USE OF FUZZY LOGIC IN SOFTWARE
ENGINEERING
A.P.C. Sudeera Jayasekara
WHAT IS FUZZY LOGIC?
Fuzzy - “not clear, blurred”
Fuzzy Logic is a form of approximate reasoning,
that can be used to represent variation or imprecision in logic,
by making use of natural language (NL) in logic.
NL: If it is sunny and warm today, drive fast
Traditional approach
Fuzzy logic representation
Fuzzification
boolean sunny;
boolean warm; // 70 F °
get the sunny;
get the warm;
if (sunny AND warm) {
// speed is fast : 80mph
}
else {
// speed is slow: 25 mph
}
Fuzzy sets
Defuzzification
65 F° → Cool = 0.4, Warm= 0.7
Speed is 20% Slow and 70% Fast
Speed = 63.8 mph
(weighted mean)
25% Cover → Sunny = 0.8, Cloudy = 0.2
Rules
WHAT IS SOFTWARE ENGINEERING?
Software engineering is an engineering is concerned with all aspects of
software production
Sub disciplines in software engineering are,
Project Management – the knowledge, techniques, and tools necessary to manage the
development of software products
Requirements Engineering – discovery , analysis, specification, and validation of
requirements of software products
Design – partitions the software into conceptual components and specifies their behavior
Development – construction of software products through the use of programming
languages
Testing– checking that the software is defect free and according to requirement specification
Deployment, Maintenance & Evolution – running the software and perform changes and
enhancements
WHY FUZZY LOGIC & SOFTWARE ENGINEERING?
Topic is an area of current research
Human
communication
50%
50% Software
engineering is
communication
Large number
of linguistic
quantifiers
linguistic
variables
Qualitative:
good, bad, best,
worse
Quantitative:
around 20, nearly
5, below 7
Many links in
the
communication
chain
human
resources
analysts,
developers,
testers,
business
stakeholders, and
end users
Alteration thresholds,
round off
logical
states
Converted to
Non linguistic
variables
classical two
valued logic
Significant loss
of essential
information
failure
most software
projects fail
Fuzzy logic could address the above problem because it can
incorporate linguistic variables such that uncertainties can be modeled
FL IN REQUIREMENT ENGINEERING & DESIGN
Using FL in Object Oriented Analysis – OOA is composed of a population of
interacting objects. Classes, attributes, operations are some object-oriented artifacts
Rule of Candidate Class identification
IF an entity in a requirement speciation is relevant
AND can exist autonomously in the application domain
THEN select it as a candidate class
Classical OOA rules
adopts only
two quantization levels
0 or 1
“Software engineer needs to decide whether an entity in a requirement speciation is relevant or not ”
However it can be perceived that an entity partially fulfills the relevance criterion, and conclude that
the entity is substantially relevant, which cannot be represented by classical logic.
Classical Formulation
A quantization error occurs which is the difference between the perception of the software engineer
and the quantization levels imposed by the two-valued logic-based rules.
quantization error when N=2 V=1
FL IN REQUIREMENT ENGINEERING & DESIGN (cont.)
Fuzzy Logic Formulation
Define linguistic variables
Relevance - relevance value
Autonomy - autonomy value
Candidate Class – candidate class relevance value
Autonomy
Dependent
Partially
dependent
Fully
Weakly
Weakly
Weakly
Weakly
Slightly
Weakly
Slightly
Slightly
Fairly
Weakly
Slightly
Fairly
Substantially
Weakly
Fairly
Substantially
Strongly
Slightly
Fairly
Strongly
Relevance
Each element of the table represents the output value
of a sub-rule, which is the relevance value of the
candidate class being considered.
As the quantization levels increases the
quantization error decreases
FUZZY LOGIC IN PROJECT MANAGEMENT
Problem faced in project management is the elicitation of numerical inputs
Obtaining these with any degree of confidence early in a project is not always
feasible
Related to this difficulty is the risk of precisely specified outputs from models
leading to over promise.
These problems can be seen as the collective failure of software
measurements to represent the inherent uncertainties
Benefit of fuzzy logic is the flexibility available in terms of the types of input
and output variables
Input variables can be expressed as
simple fuzzy labels - a large number of entities in the data model,
fuzzy numbers - about 250 entities,
or using precise values - 265 entities.
Similarly, the output variables can be expressed in the same way,
as a label - a short development time,
fuzzy number - about 400 developer-hours,
or precise values - 378 developer hours.
FUZZY LOGIC IN PROJECT MANAGEMENT (cont.)
Fuzzy Logic could be used in size estimation which can be done in advance to
make better plans, and assist in tracking progress
1.
2.
3.
4.
1
Concept of fuzzy logic in size estimation
Gather size data on previously developed programs
Divide the historical product size data into size ranges
Compare the planned product with these prior products
Based on this comparison, select the size that seems most appropriate for the new
product
3
A file utility of 1,844 LOC
log(1844)
A file management program of 5,834 LOC
log(5834)
A personnel record keeping program of 6,845 LOC log(6845)
A report generating package of 18,386 LOC
log(18386)
An inventory management program of 25,943 LOC log(25943)
=3.266
=3.766
=3.835
=4.2645
=4.414
2
3.122 3.266 3.409 3.553 3.696 3.840 3.983 4.127 4.270 4.414 4.557
4
The new program is substantially more
complex application than either the file
management or personnel programs. It is
not as complex as the inventory
management program and appears to
have significantly more function than the
report package.
conclude that the new program is in the lower end of very large or from 18 to 25 KLOC
Limitations
•
•
•
•
Requires a lot of data
The estimators must be familiar with the historically developed programs
Not useful for new program types
Not useful for programs much larger or smaller than the historical data
FUZZY LOGIC IN IMPLEMENTATION
Fuzzy Logic could be used to evaluate reusability of Components
A component is a reusable, self contained piece of software with well
specified interface that is independent of any application
To estimate reusability of Components, a relationship of the factors with reusability
needs to be considered, the following are such factors
Customizability
Interface Complexity
Understandability
Commonality
Portability
These combined factors
can be used to measure
the reusability by using
fuzzy logic
CONCLUSION
Uncertainty is inherent and inevitable in software development processes
So, software uncertainties should be modeled and managed explicitly
Fuzzy logic is well suited to a life-cycle approach to software development process with
its ability to represent differing levels of uncertainty for inputs and outputs
The ensuing consistency, effective communication, and economy make this an
attractive modeling technique for such applications as effort estimation, requirement
engineering and testing
REFERENCES
Shradhanand, Amarjeet Kaur, Dr. Satbir Jain
Use of Fuzzy Logic In Software Development
In Issues in Information Systems (Volume VIII, No. 2, 2007) pages 239-244
2007
Francesco Marcelloni, Mehmet Aksit
Fuzzy logic-based object-oriented methods to reduce quantization error and
contextual bias problems in software development
In Fuzzy Sets and Systems (2004) pages 57–80
2004
Vidhu Bhardwaj, Dr. Rajesh Kumar
Estimating Reusability of Software Components Using Fuzzy Logic
MSc. Thesis
2010
QUESTIONS
THANK YOU