Abstract:Citation: MAGUIRE, M., 2013. An analysis of specialist and non-specialist user requirements for geographic climate change information. Applied Ergonomics, 44 (6), pp. 874 885. This item was submitted to Loughborough's Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions.For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ 1 An analysis of specialist and non-specialist user… Show more
“…A relevant aspect that is worth highlighting is that in 24 of the 109 studies (∼22%) more than one requirements elicitation technique was used [44–46, 48, 51, 52, 56, 59, 61, 69, 70, 77, 79, 87, 94, 99, 104–106, 112, 120, 132, 136, 153].…”
Requirements elicitation is a critical activity that forms part of the requirements engineering process because it has to discover what the software must do through a solid understanding of the wishes and needs of the various stakeholders and to transform them into software requirements. However, in spite of its relevance, there are only a few systematic literature reviews that provide scientific evidence about the effectiveness of the techniques used to elicit software requirements. This study presents a systematic review of relevant literature on requirements elicitation techniques, from 1993 to 2015, by addressing two research questions: Which mature techniques are currently used for eliciting software requirements? and Which mature techniques improve the elicitation effectiveness? Prior literature assumes that such 'maturity' leads to a better-quality understanding of stakeholders' desires and needs, and thus an increased likelihood that a resulting software will satisfy those requirements. This research paper found 140 studies to answer these questions. The findings describe which elicitation techniques are effective and in which situations they work best, taking into account the product which must be developed, the stakeholders' characteristics, the type of information obtained, among other factors.
“…A relevant aspect that is worth highlighting is that in 24 of the 109 studies (∼22%) more than one requirements elicitation technique was used [44–46, 48, 51, 52, 56, 59, 61, 69, 70, 77, 79, 87, 94, 99, 104–106, 112, 120, 132, 136, 153].…”
Requirements elicitation is a critical activity that forms part of the requirements engineering process because it has to discover what the software must do through a solid understanding of the wishes and needs of the various stakeholders and to transform them into software requirements. However, in spite of its relevance, there are only a few systematic literature reviews that provide scientific evidence about the effectiveness of the techniques used to elicit software requirements. This study presents a systematic review of relevant literature on requirements elicitation techniques, from 1993 to 2015, by addressing two research questions: Which mature techniques are currently used for eliciting software requirements? and Which mature techniques improve the elicitation effectiveness? Prior literature assumes that such 'maturity' leads to a better-quality understanding of stakeholders' desires and needs, and thus an increased likelihood that a resulting software will satisfy those requirements. This research paper found 140 studies to answer these questions. The findings describe which elicitation techniques are effective and in which situations they work best, taking into account the product which must be developed, the stakeholders' characteristics, the type of information obtained, among other factors.
In recent years, significant changes have been presented in the climatological trends due to climatic change, originating negative impacts on the agricultural production, diminishing mainly the harvest efficiency. The following research proposes the optimization of the agricultural risk identification method for the prediction of the variables: temperature and precipitation; the risk identification method was developed through the Digital Image Processing technique (DIP) and Deep Learning (DL); Subsequently, with the processed images, Convolutional Neural Networks (CNN's) were developed for the detection of areas where there is a potential risk in the sugar cane crop harvest in the southeast of Veracruz in Mexico. The efficiency of CNN detects temperatures over 38ºC and the levels of precipitation under 70 millimeters. The efficiency of network detection is 0.9716 and 0.9948 for predicting the temperatures and precipitation variables, which represent a solid basis for detecting zones that depict a risk for the sugarcane harvest.
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