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How do developers fix issues and pay back technical debt in the Apache ecosystem?

Authors: Digkas, Georgios; Lungu, Mircea; Avgeriou, Paris; Chatzigeorgiou, Alexander; Ampatzoglou, Apostolos;

How do developers fix issues and pay back technical debt in the Apache ecosystem?

Abstract

During software evolution technical debt (TD) follows a constant ebb and flow, being incurred and paid back, sometimes in the same day and sometimes ten years later. There have been several studies in the literature investigating how technical debt in source code accumulates during time and the consequences of this accumulation for software maintenance. However, to the best of our knowledge there are no large scale studies that focus on the types of issues that are fixed and the amount of TD that is paid back during software evolution. In this paper we present the results of a case study, in which we analyzed the evolution of fifty-seven Java open-source software projects by the Apache Software Foundation at the temporal granularity level of weekly snapshots. In particular, we focus on the amount of technical debt that is paid back and the types of issues that are fixed. The findings reveal that a small subset of all issue types is responsible for the largest percentage of TD repayment and thus, targeting particular violations the development team can achieve higher benefits.

Country
Netherlands
Subjects by Vocabulary

Microsoft Academic Graph classification: Source code Computer science media_common.quotation_subject Software Empirical research media_common business.industry Software maintenance Market research Risk analysis (engineering) Technical debt Scale (social sciences) business Software evolution

Keywords

Market research, Empirical Study, source code, software evolution, Monitoring, public domain software, Ecosystems, source code (software), Tools, Java open-source software projects, Open source software, Mining Software Repositories, software maintenance, technical debt, Apache ecosystem, Java, Apache Software Foundation

18 references, page 1 of 2

[1] P. Kruchten, R. L. Nord, and I. Ozkaya, “Technical debt: From metaphor to theory and practice,” Ieee software, vol. 29, no. 6, pp. 18-21, 2012.

[2] Z. Li, P. Avgeriou, and P. Liang, “A systematic mapping study on technical debt and its management,” Journal of Systems and Software, vol. 101, pp. 193-220, 2015. [OpenAIRE]

[3] M. Lungu, “Reverse engineering software ecosystems,” Ph.D. dissertation, University of Lugano, Nov. 2009. [Online]. Available: http://scg.unibe.ch/archive/papers/Lung09b.pdf

[4] K. Manikas, “Revisiting software ecosystems research,” J. Syst. Softw., vol. 117, no. C, pp. 84-103, Jul. 2016. [Online]. Available: http://dx.doi.org/10.1016/j.jss.2016.02.003

[5] G. Bavota, G. Canfora, M. Di Penta, R. Oliveto, and S. Panichella, “The evolution of project inter-dependencies in a software ecosystem: The case of apache.” in ICSM, 2013, pp. 280-289.

[6] S. Olbrich, D. S. Cruzes, V. Basili, and N. Zazworka, “The evolution and impact of code smells: A case study of two open source systems,” in Proceedings of the 2009 3rd international symposium on empirical software engineering and measurement. IEEE Computer Society, 2009, pp. 390-400. [OpenAIRE]

[7] R. Peters and A. Zaidman, “Evaluating the lifespan of code smells using software repository mining,” in Software Maintenance and Reengineering (CSMR), 2012 16th European Conference on. IEEE, 2012, pp. 411-416.

[8] A. Chatzigeorgiou and A. Manakos, “Investigating the evolution of code smells in object-oriented systems,” Innovations in Systems and Software Engineering, vol. 10, no. 1, pp. 3-18, 2014.

[9] N. Zazworka, M. A. Shaw, F. Shull, and C. Seaman, “Investigating the impact of design debt on software quality,” in Proceedings of the 2nd Workshop on Managing Technical Debt. ACM, 2011, pp. 17-23.

[10] M. Tufano, F. Palomba, G. Bavota, R. Oliveto, M. Di Penta, A. De Lucia, and D. Poshyvanyk, “When and why your code starts to smell bad,” in Proceedings of the 37th International Conference on Software Engineering-Volume 1. IEEE Press, 2015, pp. 403-414.

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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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35
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83
184