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City, University of London Institutional Repository

Citation: Garcez, A., Spanoudakis, G. and Zisman, A. (2003). Proceedings of ACM

ESEC/FSE International Workshop on Intelligent Technologies for Software Engineering

WITSE03 (TR/2003/DOC/01). .

This is the published version of the paper.

This version of the publication may differ from the final published

version.

Permanent repository link: http://openaccess.city.ac.uk/4061/

Link to published version: TR/2003/DOC/01

Copyright and reuse: City Research Online aims to make research

outputs of City, University of London available to a wider audience.

Copyright and Moral Rights remain with the author(s) and/or copyright

holders. URLs from City Research Online may be freely distributed and

linked to.

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Proceedings of ACM ESEC/FSE International Workshop on

Intelligent Technologies for Software Engineering

WITSE 2003

A.S. d’Avila G. Spanoudakis A. Zisman

Department of Computing

City University

Technical Report Series

TR/2003/DOC/01

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A. S. d'Avila Garcez, G. Spanoudakis and A. Zisman

Proceedings of ACM ESEC/FSE International

Workshop on Intelligent Technologies for Software

Engineering WITSE03

Department of Computing

City University

Technical Report Series

TR/2003/SEG/02

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WITSE 2003

Workshop on Intelligent Technologies

in Software Engineering

ESEC/FSE 2003

9

th

European Software Engineering Conference

and

11

th

ACM SIGSOFT Symposium on the

Foundations of Software Engineering

Helsinki, Finland

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WITSE 2003

PROCEEDINGS

Workshop on Intelligent Technologies

in Software Engineering

http://witse.soi.city.ac.uk/

September 1, 2003

Helsinki, Finland

Workshop at ESEC/FSE 2003

9

th

European Software Engineering Conference

and

11

th

ACM SIGSOFT Symposium on the

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WITSE 2003

TABLE OF CONTENTS

ORGANIZING COMMITTEE

vii

WORKSHOP INTRODUCTION

ix

Developing High Assurance Software Systems: On the Role of Software Tools

1

Constance Heitmeyer

Naval Research Laboratory, USA

Abstracting Wizards from Portal Observations

2

Christopher J. Hogger

Imperial College London, UK

Frank R. Kriwaczek

Imperial College London, UK

Computing Minimal Revised Specifications by Default Logic

7

Ken Satoh

National Institute of Informatics, Tokyo, Japan

Intelligent Support for Developing Adaptable Software Architectures: A Knowledge-Based

Approach

13

Nary Subramanian

Hofstra University, NY, USA

Lawrence Chung

University of Texas at Dallas, TX, USA

Reasoning about Requirements Evolution using Clustered Belief Revision

20

Odinaldo Rodrigues

King's College London, UK

Artur d’Avila Garcez

City University London, UK

Alessandra Russo

Imperial College London, UK

The Impacts of Software Design on Development Effort – A Differential Evolution

Approach

27

Päivi Ovaska

Lappeenranta University of Technology, Finland

Alexandre Bern

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An Explanation Reasoning Procedure Applicable to Loop Transformation in Compiler

34

Mariko Sasakura

Okayama University, Japan

Susumu Yamasaki

Okayama University, Japan

Agent-Based Support for Requirements Elicitation

40

Chad Coulin

University of Technology Sydney, Australia

Didar Zowghi

University of Technology Sydney, Australia

DCBL: A framework for Dynamic Control of Behavior based on Learning

44

Patrice Vienne

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WITSE 2003

ORGANIZING COMMITTEE

Dr. Artur Garcez is a Lecturer at the Department of Computing at City University,

London. He holds an M.Eng. in Computing Engineering (honours), an M.Sc. in

Computing and Systems Engineering and a Ph.D. (D.I.C.) in Computing. He is the

author of a number of publications on Machine Learning, specifically on the integration

of Logics and Neural Networks. His research has evolved from the theoretical

foundations of Artificial Intelligent systems of Neural-Symbolic Integration to its

application in Bioinformatics and Software Engineering. Dr. Garcez is an editor of the

Journal of Applied Logic, Elsevier. He has served on the programme committees of

international conferences and workshops. He holds a Visiting Research Fellowship at

the Department of Computer Science, King's College, London. He is a member of the

British Computer Society's Requirements Engineering Specialist Group. He is an author

of the book "Neural-Symbolic Learning Systems: Foundations and Applications,

Springer-Verlag, 2002. For more information see:

http://www.soi.city.ac.uk/~aag

Dr. George Spanoudakis is a senior lecturer at the Department of Computing at City

University, London. He holds a BSc (Honours) degree in Informatics, a MSc degree in

Artificial Intelligence and a PhD degree in Computer Science. He has been a visiting

associate professor at the Department of Computer Science at the University of Crete,

and a visiting lecturer at the Department of Information Systems at the London School

of Economics. He has served in the program committees of several international

conferences and workshops on software engineering, having chaired workshops and

conference sessions. Dr. Spanoudakis has acted as a reviewer for international scientific

journals and has served as the editor of Requirenautics Quarterly, the Newsletter of the

Requirements Engineering Specialist Group of the British Computer Society. He has

published extensively in the area of software engineering. His current research interests

include requirements specification and analysis, multi-perspective software modelling,

management of inconsistencies in software documentation, and software traceability

and measurement. For more information see:

http://www.soi.city.ac.uk/~gespan

.

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WITSE 2003

PROGRAMME COMMITTEE

Artur d’Avila Garcez (City University London, UK) (co-chair)

Eric Dubois (Research Centre Henri-Tudor, Luxembourg)

Dimitra Giannakopoulou (NASA AMES, USA)

Robert Hall (AT&T Labs Research, USA)

Antony Hunter (University College London, UK)

Julio Leite (PUC-Rio, Brazil)

Neil Maiden (City University London, UK)

Tim Menzies (Nasa IV&V, USA)

Bashar Nuseibeh (Open University, UK)

Elisabetta di Nitto (Polimi, Italy)

Alessandra Russo (Imperial College London, UK)

Camille Salinesi - Ben Achour (Paris I - Sorbonne, France)

Ken Satoh (National Institute of Informatics, Japan)

George Spanoudakis (City University London, UK) (co-chair)

Andrea Zisman (City University London, UK) (co-chair)

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WITSE 2003

WORKSHOP INTRODUCTION

Software engineering practitioners and researchers continue to face huge challenges in the software development

and maintenance of software systems. The complexity of software systems and a number of recent advances in the

field of computational intelligence have been providing a fruitful integration between software engineering and

intelligent technologies. In the computational intelligence field, this is particularly true in the areas of model

checking, validation and verification, fuzzy logic and abductive reasoning, uncertainty management and belief

based reasoning, artificial neural networks and machine learning, genetic and evolutionary computing, and

case-based reasoning. In the software engineering field, this integration is seen in the areas of requirements analysis

and evolution, traceability, multiple viewpoints, inconsistency management, human-computer interaction design,

software risk assessment, and software verification.

The International Workshop on Intelligent Technologies for Software Engineering (WITSE’03) is intended to

provide a forum for presentation and discussion of a wide range of topics related to the applicability of new

intelligent technologies to software engineering problems. It aims to bring together researchers from academia

and industry, and practitioners working in the areas of computational intelligence and software engineering, to

discuss current state of the art, existing issues, recent developments, applications, experience reports, software

tools, and future research directions of intelligent technologies applied to software engineering.

WITSE’03 is a one full day event, structured around sessions composed of a keynote talk, presentations of papers

rigorously selected by the programme committee, and open round table discussions. The sessions have been

organised based on papers submitted from all corners of the globe. The workshop provides an opportunity for

exchanging valuable information on theoretical and practical aspects of:

Intelligent methods of requirements analysis and evolution

Machine learning for change management and risk assessment

Intelligent approaches for inconsistency management of software systems

Intelligent architectures for software evolution

Intelligent human-computer interaction design

Intelligent technologies for traceability management

Intelligent techniques for software validation, verification, and testing

Empirical studies, experience, and lessons learned on applying computational intelligence to software

development

We would like to take this opportunity to thank the members of the programme committee who helped in

reviewing and selecting the papers submitted to the workshop, Dr Constance Heitmeyer for her keynote talk, the

authors of the submitted and accepted papers, and the ESEC/FSE 2003 workshop co-chairs Dr Cecilia Mascolo

and Dr Andre van der Hoek for their assistance in the organisation of the workshop.

Helsinki, September 2003

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Keynote

Developing High Assurance Software Systems:

On the Role of Software Tools

Constance L. Heitmeyer

Head of Software Engineering Section

Center for High Assurance Computer Systems

Naval Research Laboratory, Washington DC, USA

[email protected]

Abstract

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(20)

Computing Minimal Revised Specifications

by Default Logic

Ken Satoh

National Institute of Informatics

2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

[email protected]

ABSTRACT

This paper presents a method of computing minimal revised

specifications represented as a function-free Horn theory.

We have already proposed a formalization of minimal

revi-sion of a logical specification and two computational

meth-ods. However, the previous methods have either a problem

of needs of minimality check or a problem of completeness.

In this paper, we propose a method using Default Logic

which not only directly computes a minimal revised

specifi-cation without minimality check, but also is guaranteed to

be complete. Then, we give a top-down proof procedure to

compute an extension correpsonding with a minimal revused

specification.

Categories and Subject Descriptors

D.2.1

[

Software

Engineering

]:

Require-ments/Specifications—

Methodologies

1.

INTRODUCTION

Software evolution

is one of the most important issue in

soft-ware engineering. The famous report [8] even in 80’s states

that 75

%

of maintenance task in software industry are

activ-ities dealing with the following issues.

Changes in the software environment.

New user requirements.

In the current network environment of computers, the above

correction/updates occurs frequently and therefore the

re-search on software evolution becomes much more important.

As the first steps, we have already formalized an update for

a specification represented as a set of Horn theories with

integrity constraints.

We divide a specification into two

parts; a temporary part and a persistent part. The

for-mer is subject to change and the latter is not allowed to be

changed. Then, when the addition of a new specification

leads to contradiction, we

minimally

revise a logical

specifi-cation to avoid contradiction. So far, we proposed the

fol-lowing methods to compute minimal revised specifications.

Using abductive logic programming [16, 17]

We

translate a logical specification into an abductive logic

program where we introduce a deletion abducible into

each rule of the temporary part of the logical

spec-ification and correspond the assumption of deletion

abducible with a deletion of the temporary part.

Using extended logic programming [18]

We translate

a logical specification into an extended logic program

where we consider all the contrapositive of the logical

specification in order to simulate a logical deduction

within extended logic programming and we introduce

a deletion literal into each rule of the temporary part

of the logical specification and correspond the truth of

deletion literals with a deletion of the temporary part.

However, either method has its own problem of computing

minimal revised specification. The former method firstly

computes a set of deletion abducibles corresponding with

the deletion of rules to avoid contradiction, but then we need

to check its minimality since the computing method in [16,

17] is not guaranteed to compute minimal abducibles. On

the other hand, the latter method does not need to perform

minimality check, but the method is not complete for

ev-ery class of logical specification; in other words, there is a

minimal revised specification, but by the latter method, we

cannot find it. So, we have to restrict the class of logical

specification to guarantee completeness.

In this paper, to solve these problems, we propose a usage

of Default Logic [12]. We translate each logical

specifica-tion into a default theory and compute an extension of the

theory. Then, we can guarantee that each extension exactly

corresponds with a possible minimal revised specification.

We also give a procedure of Default Logic which is simplied

from our original procedure of Default Logic [15] to compute

(a part of) an extension to identify which rules should be

deleted.

2.

MINIMAL REVISED SPECIFICATION

(21)

Consider the following example of a logical representation

of database and constraints which is inspired by the

exam-ple in [4, p590]. Here, we assume that data in database,

integrity constraints, and rules are represented as logical

formulas.

Example 1.

Integrity Constraint meaning that if

F

is a father of

E

then the age of

E

must be under the age of

F

:

f ather

(

F, E

)

age

(

F, A

1)

age

(

E, A

2)

(

A

1

A

2)

⊃ ⊥

.

where

means “contradiction”.

A rule of calculating the age:

birth year

(

E, Y

)

current year

(

Z

)

A

=

Z

Y

age

(

E, A

)

.

s

is the father of

c

:

f ather

(

s, c

)

.

The birth year of

s

is 1975:

birth year

(

s,

75)

.

The birth year of

c

is 1974:

birth year

(

c,

74)

.

We assume that the following:

The integrity constraint is not completely specified.

There would be an exception of the above integrity

constraint.

There are some possibilities that the information of

birth year is wrongly inserted in the database.

Therefore, we can delete either some part of the

in-tegrity constraint or some date of birth year to avoid

future contradiction.

Suppose that we add

c

(99)

.

1

. The addition of

c

(99)

.

leads to

inconsistent database state. To resolve such inconsistency,

we would consider the following possibilities.

S

1

:

b

(

s,

75) is considered to be added incorrectly and so, we

delete this information from the database.

S

2

:

b

(

c,

74) is considered to be added incorrectly and so, we

delete this information from the database.

S

3

:

We regard this situation as an exceptional situation and

so, we change the integrity constraint.

Note that there are other ways of resolving inconsistency,

but other revisions are greater than the above three

revi-sions. In this paper, we would like to have such minimal

revised specifications.

1

From now on, for brevity, we write “

f

” for “

f ather

predi-cate, “

a

” for “

age

” “

b

” for

birth year

, “

c

” for

current year

,

respectively

Definition 1.

Let

T

pst

be a set of Horn clauses which are

of the form:

B

1

B

2

...

B

l

H.

where

H

,

B

1

, ..., B

l

are atoms.

Let

T

tmp

be a set of labeled Horn clauses which are of the

form:

φ

:

B

1

B

2

...

B

l

H

where

φ

is a name for the clause.

A

logical specification

T

is a pair of

h

T

pst

, T

tmp

i

and we call

T

pst

a

persistent part

of

T

and

T

tmp

a

temporary part

of

T

.

We define a minimal revised specification based on a

maxi-mal consistent subset of the logical specification defined as

follows.

Definition 2.

Let

S

be a set of function-free Horn clauses.

A

maximal consistent subset

of

S

is

S

0

such that

S

0

is

con-sistent and

S

0

S

and there is no proper superset

S

00

of

S

0

such that

S

00

is consistent and

S

00

S

.

Definition 3.

Let

T

be a logical specification

h

T

pst

, T

tmp

i

.

Let Π

T

tmp

be a set of ground clauses obtained by replacing

all variables in each clause in

T

tmp

by every constant in

T

.

Let

R

new

be a clause.

A

minimal revised specification

w.r.t.

T

and

R

new

is

h

T

pst

∪{

R

new

}

, S

i

such that (

T

pst

∪{

R

new

}

)

S

is a maximal

consistent subset of (

T

pst

∪ {

R

new

}

)

Π

T

tmp

.

The minimal revised specification is such a specification that

deletes ground instances of

T

tmp

when

T

pst

∪ {

R

new

} ∪

T

tmp

leads to contradiction.

Example 2.

Consider the specification in Example 1.

T

pst

∪ {

R

new

}

:

b

(

E, Y

)

c

(

Z

)

A

=

Z

Y

a

(

E, A

)

.

f

(

s, c

)

.

c

(99)

T

tmp

:

b

(

s,

75)

.

b

(

c,

74)

.

f

(

F, E

)

a

(

F, A

1)

a

(

E, A

2)

(

A

1

A

2)

⊃ ⊥

.

Π

T

tmp

is as follows.

b

(

s,

75)

.

b

(

c,

74)

.

f

(

s, c

)

a

(

s,

24)

a

(

c,

25)

(24

25)

⊃ ⊥

.

f

(

s, c

)

a

(

s,

25)

a

(

c,

25)

(25

25)

⊃ ⊥

.

f

(

s, s

)

a

(

s,

24)

a

(

s,

24)

(24

24)

⊃ ⊥

.

(22)

Since

T

pst

∪ {

R

new

} ∪

Π

T

tmp

is contradictory, we have

the

following

minimal

revised

specifications

h

T

pst

{

R

new

}

andS

i

i

(

i

= 1

,

2

,

3).

S

1

= Π

T

tmp

− {

b

(

s,

75)

.

}

.

S

2

= Π

T

tmp

− {

b

(

c,

74)

.

}

.

S

3

= Π

T

tmp

{

f

(

s, c

)

a

(

s,

24)

a

(

c,

25)

(24

25)

⊃ ⊥

.

}

.

3.

COMPUTING

MINIMAL

REVISED

SPECIFICATION BY DEFAULT LOGIC

In this section, we give a translation from a logical

speci-fication to a default theory in order to compute a minimal

revised specification.

Definition 4.

Let

T

be a logical specification

h

T

pst

, T

tmp

i

where

T

pst

and

T

tmp

are function-free. We define

a

trans-lated default theory for a consistency management of

T

(de-noted as

DT

CM

(

T

) = (

D, W

)) as follows.

W

consists of the following clauses.

Every clause in

T

pst

is in

W

.

Let

φ

:

B

1

...

B

l

H

T

tmp

. We introduce

a

deletion literal

del

φ

(

x

) for each clause

φ

where

x

is a tuple of free variables in

φ

. Then, a clause

B

1

, ..., B

l

H

del

φ

(

x

)

.

is in

W

.

Unique name axioms and domain closure axioms

are in

W

.

D

consists of the following defaults.

{

:

¬

del

φ

(

x

)

¬

del

φ

(

x

)

|

(

φ

:

C

(

x

))

T

tmp

}

Definition 5.

Let

φ

:

B

1

B

2

...

B

l

H

be a labelled

clause and

X

=

h

X

1

, ..., X

k

i

be a tuple of free variables

in

φ

and

θ

1

, ..., θ

m

be substitutions of ground terms to these

free variables.

Updated clause

U pdate

(

φ

;

θ

1

, ...θ

m

)

of

φ

w.r.t.

θ

1

, ..., θ

m

is defined as the following clause:

φ

0

:

¬

EQ

(

X

, θ

1

)

...

∧ ¬

EQ

(

X

, θ

m

)

L

1

...

L

l

H

where

φ

0

is the new name of the above clause and

EQ

(

X

, θ

i

) = ((

X

1

= (

X

1

θ

i

))

...

(

X

k

= (

X

k

θ

i

)))

The following theorem shows that an extension of

DT

CM

(

T

)

exactly corresponds with a minimal revised specification.

Theorem 1.

Let

T

be a function-free logical specification

h

T

pst

, T

tmp

i

, and let

R

new

be an added clause. If there is an

extension

E

for

DT

CM

(

h

T

pst

∪ {

R

new

}

, T

tmp

i

) with a set of

deletion literals ∆, then

h

T

pst

∪{

R

new

}

,

(

T

tmp

T

del

)

T

add

i

is a minimal revised specification where

T

del

=

{

φ

:

B

1

, ..., B

l

H

|

(

del

φ

(

x

)

θ

)

}

and

T

new

=

{

U pdate

(

φ

;

θ

1

, ...θ

m

)

|

(

del

φ

(

x

)

θ

i

)

}

.

Conversely, if there is a minimal revised specification

h

T

pst

∪ {

R

new

}

, T

new

i

, then there exists an extension

E

for

DT

CM

(

h

T

pst

∪ {

R

new

}

, T

tmp

i

) s.t. Π

T

tmp

Π

T

new

=

{

φ

:

(

B

1

, ..., B

l

H

)

θ

|

(

del

φ

(

x

)

θ

)

E

}

.

Proof.

We use the following proposition mentioned

in[13]. Let

D

be a set of defaults, each of the form

:

β

β

.

We denote a set of consequents of defaults,

{

β

|

:

β

β

D

}

as

CON S

(

D

).

Proposition 1.

Suppose

R

is a set of default rules, each

of the form

:

α

α

for a first-order sentence,

α

. Then

E

is an

extension for the default theory (

R, W

) iff

E

=

T h

(

W

CON S

(

D

)) where

D

is a maximal subset of

R

such that

W

CON S

(

D

) is consistent.

Proof of Theorem 1:

By the above proposition and the

defininition of

DT

CM

(

T

)(=

h

D, W

i

),

E

is an extension of

DT

CM

(

T

) if and only if

W

∪ {¬

del

φ

(

t

)

del

φ

(

t

)

E

}

is a

maximal consistent subset of

W

∪{¬

del

φ

(

t

)

|

φ

T

tmp

}

. This

equivalently means that

T

pst

∪ {

R

new

} ∪

(

T

tmp

T

del

)

T

add

is a maximal consistent subset of (

T

pst

∪ {

R

new

}

)

Π

T

tmp

,

or equivalently,

h

T

pst

∪ {

R

new

}

,

(

T

tmp

T

del

)

T

add

i

is a

minimal revised specification.

Example 3.

Consider the database specification

T

=

h

T

pst

, T

tmp

i

in the Example 1. To compute a minimal

re-vised specification, we translate the specification into the

following default theorm (

D, W

).

W

:

b

(

E, Y

)

c

(

Z

)

A

=

Z

Y

a

(

E, A

)

.

f

(

s, c

)

.

c

(99)

b

(

s,

75)

del

φ

1

.

b

(

c,

74)

del

φ

2

.

f

(

F, E

)

a

(

F, A

1)

a

(

E, A

2)

(

A

1

A

2)

del

φ

3

(

F, E, A

1

, A

2)

.

D

:

:

¬

del

φ

1

¬

del

φ

1

,

:

¬

del

φ

2

¬

del

φ

2

,

:

¬

del

φ

3

(

F, E, A

1

, A

2)

¬

del

φ

3

(

F, E, A

1

, A

2)

Then, three extensions

E

1

, E

2

, andE

3

containing the

follow-ing literals for the above default theory.

E

1

⊇ {

del

φ

1

,

¬

del

φ

2

,

¬

del

φ

3

(

s, c,

24

,

25)

,

¬

b

(

s,

75)

,

¬

a

(

s,

24)

, a

(

c,

25)

, b

(

c,

74)

, f

(

s, c

)

, c

(99)

}

E

2

⊇ {¬

del

φ

1

, del

φ

2

,

¬

del

φ

3

(

s, c,

24

,

25)

,

¬

b

(

c,

74)

,

¬

a

(

c,

25)

, a

(

s,

24)

, b

(

s,

75)

, f

(

s, c

)

, c

(99)

}

E

3

⊇ {¬

del

φ

1

,

¬

del

φ

2

, del

φ

3

(

s, c,

24

,

25)

,

a

(

c,

25)

, b

(

c,

74)

, a

(

s,

24)

, b

(

s,

75)

, f

(

s, c

)

, c

(99)

}

Figure

Figure 1: Proof Procedure for Calculating MinimalDeletion Literals
Table 1. Attributes (design properties)
Table 3: The values of the attributes of subsystem A.
Table 6: The value of the increase in the error (%) while excluding significant attributes one by one from the model
+4

References

Related documents