exam-
   ple, and ForwardMessage is described in section 1.  Suppose that Bob
   subscribes to the ForwardMessage feature, forwarding all incoming
   message to Carol. Suppose also that Carol subscribes to FilterDesti-
   nation feature, and includes Alice in the filtering list.  If Alice
   sends an Email message directly to Carol, FilterDestination rejects
   the message.  However, if Alice sends an Email message to Bob, the
   message will be forwarded to Carol and received by her.  This outcome
   is inconsistent with the goal of the FilterDestination feature.

   MUMC (Multiple User, Multiple Component) feature interactions occur
   when several users are involved in features executing on different
   nodes.  As an example of MUSC feature interaction, consider that two
   WWW sites [11] implement ForwardMessage to each other. A browser will
   continuously change its display between the two sites.

   Finally, CUSY feature interactions occur among customer features and
   system features for operation, maintenance or administrative ser-
   vices.  As an example of CUSY feature interaction, consider a Web
   analytic center that collects WWW statistics. For page access counts,
   should the center consider the WWW site that implements ForwardMes-
   sage to another site, or simply the final destination site?

   2.2. Representation schemes

   FI detection requires the representation of participating features in
   some presentation scheme.  Beyond FI detection, feature presentation
   schemes are also essential for feature development.

   Several scheme paradigms are available for feature representation.  A
   review of several existing presentation schemes is given in [12].  We
   have:

     * State machines, where the basic system and the features are rep-
     resented as separate state machines, in which messages trigger
     state changes.  This approach is illustrated in [13].

     * More sophisticated models capable of providing a global view of
     the concurrent execution of different parts of a system and of sev-
     eral features working in parallel.  Three different such represen-
     tations are available in the literature.

        * Formally based, with mathematically described semantics.
        Example of formal concurrent schemes used for feature represen-
        tation are Petri-nets [14] and process algebras such as LOTOS
        [15].

        * Event based, centered on the events in which features may



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        participate.  In contrast to finite machine presentation
        schemes, the internal states of the feature may be invisible
        outside the system.  Chisel [16] is one example of an event
        based presentation scheme, and process algebras [15] are also
        capable of representing systems in this way.

        * Modeling languages, where state models and imperative lan-
        guages are extended with concurrent primitives in order to rep-
        resent protocols.  Promela [17] is an example of a modeling lan-
        guage.

     * Logic based, where formal semantics and deduction rules are
     available for property identification.

        * Temporal Logic (TL) is a choice for logic based presentation
        schemes.  TL involves operators expressing modalities such as
        henceforth and eventually.  Varieties of TL adopted for feature
        presentation schemes are Linear Temporal Logic [18], Branching
        Time Temporal Logic, and Temporal Logic of Actions [19].

        * Predicate logics use the concepts of pre-condition and post-
        condition of actions. They may be adopted in cases where fea-
        tures are triggered by one message, generate a limited number of
        messages and the effects of messages are described by pre and
        post conditions.  The description of state transition rules in
        [20] is an example of a presentation scheme based on predicate
        logics.

     * Informal presentation schemes. Because of their readability, such
     schemes have been adopted to complement formal presentation
     schemes.  For example, Use Case Maps describes scenarios by depict-
     ing casual relationships between responsibilities [15].

   2.3. Detection algorithms

   Research on FI detection as been focused on methods to check if a
   pair of features are likely to interact.  This can be done by using
   one of the many static FI detection algorithms known in the litera-
   ture.  A review of several existing methods is given in [12].  FI
   detection methods adopt property identification, behavior approaches
   or both.  Properties analyzed in FI detection include inconsistency,
   realizability and satisfiability.

   We highlight four methods of FI detection.

     * Simulation, where execution traces are analyzed to check if unac-
     ceptable behaviors may occur.  Simulators have been implemented to
     simulate feature representations in state machines [21] and



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     concurrent languages, such as LOTOS [15].  The unacceptable behav-
     iors detected by simulators include deadlock, non determinism and
     the existence of unreachable states.

     * Model checking unfolds the system requirements, described as a
     model, into a transition system. The model checker searches for a
     path where properties, usually expressed in some type of logic (FOL
     or TL), are not satisfied.  Model checkers have been implemented
     over different model and property languages, such as SPIN [17] with
     models expressed by the modeling language Promela and properties
     expressed in LTL, and SVAL [22] with models expressed by state
     transition rules and properties expressed in FOL.

     Although simulation and model checking are conceptually similar,
     there are some differences in the implementation.  In model check-
     ing, the state explosion problem is a major concern.  Moreover, in
     model checking the properties are expressed over the entire path
     while in simulation the properties focus only on the current state.

     * Theorem proving, where system properties are formally derived
     from the combined feature specifications [23].

     * Security based approach reverses the FI detection question to the
     following: from one specific feature, what hypothetical features
     could cause interaction? [24]

     The identification of a hypothetical feature that causes interac-
     tion, based on a STR representation of feature [20] requires to
     specify the set of every possible predicate the system may satisfy,
     the set of every possible message for which the feature may inter-
     act, the restrictions that predicate and message parameters must
     satisfy, and the inconsistencies revealing a feature interaction.
     The hypothetical feature that causes interaction is generated by
     inserting the inconsistency at the given feature post condition and
     propagating back to the pre condition.

   Although these four methods detect candidates for FI, users must
   decide if such candidates represent undesirable interactions that
   must be resolved.  For example, the MUMC feature interaction consist-
   ing of mutual ForwardMessage may be acceptable for WWW sites (for
   example, to represent a clock) but totally unacceptable for Email.

3. FI resolution

   After a message generation, or arrival, several potentially conflict-
   ing features may be selected for execution. The FI conflict must be
   resolved by deciding which feature can go ahead, possibly with change
   of feature parameters.



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   The implementation of the FI resolution task must solve three main
   issues:

     * Knowledge displacement, that defines where the control of feature
     execution lies and where the FI resolver may collect application
     and user statuses.

     * What is the model of relationships between features.

     * What is the method to resolve FIs between sets of feature candi-
     date for execution.

   3.1. Knowledge location

   The distributed control of Internet implies that feature control must
   be exercised solely on the nodes where they are executed.

   The application status is expected to be known to the node that
   selects the feature to execute, among a set of candidates.  However,
   more than one node may be involved in the interaction (consider the
   case of MC interactions).

   User status is expected to be known to the node to which the user is
   attached. Yet, more than one user may be involved in an interaction
   (consider the case of MU interactions).

   Moreover, message transfer from one node to another implies a modifi-
   cation of the processing environment for the message, as well as of
   the number of features that are selected for message processing.

   Therefore, FI resolution cannot be centralized.  Node cooperation,
   which implies location of the information source and information
   exchange between nodes, is mandatory for a FI resolution on Internet
   applications that satisfy customer expectations.

   3.2. Models of relationships between features

   Many models of relationships between features have been proposed.
   These models may be classified according to the visibility of rela-
   tionships between features.

   Relationships between features may be expressed explicitly using
   familiar data structures, such as tables [25,26] and trees [27].
   Relationships may be implicit, for example expressed by logic formu-
   las [28,29].

   The relationships between features may not express directly the pos-
   sible interactions between features. It is the method that identifies



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   where FIs occur and, by looking at the relationships between the fea-
   tures, the resolution method selects and adapts the features for exe-
   cution.

   3.3. Resolution strategies

   Three approaches have been explored for FI run time resolution [12]:
   one phase, two phase and negotiation.

     * The one phase approach uses one feature manager to decide which
     of several conflicting features is executed, according to data
     structures such as tables or trees, or logical formulas.

     The table may be used to specify a list of priorities [25], or the
     feature manager decides the activation of an additional feature on
     the basis of the resulting status of the first feature and the
     relationships displayed in the table [26].  Because [25] uses a
     stack to express priority relationships between features, relation-
     ships between interactions may be modified as result of feature
     execution.

     Tables can be replaced by state trees, where the features suggest
     possible responses and the feature manager rolls back in case of
     rejection [25].

     The one phase approach suffers from the difficulty of collecting
     user status from nodes outside the node processing the message.

     * The two phase approach, where the feature is executed in an iso-
     lated environment and actions are taken in case of FI [30].  The
     isolated environment is impractical in Internet.

     * Negotiation approach

     Several negotiation approaches have been proposed.

        * In the arbitrated approach, the negotiator (frequently desig-
        nated as Feature Manager), has the sole responsibility to find a
        solution.  Features are described as agents and interact with
        each other, by posting their intentions to a common tuple space.
        The replies, from other features, can be other intentions or
        permission/interdiction directives.  Negotiation policies
        explored include deontic-based ones with obligations and goals
        [28], constraint formulas [29], fuzzy policies [31] and rela-
        tional assertions [32].

        * Indirect negotiation [10], where a dedicated negotiator con-
        trols the negotiation and proposes solutions based on



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        experience.  The dedicated negotiator uses a centralized infer-
        ence machine to select the feature to be executed from the con-
        straints expressed by agents.

        * Direct negotiation, where agents negotiate directly without a
        negotiator.  Direct negotiation based on distributed artificial
        intelligence techniques [33] consumes too much processing power
        and communication bandwidth.  To reduce performance constraints,
        several alternatives of directed negotiation have been explored,
        such as a prune and extract approach [34] where a solution space
        is computed and undesired traces are removed.

     Currently there is no evidence of advantages between arbitrated and
     direct negotiation approaches.

4. Security Considerations

   This document depicts the feature interaction problem and the main
   problems to be solved in the FI resolution.

   Although this document does not define any protocol, feature interac-
   tion may result in unexpected behavior. The unexpected behaviors may
   include access to confidential information.  For example, consider
   that Bob subscribes to DecryptMessage and ForwardMessage Email fea-
   tures. Consider also that someone sends to Bob an Email using his
   public key. Bob Email server may sucessfully decipher the message,
   using his secret key.  However, if ForwardMessage is executed after
   the execution of DecryptMessage feature, the secret message will be
   sent in clear.

5. IANA Considerations

   None.

6. Acknowledgements

   This document is based on discussions with Tom Gray of PineTel, and
   Jacques Sincennes of the University of Ottawa, who contributed sig-
   nificantly to the ideas presented here.

7. References

7.1 Normative references

   [1] S. Bradner, Intellectual Property Rights in IETF Technology, BCP
              79, RFC 3979, March 2005.

   [2] S. Bradner, Key words for use in RFCs to Indicate Requirement



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              Levels, BCP 14, RFC 2119, March 1997.

   [3] S. Bradner, IETF Rights in Contributions, BCP 78, RFC 3778, March
              2005.

6.2 Informative references

   [4] J. Klensin, Simple Mail Transfer Protocol, RFC 5321, October
              2008.

   [5] T.F. Bowen, F.S. Dworak, C.H. Chow, N.D. Grifeth, G.E. Herman,
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   [6] R. Hall, Feature Interactions in Electronic Mail, 6th Intl Work-
              shop on Feature Interactions in Telecommunication and
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   [7] J. Lennox, H. Schulzrinne, Feature Interactions in Internet Tele-
              phony, 6th Intl Workshop on Feature Interactions in
              Telecommunication and Software Systems, pp. 38-50, May
              1998.

   [8] M. Weiss, Feature Interactions in Web Services, 7th Intl Workshop
              on Feature Interactions in Telecommunication and Software
              Systems, pp. 149-156, June 2003.

   [9] M. Nakamura, Y. Igaki, K.-i. Matsumoto, Feature Interactions in
              Integrated Services of Networked Home Appliances, 8th Intl
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   [10] E.J. Cameron, N. Griffeth, Y.-J. Lin, M.E. Nilson, W.K. Schnure,
              A feature interaction benchmark for IN and beyond, 2nd
              Intl Conference on Feature Interactions in Telecommunica-
              tions Systems, pp. 1-23, May 1994.

   [11] R. Fielding, J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P.
              Leach, T. Berners-Lee, Hypertext Transfer Protocol --
              HTTP/1.1, RFC 2616, June 1999.

   [12] M. Calder, M. Kolberg, E.H. Magill, S. Reiff-Marganiec, Feature
              interaction: a critical review and considered forecast,
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   [13] D. Mery, J.P. Gibson, Telephone feature verification: Translat-
              ing SDL to TLA+, 8th SDL forum, pp. 103-118, September



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              1997.

   [14] M. Nakamura, Y. Kakuda,T. Kikuno, Petri-net based detection
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              experimental evaluation, 3rd Intl Workshop on Feature
              Interactions in Telecommunication Systems, pp. 138-152,
              1995.

   [15] D. Amyot, L. Charfi, N. Gorse, T. Gray, L.  Logrippo, J. Sin-
              cennes, B. Stepien, B.T. Ware, Feature description and
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              cation Systems, pp. 274-289, May 2000.

   [16] A. Aho, S. Gallagher, N. Griffeth, C. Schell, D. Swayne, Sculp-
              tor with Chisel: requirements engineering for communica-
              tion services, 5th Intl Workshop on Feature Interactions
              in Telecommunication and Software Systems, pp. 45-63,
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   [17] M. Calder, A. Miller, Using SPIN for feature interaction analy-
              sis-a case study, 8th Intl SPIN Workshop on Model checking
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   [18] A. Felty, K. Namjoshi, Feature specification and automatic con-
              flict detection, ACM Transactions on Software Engineering
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   [19] J. Blom, B. Jonsson, L. Kempe, Using Temporal Logic for Modular
              Specification of Telephone Services, 2nd Intl Conference
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              197-216, May 1994.

   [20] Y. Hirakawa, T. Takenaka, Telecommunication service description
              using state transition rules, 6th Intl Workshop on Soft-
              ware Specification and Design, pp. 140-147, October 1991.

   [21] K.P. Pomakis, J.M. Atlee, Reachability analysis of feature
              interactions: a progress report, ACM SIGSOFT Intl Sympo-
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   [22] T. Yokogawa, T. Tsuchiya, M. Nakamure, T. Kikuno, Feature Inter-
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              2579-2587, December 2003.

   [23] A. Gammelgard, J.E. Kristensen, Interaction Detection, a Logical



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              Approach, 2nd Intl Workshop on Feature Interactions in
              Telecommunication Systems, pp. 178-196, May 1994.

   [24] R.G. Crespo, Security Analysis of Features Interaction, 2nd Intl
              Conference on Internet Technologies and Applications, pp
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                          FI-Problem Statement          October 14, 2008


              No. 8, pp. 48-55, August 1993.

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Author Address

   Rui Gustavo Crespo
   Electrical and Computer Engineering Department
   Technical University of Lisbon
   Av. Rovisco Pais, 1049-001 Lisboa
   Portugal

   Phone: +351 - 21 8417 626

   EMail: R.G.Crespo@comp.ist.utl.pt

   Luigi Logrippo
   Departement de Informatique et Ingenierie
   Universite du Quebec en Outaouais
   Case Postale 1250, Succ. B, Gatineau QC J8X 3X7
   Canada

   Phone: (819) 595-3900 x 1885

   Email: luigi@uqo.ca























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                          FI-Problem Statement          October 14, 2008


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