skip to main content
10.1145/3364641.3364647acmotherconferencesArticle/Chapter ViewAbstractPublication PagessbqsConference Proceedingsconference-collections
research-article

Effects of Visualizing Technical Debts on a Software Maintenance Project

Published: 28 October 2019 Publication History

Abstract

The technical debt (TD) metaphor is widely used to encapsulate numerous software quality problems. She describes the trade-off between the short term benefit of taking a shortcut during the design or implementation phase of a software product (for example, in order to meet a deadline) and the long term consequences of taking said shortcut, which may affect the quality of the software product. TDs must be managed to guarantee the software quality and also reduce its maintenance and evolution costs. However, the tools for TD detection usually provide results only considering the files perspective (class and methods), that is not usual during the project management. In this work, a technique is proposed to identify/visualize TD on a new perspective: software features. The proposed technique adopts Mining Software Repository (MRS) tools to identify the software features and after the technical debts that affect these features. Additionally, we also proposed an approach to support maintenance tasks guided by TD visualization at the feature level aiming to evaluate its applicability on real software projects. The results indicate that the approach can be useful to decrease the existent TDs, as well as avoid the introduction of new TDs.

References

[1]
Nicolli SR Alves, Thiago S Mendes, Manoel G de Mendonça, Rodrigo O Spínola, Forrest Shull, and Carolyn Seaman. 2016. Identification and management of technical debt: A systematic mapping study. Information and Software Technology 70 (2016), 100--121.
[2]
Forrest Shull Filippo Lanubile. Basili, Victor R. 1999. Building Knowledge through Families of Experiments. IEEE Transactions on Software Engineering (1999).
[3]
G Campbell and Patroklos P Papapetrou. 2013. SonarQube in action. Manning Publications Co.
[4]
Ward Cunningham. 1992. The WyCash Portfolio Management System. In Addendum to the Proceedings on Object-oriented Programming Systems, Languages, and Applications (Addendum) (OOPSLA '92). ACM, New York, NY, USA, 29--30. https://doi.org/10.1145/157709.157715
[5]
A. Aurum E. Tom and R. Vidgen. 2013. An exploration of technical debt. Journal of Systems and Software (2013).
[6]
N. Brown et al. 2010. Managing technical debt in software-reliant systems. Proceedings of the FSE/SDP workshop on Future of software engineering research (2010).
[7]
M. Zanoni F. Arcelli, C. Tosi and S. Maggioni. 2008. The marple project: A tool for design pattern detection and software architecture reconstruction. In 1st International Workshop on Academic Software Development Tools and Techniques (WASDeTT-1) (2008).
[8]
Werney A. L. Lira F. Vanderson M. A., Pedro A. S. Neto and Irvayne M. S. Ibiapina. 2018. Analysis of Code Familiarity in Module and Functionality Perspectives. SBQS 2018: XVII Simpósio Brasileiro de Qualidade de Software (2018).
[9]
Felipe G, Thiago S Novais, Mendes, Renato Gonçalves, Renato Novais, Rodrigo O Spınola, Manoel Mendonça, and BA Salvador. 2015. RepositoryMiner-: uma ferramenta extensível de mineração de repositórios de software para identificação automática de Dívidas Técnicas. (2015).
[10]
C. Fernández-Sánchez H. Humanes, J. Garbajosa and J. Díaz. 2017. An open tool for assisting in technical debt management. In Software Engineering and Advanced Applications (SEAA). IEEE, 43rd Euro micro Conference on, pages 400--403 (2017).
[11]
A. E. Hassan. 2008. The road ahead for mining software repositories. In Frontiers of Software Maintenance (2008), 48--57.
[12]
Hadi Hemmati, Sarah Nadi, Olga Baysal, Oleksii Kononenko, Wei Wang, Reid Holmes, and MichaelWGodfrey. 2013. The msr cookbook: Mining a decade of research. In Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. IEEE, 343--352.
[13]
J. Holvitie and V. Leppänen. 2013. Debtflag: Technical debt management with a development environment integrated tool. In Managing Technical Debt (MTD), 2013 4th International Workshop on, pages 20--27. IEEE, 2013. (2013).
[14]
Moreno A. M. Juzgado, N.J. and S. Vegas. 2004. Reviewing 25 Years of Testing Technique Experiments. Empirical Software Engineering, 9(1-2) (2004), 7--44.
[15]
M. FOWLER K., BECK. 1999. Refactoring: improving the design of existing code. Addison-Wesley Professional (1999).
[16]
J. I.; SHARIF-B. KAGDI, H.; MALETIC. 2007. Mining software repositories for traceability links. 15th IEEE International Conference on Program Comprehension, ICPC '07. (2007), 145--154.
[17]
KERIEVSKY. 2005. Refactoring to patterns. Pearson Deutschland GmbH (2005).
[18]
M Kersten and Gail C Murphy. 2005. Mylar: a degree-of-interest model for IDEs. 4th international conference on Aspectoriented software development (AOSD) (2005), 159--168.
[19]
Michele Lanza and Radu Marinescu. 2007. Object-oriented metrics in practice: using software metrics to characterize, evaluate, and improve the design of object-oriented systems. Springer Science & Business Media.
[20]
A. Martini and J. Bosch. 2017. The magnificent seven: towards a systematic estimation of technical debt interest. In Proceedings of the XP2017 Scientific Workshops, page 7. ACM, (2017).
[21]
Steve McConnell. 2007. Technical debt. Software Best Practices, Nov (2007).
[22]
E. G. McIver, J. P.and Carmines. 1991. Unidimensional Scaling. Sage Publications. (1991).
[23]
Thiago Mendes, Renato Novais, Manoel Mendonca, Luis Carvalho, and Felipe Gomes. 2017. RepositoryMiner - uma ferramenta extensível de mineração de repositórios de software para identificação automática de Dívida Técnica. In CBSoft 2017 - Sessao de Ferramentas (). http://XXXXX/170925.pdf
[24]
Thiago S Mendes, David P Gonçalves, Felipe G Gomes, Renato Novais, Rodrigo O Spınola, Manoel Mendonça, and BA Salvador. 2015. VisminerTD: Uma Ferramenta para Identificaç ao Automática e Monitoramento Interativo de Dıvida Técnica. (2015).
[25]
Fracisco Moura, Werney Lira, Irvayne Ibiapina, and Pedro Neto. 2016. Codivision: Uma Ferramenta para Mapear a Divisão do Conhecimento entre os Desenvolvedores a partir da Análise de Repositório de Código. Congresso Brasileiro de Software - Cbsoft (2016).
[26]
A. Vetró F. Shull N. Zazworka, R. O. Spínola and C. Seaman. 2013. A case study on effectively identifying technical debt. EASE '13: Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering (2013).
[27]
Roger S. PRESSMAN. 2011. Software Engineering. New York, NY, USA: McGraw-Hill Science, (2011).
[28]
SonarSource S.A. 2019. SonarQube. Capturado em: http://www.sonarqube.org/, abril 2019. (2019).
[29]
Diomidis. SPINELLIS. 2005. Version control systems. Software,. IEEE, v. 22, n. 5, (2005), 108--109.
[30]
Davor; GERMAN Daniel M. STOREY, Margaret-Anne D.; ČUBRANIĆ. 2005. On the use of visualization to support awareness of human activities in software development: a survey and a framework. ACM SYMPOSIUM ON SOFTWARE VISUALIZATION, (2005), 193---202.
[31]
KLINGER T. et al. 2011. An enterprise perspective on technical debt. In: ACM. Proceedings of the 2nd Workshop on Managing Technical Debt. (2011).
[32]
Runeson P. Martin Höst M. C. O. Regnell B. Wohlin, C. and A. Wesslén. 2000. Experimentation in Software Engineering: An Introduction. The Kluwer Internation Series in Software Engineering. Kluwer Academic Publishers, Norwell, Massachusets, USA. (2000).
[33]
Limsoon. ZAKI, Mohammed. WONG. 2003. Data Mining Techniques. WSPC/Lecture Notes Series. (2003).
[34]
Paris Avgeriou Zengyang Li and Peng Liang. 2015. Software Aging. In A systematic mapping study on technical debt and its management. (ICSE '94). Journal of Systems and Software 101, Supplement C (2015), 193--220. https://doi.org/10.1016/j.jss.2014.12.027

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
SBQS '19: Proceedings of the XVIII Brazilian Symposium on Software Quality
October 2019
330 pages
ISBN:9781450372824
DOI:10.1145/3364641
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]

In-Cooperation

  • SBC: Brazilian Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Refactoring
  2. Repository Mining
  3. Technical Debt
  4. features Identification

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

SBQS'19
SBQS'19: XVIII Brazilian Symposium on Software Quality
October 28 - November 1, 2019
Fortaleza, Brazil

Acceptance Rates

SBQS '19 Paper Acceptance Rate 35 of 99 submissions, 35%;
Overall Acceptance Rate 35 of 99 submissions, 35%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 110
    Total Downloads
  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 27 Dec 2024

Other Metrics

Citations

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