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Resultados de búsqueda para requirements engineering

A methodology to automatically translate user requirements into visualizations: Experimental validation

Context: Information visualization is paramount for the analysis of Big Data. The volume of data requiring interpretation is continuously growing. However, users are usually not experts in information visualization. Thus, defining the visualization that best suits a determined context is a very challenging task for them. Moreover, it is often the case that users do not have a clear idea of what objectives they are building the visualizations for. Consequently, it is possible that graphics are misinterpreted, making wrong decisions that lead to missed opportunities. One of the underlying problems in this process is the lack of methodologies and tools that non-expert users in visualizations can use to define their objectives and visualizations.Objective: The main objectives of this paper are to (i) enable non-expert users in data visualization to communicate their analytical needs with little effort, (ii) generate the visualizations that best fit their requirements, and (iii) evaluate the impact of our proposal with reference to a case study, describing an experiment with 97 non-expert users in data visualization.Methods: We propose a methodology that collects user requirements and semi-automatically creates suitable visualizations. Our proposal covers the whole process, from the definition of requirements to the implementation of visualizations. The methodology has been tested with several groups to measure its effectiveness and perceived usefulness.Results: The experiments increase our confidence about the utility of our methodology. It significantly improves over the case when users face the same problem manually. Specifically: (i) users are allowed to cover more analytical questions, (ii) the visualizations produced are more effective, and (iii) the overall satisfaction of the users is larger.Conclusion: By following our proposal, non-expert users will be able to more effectively express their analytical needs and obtain the set of visualizations that best suits their goals.

Autores: Ana Lavalle / Alejandro Maté / Juan Trujillo / Miguel A. Teruel / Stefano Rizzi / 
Palabras Clave: Big Data analytics - data visualization - Experimental validation - Model-Driven Development - requirements engineering

Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train Control and Management Case

Traceability Link Recovery (TLR) has been a topic of interest for many years within the software engineering community. In recent years, TLR has been attracting more attention, becoming the subject of both fundamental and applied research. However, there still exists a large gap between the actual needs of industry on one hand and the solutions published through academic research on the other.In this work, we propose a novel approach, named Evolutionary Learning to Rank for Traceability Link Recovery (TLR-ELtoR). TLR-ELtoR recovers traceability links between a requirement and a model through the combination of evolutionary computation and machine learning techniques, generating as a result a ranking of model fragments that can realize the requirement.TLR-ELtoR was evaluated in a real-world case study in the railway domain, comparing its outcomes with five TLR approaches (Information Retrieval, Linguistic Rule-based, Feedforward Neural Network, Recurrent Neural Network, and Learning to Rank). The results show that TLR-ELtoR achieved the best results for most performance indicators, providing a mean precision value of 59.91+ACU, a recall value of 78.95+ACU, a combined F-measure of 62.50+ACU, and a MCC value of 0.64. The statistical analysis of the results assesses the magnitude of the improvement, and the discussion presents why TLR-ELtoR achieves better results than the baselines.

Autores: Ana Cristina Marcén / Raúl Lapeña / Oscar Pastor / Carlos Cetina / 
Palabras Clave: Evolutionary algorithm - Learning to Rank - models - requirements engineering - Traceability Link Recovery

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Autores: Alessia Knauss, Daniela Damian, Xavier Franch, Angela Rook, Hausi A. Müller, Alex Thomo Revista: Informacion & Software Technology 70: 85-99 (2016) DOI: http://dx.doi.org/10.1016/j.infsof.2015.10.001 JCR IF 2015: 1.569 (primer cuartil de la categoría de ingeniería del software) 3 citas (excluyendo self-citations)

Autores: Alessia Knauss / Daniela Damian / Xavier Franch / Angela Rook / Hausi A. Müller / Alex Thomo / 
Palabras Clave: Contextual requirements - requirements engineering - Self-adaptive systems

Requirements reuse and requirement patterns: a state of the practice survey

Autores: Cristina Palomares, Carme Quer, Xavier Franch Revista: Empirical Software Engineering (Springer), in press DOI: http://dx.doi.org/10.1007/s10664-016-9485-x JCR IF 2015: 1.393 (27/106 de la categoría de ingeniería del software)

Autores: Cristina Palomares / Carme Quer / Xavier Franch / 
Palabras Clave: exploratory survey - online questionnaire - requirements engineering - software rerquirement patterns

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