Applying Test Driven Development in the Big Data Domain – Lessons From the Literature
Abstract
Big data has evolved to a ubiquitous part of today's society. However, despite its popularity, the development and testing of the corresponding applications are still very challenging tasks that are being actively researched in pursuit of ways for improvement. One newly introduced proposition is the application of test driven development (TDD) in the big data domain. To facilitate this concept, existing literature reviews on TDD have been analyzed to extract insights from those sources of aggregated knowledge, which can be applied to this new setting. After introducing the different studies, lessons for the application of TDD in the big data domain are deducted and discussed. Finally, avenues for future works are proposed.
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TDD offers a promising solution for big data applications with its iterative and incremental approach, focusing on writing tests first.
“TDD offers a promising solution with its iterative and incremental approach, focusing on writing tests first.”
From Test Driven Development and Its Impact on Program Design and Software Quality: A Systematic Literature Review (2023) · cites this paper as [21]
TDD has the potential to be used in Big Data projects.
“As discussed by Staegemann et al., TDD has the potential to be used in Big Data projects [21].”
From Test Driven Development and Its Impact on Program Design and Software Quality: A Systematic Literature Review (2023) · cites this paper as [21]
The implementation of TDD can be difficult in big data projects; with the volume of the data and the lack of effective tools making the implementation of the approach a challenge.
“The implementation of TDD can also be difficult in big data projects; with the volume of the data and the lack of effective tools making the implementation of the approach a challenge [21].”
From Test Driven Development and Its Impact on Program Design and Software Quality: A Systematic Literature Review (2023) · cites this paper as [21]
By embracing TDD, organizations can enhance reliability, reduce errors, and increase confidence in the outputs of their big data applications.
“By embracing TDD, organizations can enhance reliability, reduce errors, and increase confidence in the outputs of their big data applications [21].”
From Test Driven Development and Its Impact on Program Design and Software Quality: A Systematic Literature Review (2023) · cites this paper as [21]
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- Authors
- Staegemann, D., Volk, M., Lautenschlager, E., Pohl, M., Abdallah, M., & Turowski, K.
- Affiliations
- Otto-von-Guericke-Universität Magdeburg (DE)
- Al-Zaytoonah University of Jordan (JO)
- Venue
- 2021 International Conference on Information Technology (ICIT)
- Publisher
- IEEE
- Published
- 2021-07-14
- Pages
- 511-516
- OpenAlex
- W3184112328
- Cited by
- 19
- References
- 59
- Citations by year
- 2024: 3 · 2023: 7 · 2022: 9
- FWCI
- 3.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- test-driven development, big data applications, software testing, software development, lessons learned
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