Article

Process mining multiple repositories for software defect resolution from control and organizational perspective

Authors:

Srinivas PadmanabhuniAuthors Info & Claims

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

Pages 122 - 131

https://doi.org/10.1145/2597073.2597081

Published: 31 May 2014 Publication History

Abstract

Issue reporting and resolution is a software engineering process supported by tools such as Issue Tracking System (ITS), Peer Code Review (PCR) system and Version Control System (VCS). Several open source software projects such as Google Chromium and Android follow process in which a defect or feature enhancement request is reported to an issue tracker followed by source-code change or patch review and patch commit using a version control system. We present an application of process mining three software repositories (ITS, PCR and VCS) from control flow and organizational perspective for effective process management. ITS, PCR and VCS are not explicitly linked so we implement regular expression based heuristics to integrate data from three repositories for Google Chromium project. We define activities such as bug reporting, bug fixing, bug verification, patch submission, patch review, and source code commit and create an event log of the bug resolution process. The extracted event log contains audit trail data such as caseID, timestamp, activity name and performer. We discover runtime process model for bug resolution process spanning three repositories using process mining tool, Disco, and conduct process performance and efficiency analysis. We identify bottlenecks, define and detect basic and composite anti-patterns. In addition to control flow analysis, we mine event log to perform organizational analysis and discover metrics such as handover of work, subcontracting, joint cases and joint activities.

References

[1]

Burcu Akman and O Demirors. Applicability of process discovery algorithms for software organizations. In Software Engineering and Advanced Applications, 2009. SEAA’09. 35th Euromicro Conference on, pages 195–202. IEEE, 2009.

Digital Library

[2]

Andrew Dittrich, Mehmet Hadi Gunes, and Sergiu Dascalu. Network analysis of software repositories: Identifying subject matter experts. In Complex Networks, pages 187–198. Springer, 2013.

[3]

Rami-Habib Eid-Sabbagh, Remco Dijkman, and Mathias Weske. Business process architecture: use and correctness. In Business Process Management, pages 65–81. Springer, 2012.

Digital Library

[4]

Monika Gupta and Ashish Sureka. Nirikshan: Mining bug report history for discovering process maps, ineﬃciencies and inconsistencies. In Proceedings of the 7th India Software Engineering Conference. ACM, 2014.

Digital Library

[5]

Kazuki Hamasaki, Raula Gaikovina Kula, Norihiro Yoshida, AE Cruz, Kenji Fujiwara, and Hajimu Iida. Who does what during a code review? datasets of oss peer review repositories. In Proceedings of the Tenth International Workshop on Mining Software Repositories, pages 49–52. IEEE Press, 2013.

Digital Library

[6]

Kwanghoon Pio Kim. Mining workflow processes from distributed workflow enactment event logs. Knowledge Management & E-Learning: An International Journal (KM&EL), 4(4):528–&EL), 4(4):528––553, 2013.

[7]

Ekkart Kindler, Vladimir Rubin, and Wilhelm Schäfer. Activity mining for discovering software process models. Software Engineering, 79:175–180, 2006.

[8]

Patrick Knab, Martin Pinzger, and Harald C Gall. Visual patterns in issue tracking data. In New Modeling Concepts for Today’s Software Processes, pages 222–233. Springer, 2010.

Digital Library

[9]

Andrew Meneely, Mackenzie Corcoran, and Laurie Williams. Improving developer activity metrics with issue tracking annotations. In Proceedings of the 2010 ICSE Workshop on Emerging Trends in Software Metrics, pages 75–80. ACM, 2010.

Digital Library

[10]

Wouter Poncin, Alexander Serebrenik, and Mark van den Brand. Process mining software repositories. In Software Maintenance and Reengineering (CSMR), 2011 15th European Conference on, pages 5–14. IEEE, 2011.

Digital Library

[11]

Anita Sarma, Larry Maccherone, Patrick Wagstrom, and James Herbsleb. Tesseract: Interactive visual exploration of socio-technical relationships in software development. In Software Engineering, 2009. ICSE 2009. IEEE 31st International Conference on, pages 23–33. IEEE, 2009.

Digital Library

[12]

Jinliang Song, Tiejian Luo, and Su Chen. Behavior pattern mining: Apply process mining technology to common event logs of information systems. In Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on, pages 1800–1805. IEEE, 2008.

[13]

Wikan Sunindyo, Thomas Moser, Dietmar Winkler, and Deepak Dhungana. Improving open source software process quality based on defect data mining. In Software Quality. Process Automation in Software Development, pages 84–102. Springer, 2012.

[14]

Ashish Sureka, Atul Goyal, and Ayushi Rastogi. Using social network analysis for mining collaboration data in a defect tracking system for risk and vulnerability analysis. In Proceedings of the 4th India Software Engineering Conference, pages 195–204. ACM, 2011.

Digital Library

[15]

Wil MP Van Der Aalst, Hajo A Reijers, and Minseok Song. Discovering social networks from event logs. Computer Supported Cooperative Work (CSCW), 14(6):549–593, 2005.

Digital Library

[16]

Wil MP van der Aalst, Hajo A Reijers, Anton JMM Weijters, Boudewijn F van Dongen, AK Alves de Medeiros, Minseok Song, and HMW Verbeek. Business process mining: An industrial application. Information Systems, 32(5):713–732, 2007.

Digital Library

Cited By

Ali MMilani FDumas M(2024)Data-Driven Identification and Analysis of Waiting Times in Business ProcessesBusiness & Information Systems Engineering10.1007/s12599-024-00868-5Online publication date: 15-May-2024
https://doi.org/10.1007/s12599-024-00868-5
Codabux ZFard FVerdecchia RPalomba FDi Nucci DRecupito G(2024)Teaching Mining Software RepositoriesHandbook on Teaching Empirical Software Engineering10.1007/978-3-031-71769-7_12(325-362)Online publication date: 25-Dec-2024
https://doi.org/10.1007/978-3-031-71769-7_12
Imran MHamid SIsmail M(2023)Advancing Process Audits With Process Mining: A Systematic Review of Trends, Challenges, and OpportunitiesIEEE Access10.1109/ACCESS.2023.329211711(68340-68357)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3292117
Show More Cited By

Index Terms

Process mining multiple repositories for software defect resolution from control and organizational perspective

Recommendations

Nirikshan: process mining software repositories to identify inefficiencies, imperfections, and enhance existing process capabilities
ICSE Companion 2014: Companion Proceedings of the 36th International Conference on Software Engineering

Process mining is to extract knowledge about business processes from data stored implicitly in ad-hoc way or explicitly by information systems. The aim is to discover runtime process, analyze performance and perform conformance verification, using ...
Process Mining: Overview and Opportunities

Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/...
Nirikshan: mining bug report history for discovering process maps, inefficiencies and inconsistencies
ISEC '14: Proceedings of the 7th India Software Engineering Conference

Issue tracking systems such as Bugzilla, Mantis and JIRA are Process Aware Information Systems to support business process of issue (defect and feature enhancement) reporting and resolution. The process of issue reporting to resolution consists of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

May 2014

427 pages

ISBN:9781450328630

DOI:10.1145/2597073

General Chair:
Premkumar Devanbu
University of California at Davis, USA
,
Program Chairs:
Sung Kim
Hong Kong University of Science and Technology, China
,
Martin Pinzger
University of Klagenfurt, Austria

Copyright © 2014 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 ACM 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]

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

In-Cooperation

TCSE: IEEE Computer Society's Tech. Council on Software Engin.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

ICSE '14

Sponsor:

SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

May 31 - June 1, 2014

Hyderabad, India

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

32
Total Citations
View Citations
680
Total Downloads

Downloads (Last 12 months)26
Downloads (Last 6 weeks)4

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ali MMilani FDumas M(2024)Data-Driven Identification and Analysis of Waiting Times in Business ProcessesBusiness & Information Systems Engineering10.1007/s12599-024-00868-5Online publication date: 15-May-2024
https://doi.org/10.1007/s12599-024-00868-5
Codabux ZFard FVerdecchia RPalomba FDi Nucci DRecupito G(2024)Teaching Mining Software RepositoriesHandbook on Teaching Empirical Software Engineering10.1007/978-3-031-71769-7_12(325-362)Online publication date: 25-Dec-2024
https://doi.org/10.1007/978-3-031-71769-7_12
Imran MHamid SIsmail M(2023)Advancing Process Audits With Process Mining: A Systematic Review of Trends, Challenges, and OpportunitiesIEEE Access10.1109/ACCESS.2023.329211711(68340-68357)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3292117
Malik PChaudhary K(2023)Mining Repository for Module Reuse: A Machine Learning-Based ApproachInternational Conference on IoT, Intelligent Computing and Security10.1007/978-981-19-8136-4_6(71-81)Online publication date: 2-Apr-2023
https://doi.org/10.1007/978-981-19-8136-4_6
Pons MBruel JRaclet JSilvestre F(2023)Finding Behavioral Indicators from Contextualized Commits in Software Engineering Courses with Process MiningFrontiers in Software Engineering Education10.1007/978-3-031-48639-5_5(56-68)Online publication date: 1-Dec-2023
https://doi.org/10.1007/978-3-031-48639-5_5
Urrea-Contreras SFlores-Rios BGonzález-Navarro FAstorga-Vargas MIbarra-Esquer JPacheco IAgüero C(2022)Process Mining Model Integrated with Control Flow, Case, Organizational and Time Perspectives in a Software Development Project2022 10th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT55708.2022.00022(92-101)Online publication date: Oct-2022
https://doi.org/10.1109/CONISOFT55708.2022.00022
Qamar KSülün ETüzün E(2022)Taxonomy of bug tracking process smellsInformation and Software Technology10.1016/j.infsof.2022.106972150:COnline publication date: 4-Aug-2022
https://dl.acm.org/doi/10.1016/j.infsof.2022.106972
Doğan ETüzün E(2022)Towards a taxonomy of code review smellsInformation and Software Technology10.1016/j.infsof.2021.106737142:COnline publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.infsof.2021.106737
Sayago-Heredia JPérez-Castillo RPiattini M(2021)A Systematic Mapping Study on Analysis of Code RepositoriesInformatica10.15388/21-INFOR454(1-42)Online publication date: 2-Jun-2021
https://doi.org/10.15388/21-INFOR454
Qamar KSulun ETuzun E(2021)Towards a Taxonomy of Bug Tracking Process Smells: A Quantitative Analysis2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA53835.2021.00026(138-147)Online publication date: Sep-2021
https://doi.org/10.1109/SEAA53835.2021.00026
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten