International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 1 - Apr 2014
Fuzzy Based Approach for Predicting Software Maintainability
Vishal Choudhary#1 and Dr.P.K Bharti#2
Department of MCA
AIMT,Knowledge Park-3 Greater Noida-(India)
Abstract-Software maintenance is a process of
1. METRICS FOR MAINTAINABILITY
modifying existing operational software while leaving
Researcher have tried to quantify maintainability in
its primary functions intact. Software maintenance
different types of measures[12,13,14].here we consider
encompasses a broad range of activates, like error
four
correction, enhancement of capabilities, deletion of
maintainability,i.e average number of live variables,
obsolete
average life span of variables, average cyclomatic
ASSESSMENT
capabilities
and
optimization.sofwtare
maintainability assessment is major issue these days.
producing software that is easy to maintain may save
large costs in industries. the maintenance of existing
major
aspects
of
software
to
assess
complexity and comment ratio.
a)
AVERAGE NUMBER OF LIVE
VARIABLES(ALV):
a variable is live at a particular statement only if a certain
software can account for 70% of the total efforts putnumber of statements reference it before or after that
in application development[Pres05].the value of
statement. the average number of live variable is the sum
software can be enhanced by meeting additional
of the count of live variables divided by the count of
requirements, making it easier to use, improving
executable statements. the higher, the average number of
efficiency and employing newer technologies. this
paper discusses various issues and challenges, related
with the maintainability assessment of software
systems. The present work proposes a fuzzy logic
based approach for quantification of maintainability
of software system based on combined effect of four
major aspects of software. i.e average number of live
variables ,average life span of variables, average
cyclomatic complexity and the comment ratio.
Classroom projects are considered to estimate and
validate the proposed maintainability model.
live variables, the more difficult it would be to develop
and to maintain the software.
b) THE AVERAGE LIFE SPAN OF
VARIABLES(ALS):
The life span of a variable is defined as the number of
statements between two successive references of the
same variable. The average life span of variable is the
Ratio of the sum of life spans to number of variables.
C) COMMENT RATIO(CR):
The comment ratio is defined as
CR=(S+C)/C------------------- (1)
Where
S denotes total line of code
Keyword: Cylomatic complexity, comment ratio,
maintainability, triangular fuzzy number
ISSN: 2231-5381
C represents total number of comment
lines.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 1 - Apr 2014
A lower comment ratio means better readability and
the better maintainability.
D) AVERAGE CYCLOMATIC
COMPLEXITY (ACC):
The average cyclomatic complexity is defined as the
average cyclomatic complexity of all modules, where
cyclomatic complexity is defined as
V=e-n+2p----------------------- (2)
Where e is number of edges in a program flow
TABLE2: COMPLEXITY AND WEIGHT FOR
ALS
ALS
complexity
Weight
0-20
Low
W1
20-150
Medium
W2
150-400
High
W3
400 or more
Very high
W4
TABLE4: COMPLEXITY AND WEIGHT FOR ACC
ACC
0-5
5-13
13-18
18 or more
complexity
Low
Medium
High
Very high
Weight
W1
W2
W3
W4
graph,n is the number of nodes, and p is the number of
connected Components. Maintainability of software
declines with increasing average cyclomatic complexity.
TABLE5: COMPLEXITY AND WEIGHT FOR CR
ALS
complexity
Weight
0-4
Low
W1
4-8
Medium
W2
8-12
High
W3
12 or more
Very high
W4
2. FUZZY SET THEORY FOR
MAINTAINABILITY:
The set w1,w2,w3 and w4 may be different for program
Recently many researchers have proposed some
in different languages .
integrated models for maintainability measurement,
3. FUZZIFICATION:
which leave significant room for further improvements.
The complexity attributes low, medium and high for the
here we proposed a methodology to improve the
four metrics ALV,ALS,ACC,and CR are taken as
maintainability metrics system based on the fuzzy set
theory.ALV,ALS,ACC and CR are classified in four
levels of complexity i.e low, medium high and very high
triangular fuzzy number(TFN).membership functions are
evaluated using complexity and coefficient matrices.
fuzzy pictorial representation of TFNs for ALV,ALS
and corresponding weights are then assigned for each.
ACC and CR are shown in Figure:
The complexity levels and their corresponding weights
for ALV,ALS ACC and CRR are described in table 1
and 2
TABLE1: COMPLEXITY AND WEIGHTS FOR ALV
ALV
0-2
2-5
5-8
8 or more
Complexity
Low
Medium
High
Very High
ISSN: 2231-5381
weight
W1
W2
W3
W4
Fig1.Fuzzy Pictorial Representation of ALV
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 1 - Apr 2014
µ∗
<
≤
µ∗
+ ( − µ) ∗
,
<
≤ 20
µ∗
+ ( − µ) ∗
, <
≤ 85
Rals= µ ∗
+ ( − µ) ∗
, <
≤ 150
⎨
µ∗
+ ( − µ) ,
<
≤ 275
⎪
⎪µ ∗
+ ( − µ) ∗
,
<
≤ 400
⎩
> 400
⎧
⎪
⎪
Fig2.Fuzzy Pictorial Representation of ALS
µ∗
<
≤ .
⎧
µ∗
+ ( − µ) ∗
, . <
≤5
⎪
⎪ µ∗
+ ( − µ) ∗
, <
≤
Racc=
µ∗
+ ( − µ) ∗
, <
≤
⎨
µ∗
+ ( − µ) , <
≤ .
⎪
⎪µ ∗
+ ( − µ) ∗
, . <
≤
⎩
> 18
Fig3.Fuzzy Pictorial Representation of ACC
µ∗
<
≤
µ∗
+ ( − µ) ∗
, <
≤4
µ∗
+ ( − µ) ∗
, <
≤6
Rcr=
µ∗
+ ( − µ) ∗
, <
≤8
⎨
µ∗
+ ( − µ) , <
≤ 10
⎪
⎪µ ∗
+ ( − µ) ∗
, <
≤ 12
⎩
> 12
⎧
⎪
⎪
Fig4.Fuzzy Pictorial Representation of CR
4. Defuzzification:
Defuzzification process is applied to evaluate crisp value
Where µ represents grade of membership function. Since
maintainability of software is inversely proportional to
ALV,ALS,ACC and CR respectively. The following
equation used to evaluate maintainability
of maintainability factors for ALV,ALS,ACC and
Maintainability=Ralv*Rals*Rcrr*Racc
5. Experimental Results:
CRR.Ralv
Racc and Rcr are used to represent corresponding factors
In order to measure the performance of our system,we
of
have used a published dataset of nine classroom
maintainability
for
ALV,ALS,ACC
and
CR
repectively.Defuzzification rules are defined in the
projects[AGGA05].repair
rate
is
considered
for
following equations.
validation of results from present fuzzy logic based
model. Repair rate is taken as inverse of average
corrective maintainence time.
µ∗
⎧
µ∗
⎪
⎪ µ∗
Ralv= µ ∗
⎨ µ∗
⎪
⎪µ ∗
⎩
<
≤1
+ ( − µ) ∗
, <
≤2
+ ( − µ) ∗
, <
≤ 3.5
+ ( − µ) ∗
, . <
≤5
+ ( − µ) , <
≤ 6.5
+ ( − µ) ∗
, . <
≤8
>8
Repair rate=
Where Average CMT=average Corrective Maintenance
Time.we considered the following values for weight
factors for different levels of complexity.
W1=0.5,W2=0.4,W3=0.3 and W5=0.2
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 1 - Apr 2014
Table 6: Validation Results for Maintainability Assessment
Model.
P.NO
ALV
ALS
ACC
CR
1
2
3
4
5
6
7
8
9
2.5
1.58
4.41
3.95
2.32
3.14
3.14
5.86
6
132
43.8
238
292
118
288
141
298
300
11.5
12.6
5.28
13.7
7.43
10.7
9.37
7.0
10.7
7.74
5.62
8.30
8.80
7.32
9.23
6.89
7.00
8.80
Maintainability
0.083
0.149
0.115
0.066
0.135
0.071
0.088
0.076
0.056
Repair
rate
0.062
0.065
0.056
0.047
0.067
0.056
0.058
0.045
0.040
Variation of ALS with Reapir rate
350
300
250
200
150
100
50
0
1
2
3
Variation of ALV with maitainability
4
5
6
7
8
9
Project Number
7
ALS
Repair rate
6
Fig 6.Variation of ALS with repair
5
rate
Variation of CR with Repair rate
4
ALV
0.16
Maintainability
0.14
3
2
0.12
0.1
1
0.08
0
0.06
1 2 3 4 5 6 7 8 9 10
PROJECT NO.
0.04
Repair rate
CR
0.02
Fig 5:variation of ALV with maintainability
0
The graph of maintainability of nine projects
1
2
3
4
5
6
Project no.
7
8
under study from proposed model is compared
which shows a strong correlation between
Fig7:variation of CR with Reapir rate
integrated measure of maintainability with
maintenance time
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 1 - Apr 2014
7. REFERENCES
Maintainablity and Repair rate
1.A Mathematical Study of Fuzzy Logic Techniques in Software
0.25
0.2
Engineering
Measurements Er. Kailash Aseri et al./ International
Journal
Computer
of
Science
&
Engineering
Technology
(IJCSET).2012
0.15
Repair rate
0.1
2.Abrahamsson P., Moser R., Pedrycz W., Sillitti A., Succi G. (2007),
"Effort Prediction in Iterative Software Development Processes --
Maintainability
0.05
Incremental Versus Global Prediction Models", Proceedings of the
First International Symposium on Empirical Software Engineering and
Measurement (ESEM).
0
1 2 3 4 5 6 7 8 9
3. Engel, A., Shachar, S., Measuring and potimizing systems quality
Project No.
costs and project duration, systems engineering journal 9 (3), 259-280,
2006.
Fig8: comparison of maintainability and repair rate
4. Albrecht A.J. (1979), “Measuring Application Development
6. Conclusion:
Productivity,” Proc. IBM Applications Development Symp., SHARE-
In this paper a method is proposed for predicting the
Guide, pp. 83-92.
5.McCabe,T.,A complexity measure,IEEE Transactions on Software
maintainability of a software application based on the
engineering,2(4):308-320,1976.
combined effect of four important factors.ALV, ALS,
6.Banker,R.,Davis,G.and
slaughter,S.A.,Software
complexity
and
software maintenance effort: a field of study management science
ACC, and CR. This integrated approach gives a true
,44:433-450,1998.
Picture of the software maintainability. The model is
7.
validated on select software projects to check the
usefulness of the approach. the maintainability result of
Agarwal,
M.
(2007).
Fuzzy
logic
control
of
washing
machines.http://softcomputing.tripod.com/
8. G. D. Boetticher, “An assessment of metric contribution in the
construction of a neural network-based effort estimator”, Proceedings
the proposed model show excellent correlation with
repair rate, where as individual factors show a little
of Second International Workshop on Soft Computing Applied to
Software Engineering, 2001.
9. W. Pedrycz, H. F. Peters, and S. Ramanna, “A fuzzy set approach to
correlation
with
the
same.
Lower
value
of
cost estimation of software projects”, Proceedings of IEEE Canadian
Conference on Electrical and Computer Engineering, Alberta, Canada,
maintainability indicates the need for improvement in
software, so that the maintenance coast can be reduced.
1999.
10. M. W. Nisar, and Y.-J. Wang, and M. Elahi, "Software
Development Effort Estimation Using Fuzzy Logic – A Survey", in 5th
A crisp value for maintainability can help software
International Conference on Fuzzy Systems and Knowledge Discovery,
2008, pp. 421- 427.
managers to judge the maintenance efforts required for
various maintenance activities. further the measured
11. Geoffery K.Gill and chris F.Kemeren Cyclomatic Density and
software maintenance productivity,IEEE transaction on software
engineering.Vol 17,No.12 1284-1288.december 1991
value can be used to estimate changeability and
12.Berns,G.Assessing software maintainability communication of
ACM,27:14-13,1984.
reusability. The technique of fuzzy logic used in study is
sufficiently general and can be easily applied to other
13.Land R.,Measurements of Software maintainabaility,In proceeding
of ARTES Graduate Student conference ARTES,2002.
14.Aggarwal K.K singh&puri,measurement of software maintainability
areas of quantitative software engineering.
ISSN: 2231-5381
using Fuzzy model.journal of computer science,1(4) 538-542,2005.
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