Abstract
Background: Context modeling to support the elicitation of context-aware functionalities has been overlooked due to its high complexity. To help overcome this, we have implemented a data-driven process that analyzes contextual data and generates data-driven context models. Objective: We aim at investigating to which extent a data-driven context model supports the identification of more complex contexts (i.e., contexts that combine several contextual elements) and unexpected context-aware functionalities. Method: We used a one factor with two treatments randomized design with 13 experienced software engineers. Given a specific system-supported user task, the participants were asked to come up with requirements that describe context-aware functionalities to improve the user task. Results: Use of the data-driven context model increased the average number of contextual elements used to describe requirements from 1.77 to 4.23. No participant from the control group was able to identify by themselves any of the contexts included in the model. All comparisons between groups had sufficient effect size and power. The participants regarded the data-driven context model as a useful tool to support the elicitation of context-aware functionalities. Conclusion: The data-driven context model has shown potential to support the identification of relevant contexts for given user tasks.
This work has been partially supported by CNPq, Brazil.
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Notes
- 1.
- 2.
All materials are available at https://doi.org/10.5281/zenodo.5090748.
- 3.
- 4.
The anonymized raw data is available at https://doi.org/10.5281/zenodo.5090748.
- 5.
For H1, H2 and H3, we used \({\boldsymbol{\alpha }} = \mathbf{0 .05}\) as significance level and \({\boldsymbol{\beta }} = \mathbf{0 .2}\).
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Falcão, R., Trapp, M., Vieira, V., Vianna Dias da Silva, A. (2021). Using a Data-Driven Context Model to Support the Elicitation of Context-Aware Functionalities – A Controlled Experiment. In: Ardito, L., Jedlitschka, A., Morisio, M., Torchiano, M. (eds) Product-Focused Software Process Improvement. PROFES 2021. Lecture Notes in Computer Science(), vol 13126. Springer, Cham. https://doi.org/10.1007/978-3-030-91452-3_8
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