A Survey on Bug Tracking System for Effective Bug Clearance

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1622
A SURVEY ON BUG TRACKING SYSTEM FOR EFFECTIVE BUG CLEARANCE
M.Suresh1, M.Amarnath2, G.Baranikumar3, M.Jagadheeswaran4
1Assitant professor, Departement of Information Technology, mail4sureshuni@gmail.com.
2, 3, 4 B.TECH, 4th year student, Departement of Information Technology, Manakula Vinayagar Institute of
Technology,Pondicherry,India.
2amar.moorthy1994@gmail.com,3baranikum2r@gmail.com,4jagan.mohan239864@gmail.com.
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Abstract: Bugs are important challenge for a software organization. Software organization spends over 45 percentages of
their resources in handling these bugs. Managing these bugs manually are difficult and error prone. So an automatic
approach of instance selection and feature selection method is combined to handle the bugs, then the bugs are distributed to
bug solving experts. An inevitable step in fixing the bugs is assigning a bug solving expert. The problem is majority of bugs are
assigned to experts who has less experience in that domain which can leave the bugs unsolved. So using term selection method
a bug solving expert is predicted automatically depending upon the type of bugs. A history of these cleared bugs is maintained
using historical data management system. This automatically resolves a bug which is reported and solved in prior. This highly
reduces the time and cost involved in the bug clearance.
Keywords: Bug data reduction, Instance selection, feature selection, Historical data management system.
1. INTRODUCTION
Bug is important challenge for any software
organization. Most of the software companies need to
deal with large number of software bugs every day.
Software bugs are unavoidable and fixing software bugs
is an expensive task [2]. In fact Software organization
spends most of their resources in handling these bugs.
For managing software bugs, bug repository plays an
important role. In software development and
Maintenance, a bug repository is a significant software
repository for storing the bugs submitted by users. Most
of the software which is open source projects has an
open bug repository which allows developers as well as
users to submit issues or defects in the software that
suggest possible solutions and remark on existing bug
reports. The drawback is that large-scale software
projects are so much large that makes the triaging
process very difficult. The inefficient data and unclear
data add redundancy data to the data repository and
create a great challenge to the software experts [1]. In
bug repository, each software bug has a bug report. The
bug report consists of textual information regarding the
bug and updates related to status of bug fixing [2].
A bug repository gives a data stage support about
types of tasks on bugs, e.g., fault prediction, bug
localization, and reopened bug analysis. Large software
projects convey bug repositories (also called bug
tracking systems) support information collection and to
assist developers to handle bugs [3].
The number of regular occurring bugs for open
source large-scale software projects is so much large
that makes the triaging process very difficult and
challenging[1]. Once a bug report is formed, a human
triager assigns this bug to a developer, who will try to fix
this bug. This developer is recorded in an item assigned-
to.The process of assigning a correct developer for fixing
the bug is called bug triage. Bug triage is one of the most
time consuming step in handling of bugs in software
projects. Manual bug triage by a human triage is time
consuming and error-prone since the number of daily
bugs is large and lack of knowledge in developers about
all bugs. Because of all these things, bug triage results in
expensive time loss, high cost and low accuracy [2].
Before verifying and modifying a bug, each bug report
must be assigned to a relevant developer who could fix
it. In traditional bug repositories, all the bugs are
manually triaged by some specialized developers.
Aiming to reduce the human labor costs, some
supervised text classification approaches have been
proposed for automatic bug triage. After that the nature
of the bugs is predicted using a predictive algorithm and
then predict relevant developers for the incoming bug
reports with these classifiers.
A bug repository gives a data stage support about
types of tasks on bugs, e.g., fault prediction, bug
localization, and reopened bug analysis. Large software
projects convey bug repositories (also called bug
tracking systems) support information collection and to
assist developers to handle bugs [3].
The number of regular occurring bugs for open
source large-scale software projects is so much large
that makes the triaging process very difficult and
challenging[1]. Once a bug report is formed, a human
triager assigns this bug to a developer, who will try to fix
this bug. This developer is recorded in an item assigned-
to.The process of assigning a correct developer for fixing
the bug is called bug triage. Bug triage is one of the most

time consuming step in handling of bugs in software
projects. Manual bug triage by a human triage is time
consuming and error-prone since the number of daily
bugs is large and lack of knowledge in developers about
all bugs. Because of all these things, bug triage results in
expensive time loss, high cost and low accuracy [2].
Before verifying and modifying a bug, each bug report
must be assigned to a relevant developer who could fix
it. In traditional bug repositories, all the bugs are
manually triaged by some specialized developers.
Aiming to reduce the human labor costs, some
supervised text classification approaches have been
proposed for automatic bug triage. After that the nature
of the bugs is predicted using a predictive algorithm and
then predict relevant developers for the incoming bug
reports with these classifiers.
2. TECHNIQUES USED
2.1 Top-K pruning algorithm
Top-K ranking query in uncertain databases aims to
find a top K tuples according to a ranking function. The
interplay between score and uncertainty makes top-K
ranking in uncertain databases an intriguing issue,
leading to rich query semantics. Recently, a unified
ranking framework based on parameterized ranking
functions (PRFs) is formulated, which generalizes many
previously proposed ranking semantics. Under the PRFs
based ranking framework, efficient pruning approach for
Top-K ranking on dataset with tuple uncertainty has
been studied. However, this cannot be applied to top-K
ranking on dataset with value uncertainty (described
through attribute-level uncertain data model), which are
often natural and useful in analyzing uncertain data in
many applications.
2.2 Instance selection and feature selection
Instance selection and feature selection algorithm
deals with clustering the similar bug data sets. . Feature
and Instance Selection belong to the practice of data
preparation (or pre-processing), which is a preliminary
process that transforms raw data into a format that is
convenient to the data mining (or machine learning)
algorithm. Usually, data is stored in a table-like format:
the columns of these tables are the attributes or features
- they describe the data - and the rows, or lines, are the
records or instances - they are the examples of the
concept stored in the data. Feature and Instance
selection processes allow applications, such as
classification or clusterization, to focus only on the
important (or relevant) attributes and records to the
specific concept that is in study. By removing noise,
irrelevant and redundant features and instances, and
reducing the overall dimensionality of a dataset, feature
and instance selection have been demonstrated to
improve the performance of most machine learning
algorithms, speed up the output of models and allow
algorithms to deal with datasets whose sizes are
gigantic.
Feature selection algorithms perform very differently
in identifying and removing irrelevant, redundant and
randomly class-correlated features. Most of the works on
instance selection have been based on Nearest Neighbor
classification which finds a subset such that every
member of the original dataset is closer to a member of
the subset of the same class than to a member of the
subset of a different class.
2.3 Apollo’s algorithms
Apollo generates test inputs for a web application,
monitors the application for crashes, and validates that
the output conforms to user. Apollo algorithm, a new
sensor information processing tool for uncovering likely
facts in noisy participatory sensing data. Apollo belongs
to a category of tools called fact-finders. It is the first
fact-finder designed and implemented specifically for
participatory sensing. When data tuples are clustered
and ranked by Apollo, the quality of reported
observations increases considerably. Apollo algorithm
illustrates a fact-finding tool designed to uncover most
likely truth in noisy participatory sensing data. Filtering
the amount of data for correctness is an important
challenge, commonly known in machine learning and
knowledge discovery literature as fact-finding. Apollo is
the first fact-finder designed specifically for
participatory sensing data. The algorithm has the
following reconfigurable parts: The parser: It uses a
configuration file (that describes the format of the input
data stream) to convert input data into a standard JSON
format. Conceptually, the data stream is composed of
source, observation tuples, where source identifies the
data source (e.g., the Twitter user ID), and the
observation content may be structured, as in the case of
phones sharing sensor values, or unstructured, as in the
case of people sharing text. The configuration file
describes the observation format. The distance metrics:
A library of different distance metrics is provided for
clustering of observations. For unstructured text, these
metrics reflect text similarity. For structured data,
metrics compute differences in data vectors. Data can be
multidimensional. For example, when cell-phones report
sensor values at given locations, both measurements and
locations can be elements of the data vector.
2.4 Naïve bayes classifier
The approach iteratively labels numerous unlabeled
bug reports and trains a new classifier with labels of all
the bug reports. A weighted recommendation list is

maintained to boost the performance by imposing the
weights of multiple developers in training the classifier.
Naive bayes classifiers are a family of
simple probabilistic classifiers based on applying bayes
theorem with strong (naive) independence assumptions
between the features. It is not a single algorithm for
training such classifiers, but a family of algorithms based
on a common principle: all naive bayes classifiers
assume that the value of a particular feature
is independent of the value of any other feature, given
the class variable. For example, a fruit may be considered
to be an apple if it is red, round, and about 10 cm in
diameter. A naive bayes classifier considers each of these
features to contribute independently to the probability
that this fruit is an apple, regardless of any
possible correlations between the color, roundness and
diameter features.
2.5 Markov chains method
A Markov chain is a random process that undergoes
transitions from one state to another on a state space. It
must possess a property that is usually characterized
as "memorylessness": the probability distribution of the
next state depends only on the current state and not on
the sequence of events that preceded it. This specific
kind of "memorylessness" is called the Markov property.
Markov chains have many applications as statistical
models of real-world processes. It reveals developer
networks which can be used to discover team structures
and to find suitable experts for a new task.
2.6 Content-based recommendation (cbr) and
content based filtering (cbf)
It recommends only the types of bugs that each
developer has solved before. In a content-based
recommender system, keywords or attributes are used
to describe items. To provide content-based
recommendation we treat the prediction task as a text-
categorization problem. Content based filtering
combines cbr with a collaborative filtering recommender
(cf), it identify potential experts by identifying similar
bug reports and analyzing the associated change sets.
2.7 Defect prediction method
Defect prediction is a software task in software
metrics, which is to predict whether a software artifact
(e.g., A source code file, a class, or a module) contains a
defect or not First, a data set of software modules is
collected; second, each module is labeled (i.e., Whether a
module contains defects) and the attributes of this
module are extracted as metrics; third, a classifier (e.g., A
decision tree) is trained as a predictive model; fourth,
the trained classifier is used to predict whether a new
module contains defects.
3. LITERATURE REVIEW
3.1 Towards Effective Troubleshooting with
Data Truncation
Towards Effective Troubleshooting with Data
Truncation deals with reducing the data present in the
bug repository and improve the quality of data then
reduce time and cost of bug triaging, it represent an
automatic approach to predict a developer with relevant
experience to solve the new coming report.. The bug data
sets are obtained and techniques such as instance
selection feature selection are applied simultaneously.
The top k pruning is applied for improving results of
data reduction quality, obtaining domain wise bug
solution. Instance selection is for obtaining a subset of
relevant instances (i.e., bug reports in bug data) .It is
used to Remove noise and redundant instances,Remove
non-representative instances.
Feature selection which aims to obtain a subset of
relevant features (i.e., words in bug data) ,Sorting of
words according to feature values .Top-K pruning
algorithm for improving results of data reduction
quality.
3.2 Technique to Combine Feature Selection
with Instance Selection for Effective Bug triage
A Technique to Combine Feature Selection with
Instance Selection for Effective Bug Triage .It addresses
the issue of data reduction for bug triage by text
classification techniques. Conventional software
analysis is not totally suitable for the large-scale and
complex data in software repositories. Data mining has
developed as a promising means to handle software
data. There are two difficulties related to bug data that
may influence the effective use of bug repositories in
software development tasks, namely the huge scale and
the low quality. Therefore unfixed bugs are deleted from
the bug repositories.
3.3 Automatic Bug Triage using Semi-
Supervised Text Classification
Automatic Bug Triage using Semi-Supervised Text
Classification propose a semi-supervised text
classification approach for bug triage to avoid the
deficiency of labeled bug reports in existing supervised
approaches. This approach combines naive bayes
classifier and expectation maximization to take
advantage of both labeled and unlabeled bug reports.
This approach trains a classifier with a fraction of

labeled bug reports. Then the approach iteratively labels
numerous unlabeled bug reports and trains a new
classifier with labels of all the bug reports. Then it
employs a weighted recommendation list to boost the
performance by imposing the weights of multiple
developers in training the classifier. Before training a
supervised classifier for bug triage, a necessary step is to
collect numerous labeled bug reports, which are bug
reports marked with their relevant developers.The semi-
supervised text classification approach to improve the
classification accuracy of bug triage. This semi-
supervised approach enhances a NB classifier by
applying expectation-maximization (EM) based on the
combination of unlabeled and labeled bug reports. First,
this semi-supervised approach trains a classifier with
labeled bug reports. Then, the approach iteratively labels
the unlabeled bug reports and trains a new classifier
with labels of all the bug reports. To adjust bug triage,
we update a semi-supervised approach with a weighted
recommendation list (WRL) to augment the effectiveness
of unlabeled bug reports. This WRL is employed to
probabilistically label an unlabeled bug report with
multiple relevant developers instead of a single relevant
developer.
3.4 Reducing Features to Improve Bug
Prediction
Recently, machine learning classifiers have emerged
as a way to predict the existence of a bug in a change
made to a source code file. The classifier is first trained
on software history data, and then used to predict bugs.
Two drawbacks of existing classifier-based bug
prediction are potentially insufficient accuracy for
practical use, and use of a large number of features.
These large numbers of features adversely impact
scalability and accuracy of the approach. Reducing
Features to Improve Bug Prediction aims in classifier to
first trained on software history data, and then used to
predict bugs. The disadvantage of the traditional method
is that, classifier-based bug predictions are potentially
insufficient accuracy for practical use, and use of a large
number of features. The system uses Naive Bayes and
Support Vector Machine (SVM).The system mainly Gain
Ratio for feature selection, along with the
characterization of bug prediction results achieved when
using feature selection. This paper proposes a feature
selection technique applicable to classification-based
bug prediction. This technique is applied to predict bugs
in software changes, and performance of Naıve Bayes
and Support Vector Machine classifiers is characterized. .
These features include everything separated by
whitespace, in the code added or deleted in a change.
This leads to a large number of features, in the
thousands, and low tens of thousands. For larger project
histories which span thousand revisions or more, this
can stretch into hundreds of thousands of features. The
addition of many non-useful features reduces a
classifier’s accuracy. Additionally, the time required to
perform classification increases with the number of
features, rising to several seconds per classification for
tens of thousands of features, and minutes for large
project histories. A standardapproach (in the machine
learning literature) for handling large feature sets is to
perform a feature selection process to identify that
subset of features providing the best classification
results. This paper introduces a feature selection process
that discards features with lowest gain ratio until
optimal classification performance is reached for a given
performance measure.
3.5 Efficient Bug Triaging Using Text Mining
Efficient Bug Triaging Using Text Mining aims for an
automatic approach to predict a developer with relevant
experience to solve the new coming report. The
techniques used are five term selection method. Term
selection methods are used to reduce the high
dimensionality of term space by selecting the most
discriminating terms for the classification task. The
methods give a weight for each term in which terms with
higher weights are assumed to contribute more for the
classification task than terms with lower weights. The
goal of bug triaging is to assign potentially experienced
developers to new-coming bug reports. To reduce time
and cost of bug triaging, we present an automatic
approach to predict a developer with relevant
experience to solve the new coming report. It investigate
the use of five term selection methods on the accuracy of
bug assignment. In addition, it re-balance the load
between developers based on their experience. It
conduct experiments on four real datasets. To reduce the
time spent triaging, it present an approach for automatic
triaging by recommending one experienced developer
for each new bug report. This information can help to
manage the progress of these projects. In the last decade,
practitioners have analyzed and mined these software
repositories to support software development and
evolution. One of the important software repositories is
the bug tracking system (BTS). Many open source
software projects have an open bug repository that
allows both developers and users to submit defects or
issues in the software, suggest possible enhancements,
and comment on existing bug reports. It formulate the
bug triaging process as a classification task where
instances represent bug reports, features represent the
terms of the report, and the class label represents the
developer who fixed this report. This approach can help
the triage process in two ways: 1) it may allow a triager
to process a bug more quickly, and 2) it may allow a
triager with less knowledge about systems and
developers to perform bug assignments more accurately.

Sl.
no
Title Year Tools
Application
Area
Pros Cons
Analysis
1
Towards
Effective
Troubleshooting
With
Data Truncation
2015
Instance
selection
and
feature
selection
Bug
Repository
Handling.
The problem of
handling huge
number of data
in bug
repository is
minimized.
Instance
selection and
feature selection
is not completely
enough to
handle the data
in bug
repository.
Additionally,
text
processing
and data
processing
algorithm
must be used
to handle the
data in bug
repository.
2
A Technique to
Combine
Feature
Selection with
Instance
Selection for
Effective Bug
Triage
2015
Instance
selection
and
feature
selection
For an efficient
bug triage.
It analysis data
by considering
the word
dimension and
bug dimension
which helps in
reducing
duplicate and
unnecessary
bugs.
The order of
applying
instance
selection and
feature selection
is not clearly
explained which
leads to
inefficient
system.
The order of
applying
instance
selection and
feature is
selection
must be
determined
by a
predictive
algorithm.
3
Automatic Bug
Triage using
Semi-
Supervised Text
Classification
2010
Naïve
bayes
classifier
Software bug
handling.
It labels the bug
data iteratively.
The weighted
list maintained,
helps to boost
the results
obtained.
It only focuses
on classifying
the bugs in bug
repository. The
major problem
in bug handling
is that huge
number of data
in bug
repository.
Techniques
such as IS
and FS must
be used to
reduce the
data in bug
repository.
Proper
predictive
algorithm
must be used
to classify
bugs.
4
Reducing
Features to
Improve Bug
Prediction
2009
Support
vector
machine
Software
development
committee.
Provides a
layout for
minimizing
techniques used
in bug data
reduction.
Minimizing the
techniques used
leads in less
accuracy.
Necessary
algorithm
such as IS
and FS must
be used.
5 A Survey Paper
on Efficient
2015
Instance
selection
feature
Automatic bug
triaging.
It uses text
classification
techniques to
Too many
repositories are
used to handle
A
comparator
can be used

Approach of
Data Reduction
Techniques for
Bug Triaging
System
selection
and
historical
data sets
reduce the data
in bug
repository.
Then uses
predictive model
to predict a
developer based
on historical
data sets.
the bugs in bug
repository which
can be
minimized.
to compare
the new bug
report with
historical
data sets.
6
A survey on
bug-report
analysis
2015 Bugzilla
Explains
various
techniques in
bug handling
Research and
explain
background
details about
bug handling
which makes
bug handling an
efficient task.
The accuracy
obtains by this
techniques are
not more enough
in handling the
bugs.
Several more
techniques
such as
instance
selection and
feature
selection
must be used
to handle the
bugs.
7
Automatic bug
triage using text
cateogarization
Naïve
bayes
classifier
Large open
source
projects.
Have explained
naïve bayes
classifier for bug
triaging. And
investigated bug
data sets
efficiently for
bug handling.
There is a
chance of
assigning the
bugs to un-
suited
developers.
Therefore the
techniques used
are inefficient.
Developers
should be
involved in
bug triaging
for an
efficient bug
handling.
8
Bug report
network:
varieties
strategies and
impact in Fos
development
2004 -
Software
development
community.
It focuses on
developing a
model that
provides
enhanced
software
management
techniques to
handle bugs.
It just handles
the bugs from
single
community.
Tools must
be
introduced
to handle
BRN’s
complexity.
9
Coping with an
open bug
repository.
2004 Bugzilla
Development
of eclipse and
Firefox
projects.
Explained briefly
about various
challenges that
are faced in
handling bugs in
bug repository.
It does not
explained clearly
about how
overcoming
these challenges.
After
analyzing
the scenario
proper tools
must be
suggested
for bug
handling.

10
Defect tracking
system
2007
CDTS and
BTS
Development
of projects in
MNC’s.
CDTS helps in
keep tracking of
defect, flaws,
errors that can
happen in
development of
a product.
BTS provides a
list of developers
to solve the
reported bugs.
CDTS does not
provide a view
of
methodologies
to handle the
reported bugs.
The process is
time consuming
when CDTS is
combined with
BTS.
Alternate
approach
must be used
in handling
these defects
in the
products.
11.
Modeling bug
report quality
2007 Bugzilla
Used as filter
in Mozilla and
Firefox project
to resolve the
bug.
It will reduce
overall
maintenance
cost. Leads to
better
precession and
recall results.
The drawback is
that it will
consider only a
two projects.
The reported
should
provide
complete
guide about
the bug.
12.
Towards the
next generation
of bug tracking
system
2008
Card sort
technique
Software
development
project.
It will come out
with useful
recommendation
that improve the
working of BTS.
Though it
provides more
recommendation
it does not
provide any tool.
The feature
that can
implemented
to good
quality of
bug report in
BTS. The bug
report that
provides
complete
information
that reporter
gets an idea
on how to
file a bug
report.
13.
Duplicate bug
report
considered
harmful really??
2008 Infozilla
Software
maintenance
of large
projects.
That all
duplicate are not
always
considered to be
harmful
sometimes it
provides
additional
information’s.
The study was
based on the
survey results of
bug report from
eclipse project5.
BTS should
consist a tool
that scans
the entire
duplicate
bug and
extracts all
the
additional
information
that might
be help in

Table -1: Analysis of various bug tracking system
fixing the
bug.
14.
What makes a
good bug report
2008 cuezilla
Software
development
projects and
product
manufacturing.
It will mention
the crucial role
in resolving the
bug. Identifies
the why
developer takes
more time.
This will
consider the
response of
experienced
developers. It
involves the self
selection
principles.
To improve
the quality of
bug report is
that it should
be made
mandatory
for all
reporters to
provide all
relevant
information
required
15.
New feature for
duplicate bug
detection
2014 Weka
Large open
source
projects and
android
development
projects.
Undefined the
use of
automatically
grouping the
duplicate bug
report.
Study shows that
36% of bugs are
duplicate but in
experiments
only 1452 were
duplicate.
The
attributes
should be
further
explored so
that better
duplicate
detection
system could
be
developed.

4. Conclusion
The amount of data available in the bug repository
plays an important role in bug handling.So as to reduce the
data in bug repository, the bug reduction techniques must
be implemented. The data in bug repository is majorly
reduced by neglecting the redundancy data in the bug
repository. Then the subset of the data in data repository
is obtained. In future we planned to develop a predictive
model which predicts a developer based on the type of
bugs obtained. Then an historical data management
system is maintained which keeps record of bugs which
are reported and resolved in prior. A comparator checks
with this historical data management system when a new
bug is reported, if it is found to be reported again the
historical data management system resolves it
automatically without assigning to an expert.
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