Software requirement analysis enhancements byprioritizing re

Software requirement analysis enhancements by
prioritizing requirement attributes using rank
based Agents.
Ashok Kumar Vinay Goyal
Professor Assistant Professor
Department of Computer Science and Applications Department
of MCA
Kurukshetra University, Kurukshetra, India Panipat Institute of
Engineering & Technology
Panipat, India
Abstract- This paper proposes a new technique in the
domain of Agent oriented software engineering. Agents
work in autonomous environments and can respond to
agent triggers. Agents can be very useful in requirement
analysis phase of software development process, where
they can react towards the requirement triggers and
result in aligned notations to identify the best possible
design solution from existing designs. Agent helps in
design generation process, which includes the use of
Artificial intelligence. The results produced clearly
shows the improvements over the conventional
reusability principles and ideas.
1. INTRODUCTION
Agent oriented software engineering is a new
emerging technique which is growing very
rapidly. Software development industries have
invested huge efforts in this domain and results

published by many of them are very exiting [1].
The autonomous and reactive nature of agents
makes it possible for the designers to visualize
in terms of real life problem solving scenarios
where socio-logical [2] characteristics of agents
automatically activate the timely checks for any
problem in domain and to solve the same using
agents.
Agents are very helpful in the software
development life cycle. Experiments carried out
in past have shown [2][9][10] the improvement
in the SDLC and conclusion is that agents can be
very helpful in cost and effort minimization; if
tuned properly. Fine-tuning of agents and SDLC
process-state-plug-in for two-way
communications results in agent based software
development process where intelligent agents
will take decisions for better time and resource
utilization.
Fine-tuning of agents and SDLC process-state-
plug-in for two-way communications results in
agent based software development process
where intelligent agents will take decisions for
better time and resource utilization. Agents are
capable of storing historic data, which helps in
decision-making using heuristic based approach.
This paper discusses the details of one such
experiment conducted to improve the
requirement analysis process with the help of
proactive agents. Agents automatically sense the

requirement environment and propose their own
set of important requirement checklist. This is
sort of intelligent assistance with domain
heuristic, which leads to cover all possible
requirement entities of the problem domain.
2. RELATED WORK
Michael Wooldridge, Nicholas R. Jennings &
David Kinny describe the analysis process using
agent-oriented approach [1]. They have
considered the GAIA notations. The analysis
stages of Gaia are:
1) Identify the agent’s roles in the system, which
typically correspond to identify roles of
individuals, departments or organizations
themselves. The output of this will be a
prototypical roles model i.e. a list of the key
(IJCSIS) International Journal of Computer Science and
Information Security,
Vol. 9, No. 8, August 2011
105 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
roles that occur in the system, each with an
informal, unelaborated description.
2) For each role, identify and document the
associated protocols. Protocols are the patterns
of interaction that occur in the system between
the various roles.
3) Using the protocol model as a basis, elaborate
the roles model. The output of this activity will

be fully elaborated roles model, which
documents the key roles occurring in the system,
their permissions and responsibilities, together
with the protocols and activities in which they
participate.
Software designers and researchers have been
experimenting [3][5][7] with flexible agent-
based architectures as the foundation for next
generation systems. However, the success of
these systems will depend on how well they fit
into the embedding technical, business, and
organizational environments, and on their
ongoing ability to respond to changes in these
environments. A critical piece of technology,
therefore, is the ability to model and analyze the
architecture of software-based systems in
relation to their complex, evolving
organizational and business environments. An
important component of the capability is the
systematic treatment of non-functional
requirements (such as maintainability,
reliability, performance, and other quality
issues) during design and their monitoring
during execution [8][13].
Requirement engineering resources proposed a
new technique of Self-adaptive software and
agent oriented paradigm. An Autonomic
Computing mechanism defines a set of desirable
attributes such as self-configuration, self-
optimization, self-healing and self-protection.
All this is possible because of a core property;
self-adaptivity, which has been defined as the
ability of software to use available information
about changes in its environment to improve its

behavior [2]. A new architecture (control-loop
based) has been proposed to realize self-adaptive
software. This architecture will make the system
enable to sense its execution environment and to
choose a behavior aiming at maximizing system
availability and reliability. The details have been
investigated that define how to design self-
adaptive software and to focus on specific
autonomic properties based on it. This refers to
the ability of predicting problems and taking
actions to prevent a possible failure. An agent-
oriented paradigm adopted for requirements
analysis and design, and Multi-agent systems
(MAS) technologies, which allow the developers
to implement the control-loop architecture and
to enhance it with knowledge representation and
reasoning techniques. The resulting framework
will provide the methods for specifying design
alternatives, borrowed from goal-oriented
methodologies, MAS techniques to execute
system specifications into a simulated
environment, in order to observe system
behavior and to support the dynamic refinement
of design artifacts using feedback from run-time
behavior; traceability between design and code
artifacts.
Jaime Simão Sichman, François Bousquet and
Paul Davidsson have proposed the new
prospects of requirement engineering with the
involvement of Agent based approach [3]. A
preliminary reference model for the
requirements specification of agent-based
simulation platforms has been proposed which
aid the identification of general principles to

develop platforms. This concept advances the
analysis and prospection of technical-operational
and high-level requirements. Agent based
simulation platform promote the identification of
shared requirements [14]. This approach
addresses the development of an integrated
work. A new reference model is defined and a
comparative analysis has been made resulting in
an unambiguous and schematic characterization
of computational systems for agent-based
simulation [13].
Chiung-Hui Leon Lee and Alan Liu proposed a
new method for Agent based system
requirement analysis. It has been defined that an
agent-based system is a complex software
system with functional and nonfunctional
constrain and designing and building such
system is a complex task [4]. The idea is to use a
goal-driven based use case method for agent-
based system requirements analysis. This
approach is used to extract system requirements
from user’s point of view. It works with related
ISSN 1947-5500
use cases assigned to corresponding roles. The
defined use cases are extended with goals for
implicit requirements analysis in contrast to

role’s point of view. The role is treated as
internal actor to find more system specific use
cases [15][16]. After the system find system
specific use cases, the relationships between use
cases and goals are identified. These
relationships are satisfied, satisfiable, denied,
deniable, and independent. Those relationships
help find the relationship among roles.
Identifying these relationships assists the system
analyzer to analyze and optimized the
relationships among roles and this system will
contribute to a systematic approach for implicit
requirements analysis with efficiency
[18][19][20].
3. EXPERIMENTAL DETAILS
Our experiment discusses the details of
experiment conducted to improve the
requirement analysis process with the help of
proactive agents. Agents automatically sense the
requirement, environment and propose their own
set of important checklist which will be a better
and enhance list for requirement analysis. This is
a type of intelligent assistance with domain
heuristic ranks, which leads to cover all
important requirement entities of the problem
domain.
Proactive agents with requirement ranking
mechanism give a shot in the arm for
requirement analysis modules [7]. The
requirement gathering layout design and
attributes can be continuously improved. The
key attributes, which are important to the
system, are rearranged by ranked based

mechanism on repetitive usages by the agents.
This experiment was carried out in a simulated
environment for the requirement analysis in the
field of hospitality services using active agents
that have the capability of identification of the
most important attributes. The requirement is
gathered for 45 different rounds. The same
experiment was carried out on an environment
with no sensors for requirement attribute
alignment. The results obtained from the two
requirement processes were compared and it was
found that agent based requirement analysis
technique gather more valuable attributes for the
project.
4. ALGORITHMIC DETAILS
Environment::
Requirement analysis (History) Database is
Empty.
Agent activated:: Proactive/AI
Alignment:: False
Alignment path update:: False
Agent feedback: NULL
Agent activation::
The project requirement inputs on the basis of
the defined attribute for requirement gathering.

Proactive agent participation for the input
metadata and observation for the new attribute
value addition/Not-used attribute for
requirements.
Alignment check :: Proactive agent
Update the rank list of the attribute. Most used
will be incremented. Expert:: <- Human Input
also update the attribute list and rank list.
Simulation count incrimination
Proactive Agent::
To analyze which attribute is most valuable and
which one is least accessed.
Attribute ranking database updation:: To Access
the database and update if the item is most
accessed or if the item is specially recommended
by the analysis expert.
Synchronize the proactive agent with latest list
of requirement attribute’s Rank.
5. EXPERIMENTAL RESULTS
Following are the results of the experiments
carried out in order to achieve the enhancement
using agents.

ISSN 1947-5500
Table 1: User experience without agents.
Requirement
analysis
Attribute
Importance
Rank1
User
experience
W Agent
User
experience
Without
agent
Hos Req Attr
1 1 15 18
Hos Req Attr
2 2 18 18
Hos Req Attr
3 3 15 18
Hos Req Attr

4 4 17 20
Hos Req Attr
5 5 15 18
Hos Req Attr
6 6 21 21
Hos Req Attr
7 7 14 18
Hos Req Attr
8 8 15 17
Hos Req Attr
9 9 14 18
Hos Req Attr
10 10 17 18
Hos Req Attr
11 11 15 16
Hos Req Attr
12 12 17 18
Hos Req Attr
13 13 12 15
Hos Req Attr
14 14 14 18
Hos Req Attr
15 15 11 17
Hos Req Attr

16 16 15 18
Hos Req Attr
17 17 10 18
Hos Req Attr
18 18 15 20
Hos Req Attr
19 19 13 18
Hos Req Attr
20 20 13 18
Figure 1: User experience without agents.
Table 2: Stage-1: User experience improvements
using agents.
Requirement
analysis
Attribute
Importance
Rank2
User
experience
with agent
User
experience
Without
agent

Hos Req Attr
1 1 18 15
Hos Req Attr
2 2 18 18
Hos Req Attr
3 3 18 15
Hos Req Attr
4 4 20 17
Hos Req Attr
5 5 18 15
Hos Req Attr
6 6 21 21
Hos Req Attr
7 7 18 14
Hos Req Attr
8 8 17 15
Hos Req Attr
9 9 18 14
Hos Req Attr
10 10 18 17
Hos Req Attr
11 11 16 15
Hos Req Attr
12 12 18 17

Hos Req Attr
13 13 15 12
Hos Req Attr
14 14 18 14
Hos Req Attr
15 15 17 11
Hos Req Attr
16 20 18 15
Hos Req Attr
17 17 18 10
Hos Req Attr
18 18 20 15
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Hos Req Attr
19 19 18 13
Hos Req Attr
20 16 18 13
Figure 2: Stage-1: User experience
improvements using agents.

using agents.
Requirement
analysis
Attribute
Importance
Rank3
User
experience
with agent
User
experience
Without
agent
Hos Req Attr
1 1 24 15
Hos Req Attr
2 2 22 18
Hos Req Attr
3 3 21 15
Hos Req Attr
4 4 24 17
Hos Req Attr
5 5 21 15

Hos Req Attr
6 6 21 21
Hos Req Attr
7 7 22 14
Hos Req Attr
8 8 22 15
Hos Req Attr
9 9 21 14
Hos Req Attr
10 10 19 17
Hos Req Attr
11 11 24 15
Hos Req Attr
12 12 21 17
Hos Req Attr
13 13 21 12
Hos Req Attr
14 20 24 14
Hos Req Attr
15 15 23 11
Hos Req Attr 14 24 15
16
Hos Req Attr

17 17 24 10
Hos Req Attr
18 18 21 15
Hos Req Attr
19 19 24 13
Hos Req Attr
20 16 17 13
using agents.
Requirement
analysis
Attribute
Importance
Rank4
User
experience
with agent
User
experience
Without
agent
Hos Req Attr

1 1 32 15
Hos Req Attr
2 2 31 18
Hos Req Attr
3 3 32 15
Hos Req Attr
4 4 31 17
Hos Req Attr
5 5 32 15
Hos Req Attr
6 6 29 21
Hos Req Attr
7 7 28 14
Hos Req Attr
8 8 27 15
Hos Req Attr
9 9 32 14
Hos Req Attr
10 10 31 17
Hos Req Attr
11 11 31 15
Hos Req Attr
12 20 29 17
Hos Req Attr

13 13 29 12
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Hos Req Attr
14 12 32 14
Hos Req Attr
15 15 30 11
Hos Req Attr
16 14 32 15
Hos Req Attr
17 17 32 10
Hos Req Attr
18 18 32 15
Hos Req Attr
19 19 31 13
Hos Req Attr
20 16 32 13

using agents.
Requirement
analysis
Attribute
Importance
Rank5
User
experience
with agent
User
experience
Without
agent
Hos Req Attr
1 1 43 15
Hos Req Attr
2 2 40 18
Hos Req Attr
3 3 37 15
Hos Req Attr
4 4 36 17
Hos Req Attr
5 5 36 15
Hos Req Attr

6 6 43 21
Hos Req Attr
7 7 37 14
Hos Req Attr
8 8 39 15
Hos Req Attr
9 9 43 14
Hos Req Attr
10 10 40 17
Hos Req Attr
11 11 41 15
Hos Req Attr
12 13 39 17
Hos Req Attr
13 20 37 12
Hos Req Attr
14 12 40 14
Hos Req Attr
15 15 41 11
Hos Req Attr
16 14 42 15
Hos Req Attr
17 17 40 10
Hos Req Attr

18 18 43 15
Hos Req Attr
19 19 43 13
Hos Req Attr
20 16 43 13
using agents.
Requirement
analysis
Attribute
Importance
Rank6
User
experience
with agent
User
experience
Without
agent
Hos Req Attr
1 1 47 15
Hos Req Attr

2 2 46 18
Hos Req Attr
3 3 45 15
Hos Req Attr
4 4 44 17
Hos Req Attr
5 5 46 15
Hos Req Attr
6 6 46 21
Hos Req Attr
7 7 45 14
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8
Hos Req Attr
9 14 45 14
Hos Req Attr
10 10 44 17

Hos Req Attr
11 11 42 15
Hos Req Attr
12 20 43 17
Hos Req Attr
13 9 45 12
Hos Req Attr
14 14 44 14
Hos Req Attr
15 15 44 11
Hos Req Attr
16 16 44 15
Hos Req Attr
17 17 41 10
Hos Req Attr
18 18 41 15
Hos Req Attr
19 19 44 13
Hos Req Attr
20 12 43 13
using agents.

Requirement
analysis
Attribute
Importance
Rank7
User
experience
with agent
User
experience
Without
agent
Hos Req Attr
1 1 50 15
Hos Req Attr
2 2 51 18
Hos Req Attr
3 3 49 15
Hos Req Attr
4 4 49 17
Hos Req Attr
5 5 46 15
Hos Req Attr
6 6 47 21

Hos Req Attr
7 7 48 14
Hos Req Attr
8 14 48 15
Hos Req Attr
9 9 45 14
Hos Req Attr
10 10 46 17
Hos Req Attr
11 20 49 15
Hos Req Attr
12 11 49 17
Hos Req Attr
13 12 51 12
Hos Req Attr
14 8 50 14
Hos Req Attr
15 15 46 11
Hos Req Attr
16 16 47 15
Hos Req Attr
17 17 47 10
Hos Req Attr
18 18 46 15

Hos Req Attr
19 19 47 13
Hos Req Attr
20 13 50 13
using agents.
Requirement
analysis
Attribute
Importance
Rank8
User
experience
with agent
User
experience
Without
agent
Hos Req Attr
1 1 53 15
Hos Req Attr
2 2 52 18

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3
Hos Req Attr
4 4 50 17
Hos Req Attr
5 5 53 15
Hos Req Attr
6 6 54 21
Hos Req Attr
7 14 53 14
Hos Req Attr
8 8 52 15
Hos Req Attr
9 9 52 14
Hos Req Attr
10 10 51 17
Hos Req Attr
11 11 56 15

Hos Req Attr
12 20 52 17
Hos Req Attr
13 13 52 12
Hos Req Attr
14 7 52 14
Hos Req Attr
15 15 51 11
Hos Req Attr
16 16 52 15
Hos Req Attr
17 17 52 10
Hos Req Attr
18 18 52 15
Hos Req Attr
19 19 52 13
Hos Req Attr
20 13 53 13
Conclusion
The experiment results show the usage and
advantage of proactive agents in requirement
analysis phase. The involvement of agents adds

ranks to the requirement attributes. Over the
time, the requirements, which are more critical
to the domain, will become more important and
will appear first during requirement gathering.
Configuration also allows checking that whether
the requirements having top rank are considered
during requirement or not. The overall user
experience and requirement analysis
completeness will be more enhanced. This gives
an overall productivity to the software
development process and hence minimizes the
overall cost.
6. REFERENCES
[1] Michael Wooldridge, Nicholas R. Jennings
and David Kinny, “The Gaia Methodology
for Agent-Oriented Analysis and Design,”
Autonomous Agents and Multi-Agent
Systems, Kluwer Academic Publishers,
Netherlands, Vol 3, pp: 285-312, 2000.
[2] Kessler Bruno Fondazione, “Self adaptive
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http://se.fbk.eu/node/63
[3] Jaime Simão Sichman, François Bousquet
and Paul Davidsson, “Requirements
analysis of agent-based simulation
platforms: state of the art and new
prospects,”MABS'02 Proceedings of the
3rd international conference on Multi-
agent-based simulation, Springer-Verlag
Berlin, Heidelberg, ISBN: 3-540-00607-9,
pp: 125-141, 2003.

[4] Chiung-Hui Leon Lee and Alan Liu, "A
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[6] Wooldridge M. J. and Jennings N. R,
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[8] Jennings N. R.,” Building Complex Software
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65.
AUTHORS PROFILE

Dr. Ashok Kumar is working as Professor in Department of
Computer Science and Applications in Kurukshetra
University, Kurukshetra. He is having a vast experience in
teaching and research in the area of operation research,
software engineering, web based applications etc. He has
published numreous research papers in very reputed journals.
Mr. Vinay Goyal is working as Assistant Professor and Head of
Department (MCA) in Panipat Institute of Engineering and
Technology, Samalkha, Panipat. He has 8 years of teaching
and research experience in the area of AGENT ORIENTED
SOFTWARE ENGINEERING. He has published 4 research
papers in various international journals.
ISSN 1947-5500
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