[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/2791405.2791466acmotherconferencesArticle/Chapter ViewAbstractPublication PageswciConference Proceedingsconference-collections
research-article

Post Release Versions based Code Change Quality Metrics

Published: 10 August 2015 Publication History

Abstract

Software Metric is a quantitative measure of the degree to which a system, component or process possesses a given attribute. Bug fixing, new features (NFs) introduction and feature improvements (IMPs) are the key factors in deciding the next version of software. For fixing an issue (bug/new feature/feature improvement), a lot of changes have to be incorporated into the source code of the software. These code changes need to be understood by software engineers and managers when performing their daily development and maintenance tasks. In this paper, we have proposed four new metrics namely code change quality, code change density, file change quality and file change density to understand the quality of code changes across the different versions of five open source software products, namely Avro, Pig, Hive, jUDDI and Whirr of Apache project. Results show that all the products get better code change quality over a period of time. We have also observed that all the five products follow the similar code change trend.

References

[1]
Hassan, A. E. 2009. Predicting faults based on complexity of code change. In International Conference on Software Engineering. 78--88.
[2]
Chaturvedi, K. K., Kapur, P. K., Anand, S., and Singh, V.B. 2014. Predicting the complexity of code changes using entropy based measures. In International Journal of System Assurance Engineering and Management, Springer. 5, 155--164.
[3]
Shannon, C. E. (1948), 'A Mathematical Theory of Communication', The Bell System Technical Journal, 27, 379--423, 623--656.
[4]
http://www.apache.org/
[5]
https://github.com/
[6]
Ying, A. Murphy, G. Ng, R. Chu-Carroll, M. 2004. Predicting source code changes by mining change history. IEEE Transactions on Software Engineering (TSE), 30, 9, 574--586.
[7]
Zimmermann, T., Zeller, A., Weissgerber, P. and Diehl, S. 2005. Mining version histories to guide software changes. IEEE Transactions on Software Engineering, 31, 6, 429--445.
[8]
Nagappan, N. and Ball, T. 2005. Use of relative code churn measures to predict system defect density. In Proceeding of International Conference on Software Engineering (ICSE'05), Saint Louis MO, 284--292.
[9]
Shao, D., Khurshid, S. and Perry D. E. 2009. Semantic impact and faults in source code changes: An empirical study. In Proceeding of Software Engineering Conference, ASWEC '09. Australian, 131--141.
[10]
Pan, K., Kim, S., James, E. and Whitehead, Jr. 2009. Toward an understanding of bug fix patterns. Empirical Software Engineering, 14, 3, 286--315.
[11]
Osman, H., Lungu, M. and Nierstrasz, O. 2014. Mining frequent bug-fix code changes. In IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE), 343--347.
[12]
Giger, E., Pinzger, M. and Gall, H. C. 2011. Comparing fine-grained source code changes and code churn for bug prediction. In Proceeding of International Workshop on Mining Software Repositories. 83--92.
[13]
Negara, S., Codoban, M., Dig, D. and Johnson, R. E. 2014. Mining Fine-Grained Code Changes to Detect Unknown Change Patterns. In Proceeding of International Conference on Software Engineering. In press.
[14]
Kim, S., Whitehead, J. and Zhang, Y. 2008. Classifying software changes: Clean or buggy? IEEE Transaction Software Engineering, 34, 2, 181--196.
[15]
Giger, E., Pinzger, M. and Gall, H. 2012. Can we predict types of code changes? an empirical analysis. In MSR'12. 217--226. IEEE CS.
[16]
Tao, Y., Dang, Y., Xie, T., Zhang, D., and Kim, S. 2012. How Do Software Engineers Understand Code Changes? An Exploratory Study in Industry. In Proceedings of the 20th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2012). Research Triangle Park, North Carolina.
[17]
Kim, M. and Notkin, D. 2009. Discovering and representing systematic code changes. In Proceedings of International Conference on Software Engineering ICSE'09, 309--319.
[18]
Nguyen, T. T., Nguyen, H. A., Pham, N. H., Al-Kofahi, J. and Nguyen, T. N. 2009. Clone-aware configuration management. ln ASE'09, 123--134.
[19]
Singh, V.B. and Sharma, M. 2014. Prediction of the complexity of code changes based on number of open bugs, new feature and feature improvement. In Proceedings of the 25thIEEE International Symposium on Software Reliability Engineering (ISSRE), WOSD. Neples, Italy, 478--483.

Cited By

View all
  • (2023)An empirical investigation of social comparison and open source community healthInformation Systems Journal10.1111/isj.1248534:2(499-532)Online publication date: 15-Nov-2023
  • (2021)Open Source Community Health: Analytical Metrics and Their Corresponding Narratives2021 IEEE/ACM 4th International Workshop on Software Health in Projects, Ecosystems and Communities (SoHeal)10.1109/SoHeal52568.2021.00010(25-33)Online publication date: May-2021

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics
August 2015
763 pages
ISBN:9781450333610
DOI:10.1145/2791405
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Entropy
  2. Feature improvement
  3. New feature
  4. Open Source Software
  5. Software Repositories

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

WCI '15

Acceptance Rates

WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;
Overall Acceptance Rate 98 of 452 submissions, 22%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 12 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2023)An empirical investigation of social comparison and open source community healthInformation Systems Journal10.1111/isj.1248534:2(499-532)Online publication date: 15-Nov-2023
  • (2021)Open Source Community Health: Analytical Metrics and Their Corresponding Narratives2021 IEEE/ACM 4th International Workshop on Software Health in Projects, Ecosystems and Communities (SoHeal)10.1109/SoHeal52568.2021.00010(25-33)Online publication date: May-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media