research-article

Measuring high and low priority defects on traditional and mobile open source software

Authors:

Giuseppe Destefanis,

Michele MarchesiAuthors Info & Claims

WETSoM '16: Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics

Pages 1 - 7

https://doi.org/10.1145/2897695.2897696

Published: 14 May 2016 Publication History

Abstract

Software defects are the major cause for system failures. To effectively design tools and provide support for detecting and recovering from software failures, requires a deep understanding of defect features. In this paper we present an analysis of defect characteristics in two different open source software development domains: Mobile and Traditional. Our attention is focused on measuring the differences between High-Priority and Low-Priority defects. High or Low priority of a given defect is decided by a developer when creating a bug report for an issue tracking system. We sampled hundreds of real world bugs in hundreds of large and representative open-source projects. We used natural language text classification techniques to automatically analyse roughly 700,000 bug reports from the Bugzilla, Jira and Google Issues issue tracking systems. Results show that there are differences between High-Priority and Low-Priority defects classification in Mobile and Traditional development domains.

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Cited By

Xie XSu YChen SChen LXuan JXu B(2022)MULA: A Just-In-Time Multi-labeling System for Issue ReportsIEEE Transactions on Reliability10.1109/TR.2021.307451271:1(250-263)Online publication date: Mar-2022
https://doi.org/10.1109/TR.2021.3074512
Hirsch THofer B(2022)Detecting non-natural language artifacts for de-noising bug reportsAutomated Software Engineering10.1007/s10515-022-00350-029:2Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10515-022-00350-0
Zhao KXu ZYan MTang YFan MCatolino GHung CHong JBechini ASong E(2021)Just-in-time defect prediction for Android apps via imbalanced deep learning modelProceedings of the 36th Annual ACM Symposium on Applied Computing10.1145/3412841.3442019(1447-1454)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3412841.3442019
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cover image ACM Conferences

WETSoM '16: Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics

May 2016

76 pages

ISBN:9781450341776

DOI:10.1145/2897695

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2016

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Engineering and Physical Sciences Research Council (EPSRC) of the UK

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ICSE '16

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IEEE-CS\DATC
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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

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Cited By

Xie XSu YChen SChen LXuan JXu B(2022)MULA: A Just-In-Time Multi-labeling System for Issue ReportsIEEE Transactions on Reliability10.1109/TR.2021.307451271:1(250-263)Online publication date: Mar-2022
https://doi.org/10.1109/TR.2021.3074512
Hirsch THofer B(2022)Detecting non-natural language artifacts for de-noising bug reportsAutomated Software Engineering10.1007/s10515-022-00350-029:2Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10515-022-00350-0
Zhao KXu ZYan MTang YFan MCatolino GHung CHong JBechini ASong E(2021)Just-in-time defect prediction for Android apps via imbalanced deep learning modelProceedings of the 36th Annual ACM Symposium on Applied Computing10.1145/3412841.3442019(1447-1454)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3412841.3442019
Hirsch THofer B(2021)Identifying non-natural language artifacts in bug reports2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)10.1109/ASEW52652.2021.00046(191-197)Online publication date: Nov-2021
https://doi.org/10.1109/ASEW52652.2021.00046
Hirsch THofer B(2020)Root cause prediction based on bug reports2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)10.1109/ISSREW51248.2020.00067(171-176)Online publication date: Oct-2020
https://doi.org/10.1109/ISSREW51248.2020.00067
Hirsch T(2020)A Fault Localization and Debugging Support Framework driven by Bug Tracking Data2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)10.1109/ISSREW51248.2020.00053(139-142)Online publication date: Oct-2020
https://doi.org/10.1109/ISSREW51248.2020.00053
Singh PNevendra M(2020)Prediction Priority of Defective Modules for Testing Resource AllocationAutomated Software Testing10.1007/978-981-15-2455-4_5(95-109)Online publication date: 4-Feb-2020
https://doi.org/10.1007/978-981-15-2455-4_5
Destefanis GMarchesi MOrtu MTonelli RBracciali AHierons R(2018)Smart contracts vulnerabilities: a call for blockchain software engineering?2018 International Workshop on Blockchain Oriented Software Engineering (IWBOSE)10.1109/IWBOSE.2018.8327567(19-25)Online publication date: 20-Mar-2018
https://doi.org/10.1109/IWBOSE.2018.8327567
Ibba SPinna ASeu MPani FTonelli R(2017)CitySenseProceedings of the XP2017 Scientific Workshops10.1145/3120459.3120472(1-5)Online publication date: 22-May-2017
https://dl.acm.org/doi/10.1145/3120459.3120472

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