IRJET-Automatic Bug Triage with Software
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This document discusses automatic bug triage using data reduction techniques on bug report data. It proposes combining instance selection and feature selection to simultaneously reduce the scale of bug reports and words. An algorithm is presented that first applies feature selection to reduce words, then applies instance selection to reduce bug reports. A predictive model is used to determine the optimal order of these reduction techniques based on attributes of historical bug data. The approach aims to improve the accuracy of automatic bug triage by leveraging data processing to form a reduced, higher quality training set from large bug repositories.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 707
Automatic Bug Triage with Software
1Dhanashree Lohagaonkar,
2Kanchan Kharat , 3Omkar Bhise, 4Abhijeet Korhale,
5Prof Mrs H.A.Shinde
1,2,3,4 Student, Department of Computer Engineering All India Shri Shivaji Memorial Society Polytechinc,
Kennedy road, Pune, Maharashtra, India.
5Lecturer, Department of Computer Engineering All India Shri Shivaji Memorial Society Polytechnic
Kennedy Road, Pune, Maharashtra, India.
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract—Software companies spend over45percentofcost
in dealing with software bugs. An inevitable step of fixingbugs
is bug triage, which aims to correctly assign a developer to a
new bug. To decrease the time cost in manual work, text
classification techniques are appliedtoconductautomaticbug
triage. In this paper, we address the problem of datareduction
for bug triage, i.e., how to reduce the scale and improve the
quality of bug data. We combine instance selection with
feature selection to simultaneously reduce data scale on the
bug dimension and the word dimension. To determine the
order of applying instance selection and feature selection, we
extract attributes from historical bug data sets and build a
predictive model for a new bug data set. We empirically
investigate the performance of data reduction on totally
600,000 bug reports of two large open source projects,namely
Eclipse and Mozilla. The results show that our data reduction
can effectively reduce the data scale and improve theaccuracy
of bug triage. Our work provides an approach to leveraging
techniques on data processing to form reduced and high-
quality bug data in software development and maintenance.
INTRODUCTION
software repositories is an interdisciplinary domain, which
aims to employ data mining to deal with software
engineering problems In modern software development,
software repositories are large-scale databases for storing
the output of software development, e.g., source code, bugs,
emails, and specifications. Tradi-tional software analysis is
not completely suitable for the large-scale and complex data
in software repositories [58]. Data mining has emerged as a
promising means to handle software data (e.g., [7], [32]). By
leveraging data mining techniques, mining software
repositories can uncover inter-esting information in
software repositories and solve real-world software
problems.
A bug repository (a typical software repository, for storing
details of bugs), plays an important role in managing soft-
ware bugs. Software bugs are inevitable and fixing bugs is
expensive in software development. Software companies
spend over 45 percent of cost in fixing bugs [39]. Large soft-
ware projects deploy bug repositories(also calledbug track-
ing systems) to support information collection and to assist.
BACKGROUND AND MOTIVATION
Background
Bug repositories are widely used for maintaining software
bugs, e.g., a popular and open source bug repository, Bug-
zilla [5]. Once a software bug is found, a reporter (typically a
developer, a tester, or an end user) records this bug to the
bug repository. A recorded bug is called a bug report, which
has multiple items for detailing the information of repro-
ducing the bug. In Fig. 1, we show a part of bug report for
bug 284541 in Eclipse.2 In a bug report, the summary and
the description are two key items about the information of
the bug, which are recorded in natural languages. As their
names suggest, the summary denotesageneralstatementfor
identifying a bug while the description gives the details for
reproducing the bug. Some other itemsare recordedinabug
report for facilitating the identification of the bug, such as
the product, the platform, and the importance. 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 assigned-towillchange
to another developer if the previously assigned developer
cannot fix this bug. The process of assigning a correct
developer for fixing the bug is called bug triage.Forexample,
in Fig. 1, the developer Dimitar Giormov is thefinalassigned-
to developer of bug 284541. A developer, who is assigned to
a new bug report, starts to fix the bug based on the
knowledge of historical bug fix-ing [36], [64]. Typically, the
developer pays efforts to under-stand the new bug report
and to examine historically fixed bugs as a reference (e.g.,
searching for similar bugs [54] and applying existing
solutions to the new bug [28]).An item statusof a bug report
is changed according to the current result of handling this
bug until the bug is completely fixed. Changesofabugreport
are stored in an item history. Table 1 presents a part of
history of bug 284541. This bug has been assigned to three
developers and only the last developer can handle this bug
correctly. Changing developers lasts for over seven months
while fixing this bug only costs three days.](https://image.slidesharecdn.com/irjet-v5i1152-180316095059/85/IRJET-Automatic-Bug-Triage-with-Software-1-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 708
Motivation
Real-world data always include noise and redundancy [31].
Noisy data may mislead the data analysis techniques [66]
while redundant data may increase the cost of data process-
ing [19]. In bug repositories, all the bug reports are filled by
developers in natural languages. The low-quality bugsaccu-
mulate in bug repositories with the growth in scale. Such
Fig. 2. Illustration of reducing bug data for bug triage. Sub-
figure
(a) presents the framework of existing work on bug
triage. Before train-ing a classifier with a bug data set, we
add a phase of data reduction, in (b), which combines the
techniques of instance selection and feature selection to
reduce the scale of bug data. In bug data reduction, a prob-
lem is how to determine the order of two reduction
techniques. In (c), based on the attributes of historical bug
data sets, we propose a binary classification method to
predict reduction orders.
Example 1 (Bug 205900). Current version in Eclipse Europa
discovery repository broken.
. . . [Plug-ins] all installed correctly and do not show
any errors in Plug-in configuration view. Whenever I try to
add a [diagram name] diagram, the wizard cannot bestarted
due to a missing [class name] class . . .
In this bug report, some words, e.g., installed, show, started,
and missing, are commonly used for describing bugs. For
text classification, such common words are not helpful for
the quality of prediction. Hence, we tend to remove these
words to reduce the computation for bug tri-age. However,
for the text classification, the redundant words in bugs
cannot be removed directly. Thus, we want to adapt a
relevant technique for bug triage.
To study the noisy bug report, we take the bug report of
bug 201598 as Example 2 (Note that both the summary and
the description are included).
Example 2 (Bug 201598). 3.3.1 about says 3.3.0.
Build id: M20070829-0800. 3.3.1 about says 3.3.0.
This bug report presents the error in the version dialog.
But the details are not clear. Unless a developer is very
familiar with the backgroundof this bug, it is hard tofindthe
details. According to the item history, this bug is fixed by the
developer who has reported this bug. But the summary of
this bug may make other developers confused. Moreover,
from the perspective of data processing, espe-cially
automatic processing, the wordsin this bug mayberemoved
since these wordsare not helpful to identify this bug.Thus,it
is necessary to remove the noisy bug reports and words for
bug triage.
Algorithm 1. Data reduction based on FS ! IS
Input: training set T with n words and m bug reports,
reduction order FS!IS
final number nF of words, final
number mI of bug reports,
Output: reduced data set T FI for bug triage
1) apply FS to n words of T and calculate objective values
for all the words;
2) select the top nF words of T and generate a training
set T F ;
3) apply IS to mI bug reports of T F ;
4) terminate IS when the number of bug reports is equal to
or less than mI and generate the final training set T FI .
Algorithm 2. NaïveBayesAlgorithm (Use to classifyingdata)-
Input- Training data set
Output-
1. To classify fixed bug from data set
2.To classify most solved bug on which type.
3.To assign bug to developer to most solved bugs as
particular type of bug.](https://image.slidesharecdn.com/irjet-v5i1152-180316095059/85/IRJET-Automatic-Bug-Triage-with-Software-2-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 710
RESULT:
We examine the results of bug data reduction on bug
repositories of two projects, Eclipse and Mozilla. For each
project, we evaluate results on five data sets and each data
set is over 10,000 bug reports, which are fixed or duplicate
bug reports. We check bug reports in the two projects and
find out that 45.44 percent of bug reports in Eclipse and
28.23 percent of bug reports in Mozilla arefixedorduplicate.
CONCLUSION:
Bug triage is an expensive step of software maintenance in
both labor cost and time cost. Our work provides a
techniques on data processing to form reduced and high-
quality bug data in software development and maintenance.
The results of data reduction in bug triage to explore how to
prepare a high quality bug data set and tackle a domain
specific software task. To find out the potential relationship
between the attributes of bug data sets and the reduction
orders using predicting reduction orders.
REFERENCE
1. S. Kim, H. Zhang, R. Wu, and L. Gong, “Dealing with noise
in defect prediction,” in Proc. 32nd ACM/IEEE Int. Conf.
Softw. Eng., May 2010, pp. 481–490.
2. A. Lamkanfi, S. Demeyer, E. Giger, and B. Goethals,
“Predicting the severity of a reported bug,” in Proc. 7th
IEEE Working Conf. Mining Softw. Repositories, May
2010, pp. 1–10.
3. G. Lang, Q. Li, and L. Guo, “Discernibility matrix
4. D. Lo, J. Li, L. Wong, and S. C. Khoo, “Mining iterative
generators and representative rules for software
specification discovery,” IEEE Trans. Knowl. Data Eng.,
vol. 23, no. 2, pp. 282–296, Feb. 2011.
5. Mozilla. (2014). [Online]. Available:
http://mozilla.org/
6. D. Matter, A. Kuhn, and O. Nierstrasz, “Assigning bug
7. G. Miao, L. E. Moser, X. Yan, S. Tao, Y. Chen, and N.
Anerousis, “Generative models for ticket resolution in
expert networks,” in Proc. 16th ACM SIGKDD Int. Conf.
Knowl. Discovery Data Mining, 2010, pp. 733–742.
8. E. Murphy-Hill, T. Zimmermann, C. Bird, and N.
Nagappan, “The design of bug fixes,” in Proc. Int. Conf.
Softw. Eng., 2013, pp. 332– 341.
9. J. A. Olvera-Lopez, J. A.Carrasco-Ochoa, J. F. Martınez-
Trinidad, and J. Kittler, “A review of instance selection
methods,” Artif. Intell. Rev., vol. 34, no. 2, pp. 133–143,
2010.
10. J. A. Olvera-Lopez, J. F. Martınez-Trinidad, and J. A.
Carrasco-Ochoa, “Restricted sequential floating search
applied to object selection,” in Proc. Int. Conf. Mach.
Learn. Data Mining Pattern Rec-ognit., 2007, pp. 694–
702.
11. R. S. Pressman, Software Engineering: A Practitioner’s
Approach, 7th
ed. New York, NY, USA: McGraw-Hill, 2010.
12. J. W. Park, M. W. Lee, J. Kim, S. W. Hwang, and S. Kim,
“Costriage: A cost-aware triage algorithm for bug
reporting sys-tems,” in Proc. 25th Conf.Artif.Intell.,Aug.
2011, pp. 139–144.
13. J. C. Riquelme, J. S. Aguilar-Ruız, and M. Toro, “Finding
represen-tative patterns with ordered projections,”
Pattern Recognit., vol. 36,pp.1009–1018, 2003.
simplifica-tion with new attributedependencyfunctions
for incomplete information systems,” Knowl. Inform.
Syst., vol. 37, no. 3, pp. 611–638, 2013.
reports using a vocabulary-based expertise model of
developers,” in Proc. 6th Int. Working Conf. Mining
Softw. Repositories, May 2009, pp.131-140.](https://image.slidesharecdn.com/irjet-v5i1152-180316095059/85/IRJET-Automatic-Bug-Triage-with-Software-4-320.jpg)
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IRJET-Automatic Bug Triage with Software
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 707 Automatic Bug Triage with Software 1Dhanashree Lohagaonkar, 2Kanchan Kharat , 3Omkar Bhise, 4Abhijeet Korhale, 5Prof Mrs H.A.Shinde 1,2,3,4 Student, Department of Computer Engineering All India Shri Shivaji Memorial Society Polytechinc, Kennedy road, Pune, Maharashtra, India. 5Lecturer, Department of Computer Engineering All India Shri Shivaji Memorial Society Polytechnic Kennedy Road, Pune, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract—Software companies spend over45percentofcost in dealing with software bugs. An inevitable step of fixingbugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are appliedtoconductautomaticbug triage. In this paper, we address the problem of datareduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects,namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve theaccuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high- quality bug data in software development and maintenance. INTRODUCTION software repositories is an interdisciplinary domain, which aims to employ data mining to deal with software engineering problems In modern software development, software repositories are large-scale databases for storing the output of software development, e.g., source code, bugs, emails, and specifications. Tradi-tional software analysis is not completely suitable for the large-scale and complex data in software repositories [58]. Data mining has emerged as a promising means to handle software data (e.g., [7], [32]). By leveraging data mining techniques, mining software repositories can uncover inter-esting information in software repositories and solve real-world software problems. A bug repository (a typical software repository, for storing details of bugs), plays an important role in managing soft- ware bugs. Software bugs are inevitable and fixing bugs is expensive in software development. Software companies spend over 45 percent of cost in fixing bugs [39]. Large soft- ware projects deploy bug repositories(also calledbug track- ing systems) to support information collection and to assist. BACKGROUND AND MOTIVATION Background Bug repositories are widely used for maintaining software bugs, e.g., a popular and open source bug repository, Bug- zilla [5]. Once a software bug is found, a reporter (typically a developer, a tester, or an end user) records this bug to the bug repository. A recorded bug is called a bug report, which has multiple items for detailing the information of repro- ducing the bug. In Fig. 1, we show a part of bug report for bug 284541 in Eclipse.2 In a bug report, the summary and the description are two key items about the information of the bug, which are recorded in natural languages. As their names suggest, the summary denotesageneralstatementfor identifying a bug while the description gives the details for reproducing the bug. Some other itemsare recordedinabug report for facilitating the identification of the bug, such as the product, the platform, and the importance. 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 assigned-towillchange to another developer if the previously assigned developer cannot fix this bug. The process of assigning a correct developer for fixing the bug is called bug triage.Forexample, in Fig. 1, the developer Dimitar Giormov is thefinalassigned- to developer of bug 284541. A developer, who is assigned to a new bug report, starts to fix the bug based on the knowledge of historical bug fix-ing [36], [64]. Typically, the developer pays efforts to under-stand the new bug report and to examine historically fixed bugs as a reference (e.g., searching for similar bugs [54] and applying existing solutions to the new bug [28]).An item statusof a bug report is changed according to the current result of handling this bug until the bug is completely fixed. Changesofabugreport are stored in an item history. Table 1 presents a part of history of bug 284541. This bug has been assigned to three developers and only the last developer can handle this bug correctly. Changing developers lasts for over seven months while fixing this bug only costs three days.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 708 Motivation Real-world data always include noise and redundancy [31]. Noisy data may mislead the data analysis techniques [66] while redundant data may increase the cost of data process- ing [19]. In bug repositories, all the bug reports are filled by developers in natural languages. The low-quality bugsaccu- mulate in bug repositories with the growth in scale. Such Fig. 2. Illustration of reducing bug data for bug triage. Sub- figure (a) presents the framework of existing work on bug triage. Before train-ing a classifier with a bug data set, we add a phase of data reduction, in (b), which combines the techniques of instance selection and feature selection to reduce the scale of bug data. In bug data reduction, a prob- lem is how to determine the order of two reduction techniques. In (c), based on the attributes of historical bug data sets, we propose a binary classification method to predict reduction orders. Example 1 (Bug 205900). Current version in Eclipse Europa discovery repository broken. . . . [Plug-ins] all installed correctly and do not show any errors in Plug-in configuration view. Whenever I try to add a [diagram name] diagram, the wizard cannot bestarted due to a missing [class name] class . . . In this bug report, some words, e.g., installed, show, started, and missing, are commonly used for describing bugs. For text classification, such common words are not helpful for the quality of prediction. Hence, we tend to remove these words to reduce the computation for bug tri-age. However, for the text classification, the redundant words in bugs cannot be removed directly. Thus, we want to adapt a relevant technique for bug triage. To study the noisy bug report, we take the bug report of bug 201598 as Example 2 (Note that both the summary and the description are included). Example 2 (Bug 201598). 3.3.1 about says 3.3.0. Build id: M20070829-0800. 3.3.1 about says 3.3.0. This bug report presents the error in the version dialog. But the details are not clear. Unless a developer is very familiar with the backgroundof this bug, it is hard tofindthe details. According to the item history, this bug is fixed by the developer who has reported this bug. But the summary of this bug may make other developers confused. Moreover, from the perspective of data processing, espe-cially automatic processing, the wordsin this bug mayberemoved since these wordsare not helpful to identify this bug.Thus,it is necessary to remove the noisy bug reports and words for bug triage. Algorithm 1. Data reduction based on FS ! IS Input: training set T with n words and m bug reports, reduction order FS!IS final number nF of words, final number mI of bug reports, Output: reduced data set T FI for bug triage 1) apply FS to n words of T and calculate objective values for all the words; 2) select the top nF words of T and generate a training set T F ; 3) apply IS to mI bug reports of T F ; 4) terminate IS when the number of bug reports is equal to or less than mI and generate the final training set T FI . Algorithm 2. NaïveBayesAlgorithm (Use to classifyingdata)- Input- Training data set Output- 1. To classify fixed bug from data set 2.To classify most solved bug on which type. 3.To assign bug to developer to most solved bugs as particular type of bug.
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 709 EXISTING SYSTEM: A time-consuming step of handling software bugsis bug triage, which aims to assign a correct developer to fix a new bug. In traditional software development, new bugsare manually triaged by an expert developer, i.e.,ahumantriage. Due to the large number of daily bugs and the lack of expertise of all the bugs, manual bug triage is expensive in time cost and low in accuracy. In manual bug triage in Eclipse, percent of bugs are assigned by mistake while the time cost between opening one bug and its first triaging is 19.3 days on average. To avoid the expensive cost of manual bug triage, existing work has proposed an automatic bug triage approach, which appliestext classification techniques to predict developersfor bug reports. In this approach,abug report is mapped to a document and a related developer is mapped to the label of the document. Then, bug triage is converted into a problem of text classification and is automatically solved with mature text classification techniques, e.g., Naive Bayes. Based on the results of text classification, a human triage assigns new bugs by incorporating his/her expertise. However, large-scale and low-quality bug data in bug repositories block the techniques of automatic bug triage. .Since software bug data are a kind of free-form text data, it is necessary to generate well-processed bug data to facilitate the application. we address the problem of data reduction for bug triage, i.e., how to reduce the bug data to save the labor cost of developers and improve the quality to facilitate the process of bug triage. Data reduction for bug triage aims to build a small-scale and high-quality set of bug data byremovingbug reports and words, which are redundantornon-informative. In our work, we combine existing techniques of instance selection and feature selection to simultaneously reducethe bug dimension and the word dimension. The reduced bug data contain fewer bug reports and fewer words than the original bug data and provide similar information over the original bug data. We evaluate the reduced bug data according to two criteria: the scale of a data set and the accuracy of bug triage. To avoid the bias of a single algorithm, we empirically examine the results of four instance selection algorithms and four feature selection algorithm. SOFTWARE REQUIREMENTS: Operating System : Windows 7 Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS IDE : Eclipse Juno Web Server : Tomcat Database : My SQL Java Version : J2SDK1.7 HARDWARE REQUIREMENTS: Hardware :Pentium Dual Core Speed :2.80 GHz RAM : 1GB Hard Disk : 20 GB Floppy Drive : 1.44 MB KeyBoard :Standard Windows Keyboard Mouse :Two or Three Button Mouse Monitor : SVGA MODULE DESCRIPTION: INSTANCE SELECTION: Instance selection and feature selection are widely used techniques in data processing. For a given data set in a certain application, instance selection is to obtain asubsetof relevant instances (i.e., bug reports in bug data) while feature selection aimsto obtain a subset of relevant features (i.e., words in bug data). In our work, we employ the combination of instance selection and feature selection. DATA REDUCTION: In our work, to save the labor cost of developers, the data reduction for bug triage has two goals. 1) Reducing the data scale. 2) Improving the accuracy of bug triage. DISADVANTAGES: We present the problem of data reduction for bug triage. This problem aims to augment the data set of bug triage in two aspects, namely a) To simultaneously reduce the scales of the bug dimension and the word dimension. b) To improve the accuracy of bug triage. We propose a combination approach to addressing the problem of data reduction. This can be viewed as an application of instance selection and feature selectioninbug repositories. PROPOSED SYSTEM: In this part, we present the data preparation forapplying the bug data reduction. We evaluate the bug data reduction on bug repositories of two large open source projects, namely Eclipse and Mozilla. Eclipse is a multi-language software development environment, includinganIntegrated Development Environment (IDE) and an extensible plug-in system; Mozilla is an Internet application suite, including some classic products, such as the Firefox browser and the Thunderbird email client. Up to December31,2011,366,443 bug reports over 10 years.
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 710 RESULT: We examine the results of bug data reduction on bug repositories of two projects, Eclipse and Mozilla. For each project, we evaluate results on five data sets and each data set is over 10,000 bug reports, which are fixed or duplicate bug reports. We check bug reports in the two projects and find out that 45.44 percent of bug reports in Eclipse and 28.23 percent of bug reports in Mozilla arefixedorduplicate. CONCLUSION: Bug triage is an expensive step of software maintenance in both labor cost and time cost. Our work provides a techniques on data processing to form reduced and high- quality bug data in software development and maintenance. The results of data reduction in bug triage to explore how to prepare a high quality bug data set and tackle a domain specific software task. To find out the potential relationship between the attributes of bug data sets and the reduction orders using predicting reduction orders. REFERENCE 1. S. Kim, H. Zhang, R. Wu, and L. Gong, “Dealing with noise in defect prediction,” in Proc. 32nd ACM/IEEE Int. Conf. Softw. Eng., May 2010, pp. 481–490. 2. A. Lamkanfi, S. Demeyer, E. Giger, and B. Goethals, “Predicting the severity of a reported bug,” in Proc. 7th IEEE Working Conf. Mining Softw. Repositories, May 2010, pp. 1–10. 3. G. Lang, Q. Li, and L. Guo, “Discernibility matrix 4. D. Lo, J. Li, L. Wong, and S. C. Khoo, “Mining iterative generators and representative rules for software specification discovery,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 2, pp. 282–296, Feb. 2011. 5. Mozilla. (2014). [Online]. Available: http://mozilla.org/ 6. D. Matter, A. Kuhn, and O. Nierstrasz, “Assigning bug 7. G. Miao, L. E. Moser, X. Yan, S. Tao, Y. Chen, and N. Anerousis, “Generative models for ticket resolution in expert networks,” in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp. 733–742. 8. E. Murphy-Hill, T. Zimmermann, C. Bird, and N. Nagappan, “The design of bug fixes,” in Proc. Int. Conf. Softw. Eng., 2013, pp. 332– 341. 9. J. A. Olvera-Lopez, J. A.Carrasco-Ochoa, J. F. Martınez- Trinidad, and J. Kittler, “A review of instance selection methods,” Artif. Intell. Rev., vol. 34, no. 2, pp. 133–143, 2010. 10. J. A. Olvera-Lopez, J. F. Martınez-Trinidad, and J. A. Carrasco-Ochoa, “Restricted sequential floating search applied to object selection,” in Proc. Int. Conf. Mach. Learn. Data Mining Pattern Rec-ognit., 2007, pp. 694– 702. 11. R. S. Pressman, Software Engineering: A Practitioner’s Approach, 7th ed. New York, NY, USA: McGraw-Hill, 2010. 12. J. W. Park, M. W. Lee, J. Kim, S. W. Hwang, and S. Kim, “Costriage: A cost-aware triage algorithm for bug reporting sys-tems,” in Proc. 25th Conf.Artif.Intell.,Aug. 2011, pp. 139–144. 13. J. C. Riquelme, J. S. Aguilar-Ruız, and M. Toro, “Finding represen-tative patterns with ordered projections,” Pattern Recognit., vol. 36,pp.1009–1018, 2003. simplifica-tion with new attributedependencyfunctions for incomplete information systems,” Knowl. Inform. Syst., vol. 37, no. 3, pp. 611–638, 2013. reports using a vocabulary-based expertise model of developers,” in Proc. 6th Int. Working Conf. Mining Softw. Repositories, May 2009, pp.131-140.