CONTENTS

DECISION SUPPORT SYSTEM FOR SOFTWARE MAINTENANCE AND EVOLUTION 183
of the multiagent system: Software developers (human agents); Personal Assistant
Agents (PA); Decision Support Agents (intelligent mobile agents - DSA).
The system that we propose in this paper is a distributed system consisting of
some local nodes, where software developers are assisted by DSSEM in activities
related to software maintenance and evolution (as presented in Section 2), and a
central location that supervises the local nodes.
We assume that at each local node, the human agent (software developer) use
his/her favourite IDE (Integrated Development Environment) for developing a software
application SA. At each local node, a personal assistant agent PA is also available.
Each time a software developer needs assistance in a task related to refactoring,
design pattern identification, or other tasks supported by DSSEM (see Section 2) PA
agent is activated. PA is a software agent that is responsible for interacting with the
IDE in which the user develops its application.
On the central location six kinds of decision support agents exist: an agent responsible
for design patterns identification, one responsible for concept location, one
responsible for crosscutting concerns identification, one responsible for introducing
design patterns, one responsible for refactoring, and one responsible for the behavioral
adaptation of the system. All of them are sleeping until they receive a specific
message and they become activated. After a software developer authenticates on a
local node and request assistance for an activity, its personal assistant agent PA sends
an awakening message to the coresponding decision support agent.
Once activated, an instance of the corresponding decision support agent DSA
migrates to the local node, where its interaction with the personal assistant agent
PA starts (DSA agent receives the information that PA agent has collected about the
software application SA). After the task of DSA agent is completed, it sends the
results to PA agent that will communicate the information to the software developer.
After this, DSA migrates back to the central location. DSA are intelligent agents
that accomplish their tasks by using machine learning [1] techniques.
We can conclude that the advantages of the DDSEM system proposed in this
paper are: (1) It benefits from the advantages that the agent based technology offers
- especially autonomy and flexibility [5]. (2) It offers protection of confidential information
by using the mobile agent technology. Maintaining security [6] implies that
the software developer’s intelectual property (source code) might be highly sensitive
data that should not leave its location. In such cases, the mobile-agent model [7] offers
a solution: a mobile agent is sent to the local node, and at the end of the process the
mobile agent is destroyed on the location itself. (3) DSSEM system can be simply
adapted to any IDE and/or programming languages (such as Eclipse, Visual Studio;
Java, C++), only by changing the behavior of PA.
We have implemented a prototype of DSSEM system, in which the functionality of
refactorings identification is added. In this direction, RDSA agent was developed, i.e.,
an instance of DSA agent that is responsible for identifying refactorings that improve
the structure of a software system. For refactorings identification we use a clustering
approach that we have previously introduced in [8]. The identified refactorings are
suggested to the software developer, that can decide if the refactorings are appropriate.