The Eclipse and Mozilla defect tracking dataset: A genuine dataset for mining bug information

Lamkanfi, Ahmed; Pérez, Javier; Demeyer, Serge

doi:10.1109/msr.2013.6624028

Cited by 63 publications

(35 citation statements)

References 9 publications

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“…This dataset includes all the bug reports and their histories from their inception to May 2014. For Java projects, we have used Lamkanfi et al's [16] bug dataset to extract the bug information. The Java dataset includes all the bug reports and their histories from their inception to March 2011 for these four projects (extracted from Eclipse Bugzilla database).…”

Section: Subject Systemsmentioning

confidence: 99%

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Understanding the triaging and fixing processes of long lived bugs

Saha

Khurshid

Perry

2015

Information and Software Technology

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Section: Subject Systemsmentioning

confidence: 99%

“…Then using JGit APIs, we extracted all the commit messages from the histories and searched all numbers. 13,14,15,16 Then we matched each number with the bug IDs. To further ensure that those are indeed bug IDs, we only accepted those commits that contain additional information.…”

Section: Identification Of Faulty Source Codementioning

confidence: 99%

Understanding the triaging and fixing processes of long lived bugs

Saha

Khurshid

Perry

2015

Information and Software Technology

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“…Challenges in Adoption of Research: When authors were designing the solution, it was not clear whether we need to adopt techniques which are claimed to be achieving best performance on open source data sets( [11]) for the problem of duplicate defect identification. Interestingly most of the published works with high scores in detecting duplicate bugs apply supervised machine learning techniques (including deep learning), which though work well on large open source data sets, however, can't be easily adopted in practice without incurring significant cost to create desired training data in form of semantically equivalent pairs of bugs by the experienced SMEs before tool could be adopted in practice.…”

Section: Relevant Results Relevant + Related Results Topmentioning

confidence: 99%

Data-Driven Application Maintenance: Experience from the Trenches

Misra¹,

Sengupta²,

Rawat³

et al. 2017

2017 IEEE/ACM 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP)

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In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents to business processes. In the context of IT services, these problems are frequently encountered, yet there is a gap in bringing automation and optimization. Despite long-standing research around mining and analysis of software repositories, such research outputs are not adopted well in practice due to the constraints these solutions impose on the users. We discuss need for designing pragmatic solutions with low barriers to adoption and addressing right level of complexity of problems with respect to underlying business constraints and nature of data.

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“…We have used Lamkanfi et al's [21] bug dataset, obtained from the Eclipse Bugzilla database, 4 to obtain the bug information associated with these projects. This dataset includes all the bug reports and their histories from the project inception to March 2011 for these four projects.…”

Section: B Subject Systemsmentioning

confidence: 99%

Are These Bugs Really "Normal"?

Saha

Lawall

Khurshid

et al. 2015

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories

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Understanding the severity of reported bugs is important in both research and practice. In particular, a number of recently proposed mining-based software engineering techniques predict bug severity, bug report quality, and bug-fix time, according to this information. Many bug tracking systems provide a field "severity" offering options such as "severe", "normal", and "minor", with "normal" as the default. However, there is a widespread perception that for many bug reports the label "normal" may not reflect the actual severity, because reporters may overlook setting the severity or may not feel confident enough to do so. In many cases, researchers ignore "normal" bug reports, and thus overlook a large percentage of the reports provided. On the other hand, treating them all together risks mixing reports that have very diverse properties. In this study, we investigate the extent to which "normal" bug reports actually have the "normal" severity. We find that many "normal" bug reports in practice are not normal. Furthermore, this misclassification can have a significant impact on the accuracy of mining-based tools and studies that rely on bug report severity information.

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The Eclipse and Mozilla defect tracking dataset: A genuine dataset for mining bug information

Cited by 63 publications

References 9 publications

Understanding the triaging and fixing processes of long lived bugs

Understanding the triaging and fixing processes of long lived bugs

Data-Driven Application Maintenance: Experience from the Trenches

Are These Bugs Really "Normal"?

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The Eclipse and Mozilla defect tracking dataset: A genuine dataset for mining bug information

Cited by 63 publications

References 9 publications

Understanding the triaging and fixing processes of long lived bugs

Understanding the triaging and fixing processes of long lived bugs

Data-Driven Application Maintenance: Experience from the Trenches

Are These Bugs Really &#x0022;Normal&#x0022;?

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Are These Bugs Really "Normal"?