| 1. |
- Iqbal, Tahira, et al.
(författare)
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Generating requirements out of thin air : Towards automated feature identification for new apps
- 2019
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Ingår i: Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728151656 ; , s. 193-199
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Konferensbidrag (refereegranskat)abstract
- App store mining has proven to be a promising technique for requirements elicitation as companies can gain valuable knowledge to maintain and evolve existing apps. However, despite first advancements in using mining techniques for requirements elicitation, little is yet known how to distill requirements for new apps based on existing (similar) solutions and how exactly practitioners would benefit from such a technique. In the proposed work, we focus on exploring information (e.g. app store data) provided by the crowd about existing solutions to identify key features of applications in a particular domain. We argue that these discovered features and other related influential aspects (e.g. ratings) can help practitioners(e.g. software developer) to identify potential key features for new applications. To support this argument, we first conducted an interview study with practitioners to understand the extent to which such an approach would find champions in practice. In this paper, we present the first results of our ongoing research in the context of a larger road-map. Our interview study confirms that practitioners see the need for our envisioned approach. Furthermore, we present an early conceptual solution to discuss the feasibility of our approach. However, this manuscript is also intended to foster discussions on the extent to which machine learning can and should be applied to elicit automated requirements on crowd generated data on different forums and to identify further collaborations in this endeavor. © 2019 IEEE.
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| 2. |
- Maksimov, Yuliyan V., 1982-, et al.
(författare)
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Framework for Analysis of Multi-Party Collaboration
- 2019
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Ingår i: Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019. - : IEEE Computer Society Digital Library. - 9781728151656 ; , s. 44-53
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Konferensbidrag (refereegranskat)abstract
- In recent years, platforms have become important for allowing ecosystems to emerge that allow users to collaborate and create unprecedented forms of innovation. For the platform provider, the ecosystem represents a massive business opportunity if the platform succeeds to make the collaborations among the users value-creating and to facilitate trust. While the requirements flow for evolving existing ecosystems is understood, it is unclear how to analyse an ecosystem that is to be. In this paper, we draw on recent work on collaboration modelling in requirements engineering and propose an integrated framework for the analysis of multi-party collaboration that is to be supported by a platform. Drawing on a real-world case, we describe how the framework is applied and the results that have been obtained with it. The results indicate that the framework was useful to understand the ecosystem context for a planned platform in the domain of artificial intelligence, allowed identification of platform requirements and offered a basis to plan validation.
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| 3. |
- Ocieszak, Marcin, et al.
(författare)
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Using financial valuation techniques to minimize waste in requirements scoping
- 2019
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Ingår i: Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728151656 ; , s. 3-6
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Konferensbidrag (refereegranskat)abstract
- This paper presents our initial experiences with employing option theory and NPV techniques for optimizing waste reduction in requirements scoping. Inspired by financial market theories, we analyze a large requirements scoping decision making history from the mobile handset domain. We outline how we can optimize waste reduction in requirements scoping by modeling the neutral, positive and negative scenarios, giving each of the scenarios appropriate budget and development team commitment. © 2019 IEEE.
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| 4. |
- Qian, Wu, et al.
(författare)
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Segmentation-based Deep Learning Fundus Image Analysis
- 2019
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Ingår i: 2019 IEEE 27TH INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE WORKSHOPS (REW 2019). - : IEEE. - 9781728151656 ; , s. 44-53
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Konferensbidrag (refereegranskat)abstract
- Diabetic retinopathy is the most common cause of new cases of blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus images form an important basis for judging these retinal diseases. To the best of our knowledge, no prior studies have scrutinized the predictive power of the different compositions of retinal images using deep learning. This paper is to investigate whether there exists specific region that could assist in better prediction of the retinopathy disease, meaning to find the best region in fundus images that can boost the prediction power of models for retinopathy classification. To this end, with image segmentation techniques, the fundus image is divided into three different segments, namely, the optic disc, the blood vessels, and the other regions (regions other than blood vessels and optic disk). These regions are then contrasted against the performance of original fundus images. The convolutional neural network as well as transfer deep learning with the state-of-the-art pre-trained models (i.e., AlexNet, GoogleNet, Resnet50, VGG19) are deployed. We report the average of ten runs for each model. Different machine learning evaluation metrics are used. The other regions' segment reveals more predictive power than the original fundus image especially when using AlexNet/Resnet50.
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