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Towards a Mapping of Software Technical Debt onto Testware | IEEE Conference Publication | IEEE Xplore

Towards a Mapping of Software Technical Debt onto Testware


Abstract:

Technical Debt (TD) is a metaphor used to explain the negative impacts that sub-optimal design decisions have in the long-term perspective of a software project. Although...Show More

Abstract:

Technical Debt (TD) is a metaphor used to explain the negative impacts that sub-optimal design decisions have in the long-term perspective of a software project. Although TD is acknowledged by both researchers and practitioners to have strong negative impact on Software development, its study on Testware has so far been very limited. A gap in knowledge that is important to address due to the growing popularity of Testware (scripted automated testing) in software development practice.In this paper we present a mapping analysis that connects 21 well-known, Software, object-oriented TD items to Testware, establishing them as Testware Technical Debt (TTD) items. The analysis indicates that most Software TD items are applicable or observable as TTD items, often in similar form and with roughly the same impact as for Software artifacts (e.g. reducing quality of the produced artifacts, lowering the effectiveness and efficiency of the development process whilst increasing costs). In the analysis, we also identify three types of connections between software TD and TTD items with varying levels of impact and criticality. Additionally, the study finds support for previous research results in which specific TTD items unique to Testware were identified. Finally, the paper outlines several areas of future research into TTD.
Date of Conference: 30 August 2017 - 01 September 2017
Date Added to IEEE Xplore: 28 September 2017
ISBN Information:
Conference Location: Vienna, Austria
References is not available for this document.

I. Introduction

Technical debt (TD) has become a generally acknowledged concept in industrial practice that describes the increased costs of development and maintenance of sub-optimal Software [1]. TD is also associated with interest that is the cost of adding, changing or removing code from the sub-optimal solution compared to the optimal one [2]. An analysis of the collective global TD, performed by Gartner in 2010, estimated its cost to $500 billion US and that this figure could double by the end of 2015 [3]. Because of the financial impact of TD, the concept has received attention from academia [2], [4]–[7]. However, despite research to suggest the presence of TD also in Testware [8], [9], TD in Testware is still an unexplored research area.

Select All
1.
W. Cunningham, “The wycash portfolio management system,” ACM SIGPLAN OOPS Messenger, vol. 4. no. 2, pp. 29–30, 1993.
2.
A. Nugroho, J. Visser, and T. Kuipers, “An empirical model of technical debt and interest,” in Proceedings of the 2nd Workshop on Managing Technical Debt. ACM, 2011, pp. 1–8.
3.
P. Thibodeau, “Counting ‘technical debt’,” Information Age, vol. 64, 2011.
4.
M. Fowler, Refactoring: improving the design of existing code. Pearson Education India, 1999.
5.
R. J. Eisenberg, “A threshold based approach to technical debt,” ACM SIGSOFT Software Engineering Notes, vol. 37, no. 2, pp. 1–6, 2012.
6.
R. Marinescu, “Assessing technical debt by identifying design flaws in software systems,” IBM Journal of Research and Development, vol. 56, no. 5, pp. 9–1, 2012.
7.
N. Brown, Y. Cai, Y. Guo, R. Kazman, M. Kim, P. Kruchten, E. Lim, A. MacCormack, R. Nord, I. Ozkaya, “Managing technical debt in software-reliant systems,” in Proceedings of the FSE/SDP workshop on Future of software engineering research. ACM, 2010, pp. 47–52.
8.
K. Wiklund, S. Eldh, D. Sundmark, and K. Lundqvist, “Technical debt in test automation,” in 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation (ICST). IEEE, 2012, pp. 887–892.
9.
E. Alégroth, M. Steiner, and A. Martini, “Exploring the presence of technical debt in industrial gui-based testware: A case study,” in 2016 IEEE Ninth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2016, pp. 257–262.
10.
D. Ståhl and J. Bosch, “Modeling continuous integration practice differences in industry software development,” Journal of Systems and Software, vol. 87, pp. 48–59. 2014.
11.
S. Berner, R. Weber, and R. K. Keller, “Observations and lessons learned from automated testing,” in Proceedings of 27th International Conference on Software Engineering, IEEE, 2005, pp. 571–579.
12.
S. R. Dalal, A. Jain, N. Karunanithi, J. Leaton, C. M. Lott, G. C. Patton, and B. M. Horowitz, “Model-based testing in practice,” in Proceedings of the 21st international conference on Software engineering. ACM, 1999, pp. 285–294.
13.
A. M. Memon, M. E. Pollack, and M. L. Soffa, “Automated test oracles for guis,” in ACM SIGSOFT Software Engineering Notes, vol. 25, no. 6. ACM, 2000, pp. 30–39.
14.
S. R. Shahamiri, W. M. N. W. Kadir, and S. Z. Mohd-Hashim, “A comparative study on automated software test oracle methods,” in Software Engineering Advances, 2009. ICSEA 09. Fourth International Conference on. IEEE, 2009, pp. 140–145.
15.
P. Hamill, Unit Test Frameworks: Tools for High-Quality Software Development. “OReilly Media. Inc.”, 2004.
16.
D. J. Richardson and A. L. Wolf, “Software testing at the architectural level,” in Joint proceedings of the second international software architecture workshop (ISAW-2) and international workshop on multiple perspectives in software development (Viewpoints 96) on SIGSOFT96 workshops. ACM, 1996, pp. 68–71.
17.
H. Srikanth, L. Williams, and J. Osborne, “System test case prioritization of new and regression test cases,” in 2005 International Symposium on Empirical Software Engineering. IEEE, 2005, pp. 10 -pp.
18.
Q. Luo, F. Hariri, L. Eloussi, and D. Marinov, “An empirical analysis of flaky tests,” in Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, 2014, pp. 643–653.
19.
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.
20.
T. Erl, SOA design patterns. Prentice Hall, 2009.
21.
T. Erl, T. Erl, H. rn Wilhelmsen, and C. Pautasso, SOA with REST: principles, patterns constraints for building enterprise solutions with REST, ser. The Prentice Hall Service Technology Series from Thomas Erl. Prentice Hall, 2012.
22.
N. Moha and Y. Guéhéneuc, “DECOR: A method for the specification and detection of code and design smells,” IEEE Transactions on Software Engineering, vol. 36, no. 1, pp. 20–36, 2010.
23.
N. Tsantalis, T. Chaikalis, and A. Chatzigeorgiou, “JDeodorant: Identification and removal of type-checking bad smells,” in Proceedings of the European Conference on Software Maintenance and Reengineering, CSMR. Athens, Greece : IEEE, 2008, pp. 329–331.
24.
F. Palma, M. Nayrolles, N. Moha, Y.-G. Guéhéneue, B. Baudry, and J.-M. Jézéquel, “SOA ANTIPATTERNS: AN APPROACH FOR THEIR SPECIFICATION AND DETECTION,” International Journal of Cooperative Information Systems, vol. 22. no. 04. dec 2013.
25.
G. Hecht, O. Benomar, R. Rouvoy, N. Moha, and L. Duchien, “Tracking the Software Quality of Android Applications Along Their Evolution,” in 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE). Lincoln, Nebraska, USA : IEEE, 2015, pp. 236–247.
26.
G. Bavota, B. De Carluccio, A. De Lucia, M. Di Penta, R. Oliveto, and O. Strollo, “When Does a Refactoring Induce Bugs? An Empirical Study,” in 2012 IEEE 12th International Working Conference on Source Code Analysis and Manipulation. IEEE, 2012, pp. 104–113.
27.
G. Bavota, A. De Lucia, M. Di Penta, R. Oliveto, and F. Palomba, “An experimental investigation on the innate relationship between quality and refactoring,” Journal of Systems and Software, vol. 107, pp. 1–14.2015.
28.
T. Besker, A. Martini, and J. Bosch, “A Systematic Literature Review and a Unified Model of ATD,” in 2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Limassol, Cyprus, 2016, pp. 189–197.
29.
A. Martini and J. Bosch, “Towards Prioritizing Architecture Technical Debt: Information Needs of Architects and Product Owners,” Proceedings of 41st Euromicro Conference on Software Engineering and Advanced Applications. SEAA 2015, pp. 422–429. 2015.
30.
A. Martini, J. Bosch, and M. Chaudron, “Investigating Architectural Technical Debt accumulation and refactoring over time: A multiple-case study,” Information and Software Technology, vol. 67, pp. 237–253, 2015.

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References

References is not available for this document.