Implementation of reducing features to improve code change based bug prediction by using cos triage algorithm

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 11 | Nov-2014, Available @ http://www.ijret.org 429
IMPLEMENTATION OF REDUCING FEATURES TO IMPROVE CODE
CHANGE BASED BUG PREDICTION BY USING COS-TRIAGE
ALGORITHM
Veena Jadhav1
, Vandana Gaikwad2
, Netra Patil3
1
Student, Computer Engineering Department, BVDUCOE Pune-43, Maharashtra, India
2
Assistant Professor, Computer Engineering Department, BVDUCOE Pune-43, Maharashtra, India
3
Assistant Professor, Computer Engineering Department, BVDUCOE Pune-43, Maharashtra, India
Abstract
Today, we are getting plenty of bugs in the software because of variations in the software and hardware technologies. Bugs are
nothing but Software faults, existing a severe challenge for system reliability and dependability. To identify the bugs from the
software bug prediction is convenient approach. To visualize the presence of a bug in a source code file, recently, Machine
learning classifiers approach is developed. Because of a huge number of machine learned features current classifier-based bug
prediction have two major problems i) inadequate precision for practical usage ii) measured prediction time. In this paper we
used two techniques first, cos-triage algorithm which have a go to enhance the accuracy and also lower the price of bug
prediction and second, feature selection methods which eliminate less significant features. Reducing features get better the quality
of knowledge extracted and also boost the speed of computation.
Keywords: Efficiency, Bug Prediction, Classification, Feature Selection, Accuracy.
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1. INTRODUCTION
Classifiers such as SVM and navies byes are accomplished
on previous software project information, also it can be
expended to estimate the presence of a bug in an specific
record in software, as complete in previous work[2].
To fix bug cos-triage algorithm is used. Afterward that data
is added in historical data or in log record. Afterward,
classifier is accomplished on data originate in previous log
record. Then, it can be expended to categorize a renewed
variation is either buggy or clean.
Now days, The Eclipse IDE contains a prototype displaying
server which is used to compute bug predictions [4]. It can
aslo offer accurate predictions. Bug prediction system must
offer a lesser number of incorrect variations that are
predicted to be pram but which are very new, if software
developers are to belief [3]. Suppose, if enormous numbers
of improved variations are incorrectly estimated to be pram,
engineers won’t have belief in the bug prediction.
Previous bug prediction methodology and identical work
offered by scientist which employ the removal of “features”
from the prior of variations created to a software system[2].
These features comprise completely distributed by blank
space in the code and that all were included or rejected in a
variation. Henceforth, to prepare the SVM classifier entirely
variables, comment words, numerical operatives, name of
methods, and development language keywords are used as
features which is existing in this paper.
Cost of large feature set is tremendously high. Because of
multiple interactions and noise classifiers cannot handle
such an enormous feature set. As well as number of features
increases time also increases and expanding to a number of
seconds per categorization for tens of thousands of features,
and minutes for enormous project data accounts.
Henceforth, this will influences the availability of a bug
prediction scheme.
There are several classification approaches could be
working. But in this paper we used cos-triage algorithm and
SVM.
1) This paper contributes three aspects:
2) Study of various feature selection methods to
categorize bugs in software program variations.
3) Use of cos-triage algorithm to increase accuracy and
reduce cost of bug prediction.
The rest of this paper is prepared as follows: In Section 2
primary steps involved in performing change classification
is presented. Also this section discuss about feature selection
techniques in more detail. Section 3 discuss prior work.
Section 4 contains system overview for proposed system.
Finally a conclusion is made in section 5.
2. CHANGE CLASSIFICATION PROCESS
Following steps are concerned in executing Various
categorization on distinct project. Creating a Corpus:
1) Change deltas are mining from the log records of a
project, as kept in its SCM repository.

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2) For each file bug fix variations are known by observing
keywords in SCM variation log messages.
3) At the record level without bug and buggy variations are
known by finding reverse in the review log record from bug
resolve.
4) Features contain both buggy and clean and that mined
from all variations. In Complete source code all expressions
are considered as features. Features are nothing but the lines
fitted in each variation, meta-data such as author, time etc.
we can calculate complexity metrics at this stage.
5) In this paper to compute a reduced set of features
combination of wrapper and filter approaches are used. In
this paper four filter based rankers are used. i) Relief-F. ii)
Chi-Squared iii) Gain Ratio iv) Significance. The wrapper
methods are depends on the SVM classifiers.
6) Classification model is accomplished by using reduced
set.
7) Skilled classifier is set to use. Whether a original
variation is more analogous to a buggy variation or a clean
variation is proven by classifier.
2.1 Detection of Buggy and Clean Changes
For bug prediction training data is fixed and used. Mining
variation log records is used to determine bug introducing
variations and to identify bug fixes. First is Examining for
keywords in old revision such as “Set” “Bug” or other
keywords possible to develop in a bug repair.second,
Examining for another situations to bug.
2.2 Feature Extraction
With the help of support vector machine algorithm, using
buggy and clean variations a classification model must be
accomplished which is used to consolidate software
variations.
The entire word in the source code programme distributed
with the help of blank space or a semicolon which is used as
a article .Example of that is method name, keyword,
variable name, comment word, function name and operator.
2.3 Feature Selection Techniques
Huge feature groups need extended working out and
estimation periods, also need lot of memory these are the
requirement to execute classification.
Feature selection is common solution to this problem In
which only the subcategory of structures that are generally
suitable for creating grouping outcomes are truly used.
2.4 Feature Selection Process
A repetitive process of choosing increasingly fewer
important group of features is finished by using Filter and
wrapper approaches.
This process starts by splitting the primary feature set in
partial which reduces processing requirements and memory
for the remaining process.
A classification model is accomplished by using the reduced
feature set. Whether a new variation is more associated to a
buggy variation or a clean variation is decided by classifier.
3. RELATED WORK
A model proposed by scientist Khoshgoftaar and Allen to
group modules corresponding to software quality factors
such as upcoming defect intensity using different phases of
multi-regression[5][6][7]. Ostrand et al. discovered two
model i) The top 20% of problematic records in a project
[11] using upcoming defect predictors ii) A linear regression
model
Totally Ordered Program Units could be transformed into a
half ordered suite list, e.g. by giving the topmost N% of
units as offered by Ostrand et al. Hassan and A caching
algorithm to compute the set of fault-prone modules, called
the topmost-10 list offered by halt [9]. The bug cache
algorithm to envisage upcoming faults built on preceding
fault areas offered by Kim et al.
Fault sessions of the Mozilla project through numerous
releases offered by Gyimothy et al. [11]. With the help of
decision trees and neural networks that use object-oriented
metrics as features. Six different feature selection techniques
when using the Naive Bayes and the C4.5 classifier
considered by Hall and Holmes [12] .There are about 100
features in each dataset. Hall and Holmes launched many of
the feature selection methods.
A general defect prediction framework which encompass a
data pre-processor, feature selection methods, and machine
learning algorithms elaborated by Song et al. [14]. They also
consider that slight variations to data illustration can have a
vast influence on the outcomes of article selection and bug
prediction. Numerous feature selection algorithms to
envisage buggy software modules for a hugeheritage
communications software offered by Gao et al.
Numerous feature selection algorithms to envisage buggy
software units for a huge legacy communications software
offered by Gao et al. There are two methods used. First,
filter based techniques and second, subset selection search
algorithm.

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4. SYSTEM OVERVIEW
Fig -1: System Overview of bug prediction system
Above diagram fig 1. Shows general overview of the
system. Originally, user login/register into the system. It
also encompasses training data/raw data which retains the
logs or history. Then it checks for bug. If bug present in the
system verify and report bug. Then, cos-triage algorithm is
applied on that bug. Cos-triage algorithm supports us for
fixing bug or defects. Then at the back end that output with
bug is used as input to support vector machine (SVM)
classifier .SVM classifier uses different feature selection
methods which is given above. After that by reducing the
features we gets final output without bug. SVM classifier
operate on skilled data/raw data which is kept in log record.

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5. SCREENSHOTS

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6. GRAPHICAL REPRESENTATION
Fig 2.Graphical representation of bug prediction on projects

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Fig.3: Cos-triage algorithm graph
7. CONCLUSION
This paper has explored algorithm called Cos-triage
algorithm which helps to reduce cost and increase accuracy
of bug fixing or bug prediction. In this paper we used
feature selection method which reduces the quantity of
structures used by a support vector device classifier and
naive byes for bug prediction.
Henceforward, the usage of classifiers with feature selection
will approve fast, correct, more accurate bug predictions, if
software designers have highly developed bug prediction
methods rooted into their software development
environment. Similarly, many algorithm will build in future
which will increase accuracy of bug prediction.
REFERENCES
[1] T.L. Graves, A.F. Karr, J.S. Marron, and H. Siy,
“Predicting Fault Incidence Using Software Change
History,” IEEE Trans. Software Eng., vol. 26, no. 7,
pp. 653-661, July 2000.
[2] S. Kim, E. W. Jr., and Y. Zhang, “Classifying
Software Changes: Clean or Buggy?” IEEE Trans.
Software Eng., vol. 34, no. 2, pp. 181–196,2008.
[3] A.Bessey, K. Block, B. Chelf, A. Chou, B. Fulton, S.
Hallem, C.Henri-Gros, A. Kamsky, S. McPeak, and
D.R. Engler, “A Few Billion Lines of Code Later:
Using Static Analysis to Find Bugs in the Real
World,” Comm. ACM, vol. 53, no. 2, pp. 66-75,
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[4] J. Madhavan and E. Whitehead Jr., “Predicting
Buggy Changes Inside an Integrated Development
Environment,” Proc. OOPSLA Workshop Eclipse
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[5] T. Khoshgoftaar and E. Allen, “Predicting the Order
of Fault-Prone Modules in Legacy Software,” Proc.
1998 Int’l Symp. on SoftwareReliability Eng., pp.
344–353, 1998.
[6] R. Kumar, S. Rai, and J. Trahan, “Neural-Network
Techniques for Software-Quality Evaluation,”
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[7] T. Ostrand, E. Weyuker, and R. Bell, “Predicting the
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[8] A.Hassan and R. Holt, “The Top Ten List: Dynamic
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[9] R. Moser, W. Pedrycz, and G. Succi, “A Comparative
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[10] L.C. Briand, W.L. Melo, and J. Wu¨ st, “Assessing
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[11] M. Hall and G. Holmes, “Benchmarking Attribute
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[12] J. Quinlan, C4.5: Programs for Machine Learning.
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[13] Q. Song, Z. Jia, M. Shepperd, S. Ying, and J. Liu, “A
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[14] K. Gao, T. Khoshgoftaar, H. Wang, and N. Seliya,
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Software: Practice and Experience,vol. 41, no. 5, pp.
579-606, 2011.
[15] A. Bessey, K. Block, B. Chelf, A. Chou, B. Fulton, S.
Hallem, C.Henri-Gros, A. Kamsky, S. McPeak, and
D.R. Engler, “A FewBillion Lines of Code Later:
Using Static Analysis to Find Bugs in the Real
World,” Comm. ACM, vol. 53, no. 2, pp. 66-75, 2010

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BIOGRAPHIES
Miss Veena jadhav M.tech (Computer)
BVDUCOE,
Pune
Prof.Vandana Gaikwad, Assistant Professor, Computer
department, BVDUCOE, Pune
Prof.Netra Patil, Assistant Professor, Computer department
BVDUCOE, Pune

Implementation of reducing features to improve code change based bug prediction by using cos triage algorithm

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