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 Authors : Rahul Kumar Yadav, Dr. S. Niranjan
 Paper ID : IJERTV2IS50762
 Volume & Issue : Volume 02, Issue 05 (May 2013)
 Published (First Online): 22052013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
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Software Effort Estimation Using Fuzzy Logic: A Review
Rahul Kumar Yadav Mewar University Gangrar, Chhittorghara, India
Dr. S. Niranjan
PDM School of Technology & Management, Bahadurgarh, India.
Abstract
One of the major problems with software project management is the difficulty to predict accurately the required effort for developing software applications. This is due to the reason that most of the software estimates should be performed at the beginning of the life cycle, when we do not yet know the problem we are going to solve. The task of effort estimation is challenging and is an important area of research in the field of Software Project Management. Though a number of estimation models exist for effort prediction, still many newer models are being proposed and active research is going on to obtain more accurate estimation models. In this paper we survey the most common and widely used effort estimation techniques using fuzzy logic. The survey shows that fuzzy logic effort estimation can be coupled with other techniques such as neural network, Bayesian Network and Particle Swarm Optimization technique. Recent trends on effort estimation have also been discussed at length.
Keywords Software Development Effort, Effort Estimation, Fuzzy Logic Techniques, Estimation Models.

Introduction
It is ideally desirable that the improvement in estimation techniques currently available to project managers would facilitate increased control of time and overall cost benefit in software development life cycle. Furthermore, any improvement in the accuracy of predicting the development effort can significantly reduce the costs from errors, such as estimating inaccurately, inappropriate tendering bids, and disabling the monitoring progress. Software
development effort estimates are the basis for project bidding and planning. The consequences of poor budgeting and planning can be disastrous: if they are too pessimistic, business opportunities can be gone astray, while optimism may be followed by significant loss. Software effort estimation has even been identified as one of the three most demanding challenges in software application areas [1]. During the development process, the cost and time estimates are useful for the initial rough validation and monitoring of the projects completion process. And in addition, these estimates may be useful for project productivity assessment phases. Software effort estimation models are divided into two main categories: viz., algorithmic and nonalgorithmic. The most popular algorithmic estimation models include Boehms COCOMO [2], Putnams SLIM[3] and Albrechts Function Point[4]. Nonalgorithmic techniques include PricetoWin [1],Parkinson [1], expert judgment [1] and machine learning approaches[5]. Machine learning is used to group together a set of techniques that embodies some of the facets of human mind [5]. For example, fuzzy systems, analogy, regression trees, rule induction and neural networks are among the machine learning approaches, and fuzzy systems and neural networks are considered to belong to the soft computing paradigm.

Algorithmic models
Some of the famous algorithmic models are: Boehms COCOMO81, II (Boehm et al., 2000), Albrechts Function Point (Boehm et al., 2000; Boehm, 1995) and Putnams (1978) SLIM. All of them require inputs, accurate estimate of specific attributes, such as Line of Code (LOC), number of user screen, interfaces and complexity, which are not easy to acquire during
the early stage of software development life cycle process. Models based on historical data have limitations. Understanding and the calculation using these models are difficult due to inherent complex relationships between the related attributes, which are unable to handle categorical data as well as lack of reasoning capabilities [6]. Besides, attributes and relationships used to predict software development effort those could change with the passage of time and/or differ for software development environments (Srinivasan and Fisher, 1995). The limitations of the algorithmic models led to the exploration of the nonalgorithmic techniques visualised through soft computing philosophy.

NonAlgorithmic models
In 1990s nonalgorithmic model was conceptualized and have been proposed to project cost estimation. Software researchers have turned their attention to new approaches those are based on soft computing methodologies such as based on artificial neural networks and fuzzy logic models and genetic algorithms based implementations. Neural networks are able to generalize from trained data set. A set of training data, a specific learning algorithm makes a set of rules that fit the data and fits previously unseen data in a rational manner as well. Some of the early works show that neural networks are adequately applicable to cost estimation phases as presented in the works of Venkatachalam [7] and Krishna and Satsangi [8]. Fuzzy logic offers a powerful linguistic representation that is sufficiently accommodate the imprecision in inputs and outputs, while providing a more realistic knowledge based approach to model building. Contemporary research establishes to some extent that fuzzy logic model achieved good performance index, being outperformed in terms of accuracy only by neural network model with considerably more input variables. Hodgkinson and Garratt in their works presented that estimation by expert judgment was better than all regression based models [9].

Fuzzy logic models
A fuzzy model is used when the systems are not suitable for analysis by conventional approach or when the available data is uncertain, inaccurate or vague [10]. The fuzzy model uses the fuzzy logic concepts introduced by Lofti A. Zadeh [11]. Fuzzy reasoning consists of three main components [12]: fuzzification process, inference from fuzzy rules and defuzzification process. Fuzzification process is where the objective term is transformed into a fuzzy concept. The membership functions are applied to the actual values
of variables to determine the confidence factor or membership value (MV). Fuzzification allows input and output to be expressed in linguistic terms. Inferencing involves defuzzification of the conditions of the rules and propagation of the confidence factors of the conditions to the conclusion of the rules. Defuzzification process refers to the translation of fuzzy output into objective terms.
A system based on Fuzzy Logic has a direct relationship with fuzzy concepts (such as fuzzy sets, linguistic Variables etc.) and fuzzy logic. The popular fuzzy logic systems can be categorised into three types: pure fuzzy logic systems, Takagi and Sugenos fuzzy system, fuzzy logic systems with fuzzification and defuzzification [12]. Since most of the engineering applications produce crisp data as input and expects crisp data as output, the last type i.e., fuzzy logic system with fuzzification and defuzzification is most widely used one and was first proposed by Mamdani. It has been successfully applied to a variety of industrial processes and consumer products [12].
1.3.1 Fuzzy Logic in Software Effort Estimation
A fuzzy set theoretic model is a modelling construct featuring two main properties [13]: (1) It operates at a level of linguistic terms (fuzzy sets), and (2) it represents and processes uncertainty. Fuzzy logic offers a particularly convenient way to generate a keen mapping between input and output spaces thanks to the natural expression of fuzzy rules. In software development effort estimation, two considerations justify the deciion of implement–ing a fuzzy model:1) it is impossible to develop a precise mathematical model of the domain [14]; second, metrics only produce estimations of the real complexity. Thus, according to the previous assertions, formulating a tiny set of natural rules describing underlying interactions between the software metrics and the effort estimation could effortlessly reveal their intrinsic and wider correlations.


Review of Software Estimation Based On Fuzzy Logic Techniques
During the last decade, many methodologies have been developed in the areas of software cost estimation for improving estimation accuracy. Here we present a tabular view (Table 1) of works of various authors on software development effort estimation based on Fuzzy Logic techniques and concepts.
Table 1. Research on Software Development Effort Estimation Based On Fuzzy Logic Techniques.
Authors
Year
Related Work Done
Result Reported
Fei Z and Liu X
1992
Introduced the fCOCOMO model
Since there was no comparison of
[15] which applied Fuzzy Logic to the COCOMO model for software effort
results between the fCOCOMO and other effort estimation models in their
estimation.
study the estimation capability of their
model is unknown.
S. Kumar, B.A.
1994
Had applied fuzzy logic in
The w o r k s h o w e d h o w fuzzy
Krishna and P.S.
Putnams manpower buildup index
F A M s can be effectively applied to
Satsangi [16]
( MBI) estimation model. MBI
the domain of software project
selection process was based
management and control for the
upon 64 different fuzzy associative
estimation of the MBI.
memory (FAM) rules.
Gray and MacDonell [17]
1997
Compared Function Point Analysis, Regression techniques, feedforward neural network and fuzzy logic in software effort estimation.
Their results showed that fuzzy logic model achieved good performance, being outperformed in terms of accuracy only by neural network model with considerably more input variables.
Gray and
1999
Developed FULSOME (Fuzzy
The automatically generated fuzzy
MacDonell [18]
Logic for Software Metrics) which is a
model performs acceptably when
set of tools that helps in creating fuzzy
compared to regressionbased models.
model.
J. Ryder [19]
1998
Researched on the application of fuzzy logic to COCOMO and Function Points models.
Result showed Fuzzy Logic is good at making effort estimations.
P. Musflek, W.
2000
Worked on fuzzifying basic COCOMO
They concluded that (a) fuzzy sets help
Pedrycz, G. Succi
model without considering the
articulate the estimates and their
and M. Reformat
adjustment factor. In their simple f
essence (by exploiting fuzzy numbers
[20] COCOMO model, the size input into the
described by asymmetric membership
COCOMO model is represented by a
functions) and (b) they generate a
fuzzy set, while a and b coefficients are
feedback as to the given uncertainty
crisp values. Besides the size,
(granularity) of the results.
augmented f COCOMO also fuzzified
both the coefficients related to the
developm ent mode.Triangular memb
ership functions are used in this
study.
A.Idri, A. Abran,
2000
Proposed fuzzy intermediate
Validation results showed that the
L. Kjiri [21]
COCOMO'81. The FLM is based upon
fuzzy intermediate COCOMO81 can
trapezoidal membership functions. The
tolerate imprecision in its input (cost
dataset is randomly generated and
drivers) and generate more gradual
compared with actual data of
outputs. Thus fuzzy intermediate
COCOMO 81. The effort multiplier for
COCOMO81 is less sensitive to the
each cost driver is obtained from fuzzy
changes in the inputs as compared
set, enabling its gradual transition from one interval to a contiguous interval such as from high to very high).
to intermediate COCOMO81.
A. Idri, and A.
2002
Proposed an approach based on
Taking into account their results,
Abran[22]
fuzzy logic named Fuzzy Analogy. Its
they suggested the following ranking of the
dataset is that of COCOMO 81.
four techniques in terms of accuracy and
adequacy to deal with linguistic values: 1.
Fuzzy Logic, 2. Fuzzy intermediate
COCOMO81, 3.Classical intermediate
COCOMO81, and 4. Classical Analogy.
Huang, X.,
2003
Proposed a model combining fuzzy
The results of the fuzzy logic model
Capretz. L.F.,
logic and neural networks. The
were better than those of the COCOMO
Ren, J., Ho. [23]
dataset was obtained from the
equations. The FLM was based
original COCOMO (1981).
upon triangular membership functions.
The main benet of this model is its
good interpretability by using the fuzzy
rules.
M.O. Saliu, M.
2004
They fuzzyfied the two different
This approach is able to deal with
Ahmed and J.
portions of the intermediate COCOMO
uncertainty, provides transparency on
AlGhamdi. [24]
model i.e. nominal effort estimation
prediction rationale through rules,
and the adjustment fac tor . They
incorporate experts knowledge in the
p rop os ed a fu z z y logic framework
definition of membership functions and
for effort prediction by integrating the
rules, as well as adaptable to new data
fuzzified nominal effort and th
by changing the parameters of
fuzzified effort multipliers of
membership functions.
theintermediate COCOMO model.
Ahmed, M.A.,
2004
Presented a FLM based upon
Results showed that the FLM was
Saliu, M.O. and
triangular membership functions.
slightly better than COCOMO equations.
AlGhamdi, J. [25]
The dataset for validating the
In addition, they reported promising
FLM was (a) generated randomly
experimental summary results in
and (b) that of COCOMO 81 was
spite of the little background knowledge
used.
of the rule base and training data.
Crespo, F.J.,
2004
Explored fuzzy regression techniques
Fuzzy regression is able to obtain
Sicicila, M.A.,
based upon fuzzication of input
estimation models with similar predictive
Cuadrado, J.J.
values. Project database of COCOMO
properties than existing basic estimation
[26] 81 are used.
models.
M.R. Braz, S.R.
2004
Applied Fuzzy Logic for effort
Results showed that FUSP fares
Vergilio. [27]
estimation of objectoriented software.
better than USP.
FUSP (Fuzzy use case size
points) metric allows gradual
classifications of use case size
points in the effort estimation by using
fuzzy numbers.
Xu and
2004
Presented a fuzzy identication cost estimation modelling technique to deal with linguistic data, and
It was observed that the fuzzy identication model provided signicantly better cost estimations than the three
Khoshgoftaar [28]
automatically generate fuzzy membership functions and rules. A case of study based on the COCOMO81 database compared the proposed model with all three COCOMO81 models (basic, intermediate and detailed).
COCOMO81 models.
L.M.Cuauhtemoc,
Y.M. Cornelio and G.T.Agustin.
[29]2006
Carried out a study to compare personal Fuzzy Logic Systems (FLS) with linear regression using evaluation criteria which is based upon ANOVA of MRE and MER, as well as MMRE, MMER and pred(25)
Results show that a FLS can be used as an alternative for estimating the development effort at personal level.
Moon Ting Su,
2007
Proposed an enhanced fuzzy logic
The analysis of the results shows that
Teck Chaw Ling,
model for the estimation of software
FLECE is able to obtain more accurate
Keat Keong
development effort. The model Fuzzy
results in the estimation of software
Phang, Chee
Logic Model for Software
development effort when compared
Sun Liew and
Development Effort and Cost
to the previous fuzzy logic model.
Peck Yen Man
Estimation (FLECE) possesses
Hence, the enhancements to FLECE
[30] similar capabilities as the previous
are truly useful and had given better
fuzzy logic model. In addition to that,
performance to the model.
the enhancements done in FLECE
improved the empirical accuracy of
the previous model in terms of MMRE
(Mean Magnitude of Relative Error)
and threshold oriented prediction
measure or prediction quality (pred).
Venus Marza,
2008
Hybrid neurofuzzy technique is used
The results showed that neurofuzzy
Amin Seyyedi,
for development time and is validated
system is much better than two other
and Luiz
with gathered data.
mentioned methods (fuzzy logic and
Fernando
neural network separately).Hence, In
Capretz[31]
order to achieve more accurate
estimation, several techniques maybe
combined.
Parvinder S.
2008
NeuroFuzzy technique is used for
The performance of the Neurofuzzy
Sandhu, Porush
software estimation of NASA software
based effort estimation Model and the
Bassi, and
project data and performance of the
other existing Halstead Model, Walston
Amanpreet Singh
developed models are compared with the
Felix Model, BaileyBasili Model and Doty
Brar[32]
Halstead, WalstonFelix, BaileyBasili
Model models is compared for effort
and Doty Models
dataset .The results show that the Neuro
fuzzy system has the lowest MMRE and
RMSSE values.
Iman Attarzadeh and Siew
2009
Proposed an enhanced Fuzzy Logic approach for the estimation of software development effort.
Results s h o w e d t h a t t h e v a l u e of M M R E applying their Fuzzy Logic model was substantially lower than MMRE values
Hock Ow [33]
as calculated by applying other Fuzzy Logic models.
Ch. Satyananda Ready, KVSVN Raju[34]
2009
The proposed work is based on COCOMO dataset and the experimental part of the study illustrates the approach using Gaussian membership function
Result showed the proposed model gives more precise result than that of using the TMF. Thus by using GMF, the accuracy of effort estimation can be improved and the estimated effort can be very close to the actual effort.
2010
Proposed an approach combining the neurofuzzy technique and the SEER
Results shows that that combining the neurofuzzy model with the SEERSEM
Wei Lin Du, Danny Ho, Luiz Fernando Capretz[35]
SEM effort estimation algorithm and evaluate the prediction performance of the proposed neurofuzzy model with SEERSEM in software estimation practices.
effort estimation model produces unique characteristics and performance improvements. Results also proves that the proposed neurofuzzy structure can be used with other algorithmic models besides the
COCOMO model.
Abou Bakar Nauman, Romana Aziz[36]
2011
This paper proposes a simple Bayesian Network (BN), based on classification approach. The classes of ranges of size value are distributed with help of fuzzification to distribute the probability of crisp value.
The proposed model shows two specific achievements. 1). Model shows that a smaller Bayesian network can be developed to achieve intelligent effort estimates. 2). The classifications of sizes can be managed with the help of fuzzy logic.
Prasad Reddy P.V.G.D, Sudha
K. R, Rama Sree [37]
2011
Software development effort predicted using Fuzzy Triangular Membership Function and GBell Membership Function is implemented and compared with COCOMO using NASA93 dataset.
Results shows that software effort estimation using Fuzzy method with TMF (triangular membership function) is
better than Fuzzy method using GBellMF or Intermediate COCOMO. It is not possible to evolve a method, which can give 100 % VAF. By suitably adjusting the values of the parameters in FIS we can optimize the estimated effort.
A.BalaKrishna, T.K.Rama Krishna[38]
2012
The propsed work is to employ Particle Swarm Optimization for tuning the effort parameters, fuzzy logic for reducing uncertainty in input and test its
Results shows that the proposed model reduce the uncertainty in the input sizes by using fuzzy logic and by lining the parameters of the cost model using PSO
suitability for software effort estimation.
with inertia weight in order to generate an
This methodology is then tested using
optimal result. The model was proved to be
NASA dataset provided by Boehm. The
efficient on the basis of VARE, MARE and
results are then compared with the models such as BailyBasili, Alaa F.
VAF after comparing with the models such as BailyBasili, Alaa F. Sheta, and Harish
Sheta, and Harish models.
models.

Conclusion
Although many researchers contributed on cost/effort estimation, still many issues on cost/effort estimation remain unresolved. In this paper we presented a review on the Fuzzy Logic applications in Software development effort estimation models development. We also discussed the various advantages of Fuzzy Logic for developing prediction models. In order to achieve more accurate estimation, voting the estimated values of several techniques and combine their results maybe be useful. Further results can explore using four fuzzy logic membership functions Fuzzy Triangular Membership Function, GBell Membership Function, Gauss Membership Function and Trapezoidal Membership Function and their results will be compared with other estimation models and actual data set of the project. The fuzzy logic models for effort estimation can be deployed on COCOMO II environment for creating an appropriate expert system for providing required information for developing fuzzy sets and an appropriate rule base.

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