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Evaluating different i*-based approaches for selecting functional requirements while balancing and optimizing non-functional requirements: A controlled experiment
- Source :
- Repositori Universitat Jaume I, Universitat Jaume I, RUA. Repositorio Institucional de la Universidad de Alicante, Universidad de Alicante (UA)
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Context: A relevant question in requirements engineering is which set of functional requirements (FR) to prioritize and implement, while keeping non-functional requirements (NFR) balanced and optimized. Objective: We aim to provide empirical evidence that requirement engineers may perform better at the task of selecting FRs while optimizing and balancing NFRs using an alternative (automated) i* post-processed model, compared to the original i* model. Method: We performed a controlled experiment, designed to compare the original i* graphical notation, with our post-processed i* visualizations based on Pareto efficiency (a tabular and a radar chart visualization). Our experiment consisted of solving different exercises of various complexity for selecting FRs while balancing NFR. We considered the efficiency (time spent to correctly answer exercises), and the effectiveness (regarding time: time spent to solve exercises, independent of correctness; and regarding correctness of the answer, independent of time). Results: The efficiency analysis shows it is 3.51 times more likely to solve exercises correctly with our tabular and radar chart visualizations than with i*. Actually, i* was the most time-consuming (effectiveness regarding time), had a lower number of correct answers (effectiveness regarding correctness), and was affected by complexity. Visual or textual preference of the subjects had no effect on the score. Beginners took more time to solve exercises than experts if i* is used (no distinction if our Pareto-based visualizations are used). Conclusion: For complex model instances, the Pareto front based tabular visualization results in more correct answers, compared to radar chart visualization. When we consider effectiveness regarding time, the i* graphical notation is the most time consuming visualization, independent of the complexity of the exercise. Finally, regarding efficiency, subjects consume less time when using radar chart visualization than tabular visualization, and even more so compared to the original i* graphical notation. Sven Casteleyn is funded under the Ramón y Cajal Program of the Spanish Government, grant number RYC-2014-16606. This work has been partially supported by the Publi@City project (TIN2016-78103-C2-2-R) from the Spanish Ministry of Economy and Competitiveness.
- Subjects :
- Controlled experiment
0106 biological sciences
Correctness
Non-functional requirement
Computer science
Pareto efficiency
Context (language use)
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
I
Estadística e Investigación Operativa
0202 electrical engineering, electronic engineering, information engineering
Requirements engineering
business.industry
Pareto principle
020207 software engineering
Functional requirement
Computer Science Applications
Visualization
Lenguajes y Sistemas Informáticos
Radar chart
Artificial intelligence
business
computer
Software
010606 plant biology & botany
Information Systems
Subjects
Details
- ISSN :
- 09505849
- Volume :
- 106
- Database :
- OpenAIRE
- Journal :
- Information and Software Technology
- Accession number :
- edsair.doi.dedup.....550f4743fe7b577f34da6db0c2511161