@inproceedings{Samer:2019:TIR:3350546.3352514,
title = {Towards Issue Recommendation for Open Source Communities},
author = {Ralph Samer and Alexander Felfernig and Martin Stettinger},
url = {http://doi.acm.org/10.1145/3350546.3352514},
doi = {10.1145/3350546.3352514},
isbn = {978-1-4503-6934-3},
year = {2019},
date = {2019-01-01},
booktitle = {IEEE/WIC/ACM International Conference on Web Intelligence},
pages = {164--171},
publisher = {ACM},
address = {Thessaloniki, Greece},
series = {WI '19},
keywords = {Open-Source Software Development, Prioritization, Recommender Systems, requirements engineering},
pubstate = {published},
tppubtype = {inproceedings}
}
@conference{Felfernig2017,
title = {OpenReq: Recommender Systems in Requirements Engineering},
author = {Alexander Felfernig and Martin Stettinger and Andreas Falkner and M\"{u}sl\"{u}m Atas and Xavier Franch and Cristina Palomares},
editor = {Alexander Felfernig and Martin Stettinger and Andreas Falkner and M\"{u}sl\"{u}m Atas and Xavier Franch and Cristina Palomares},
url = {http://ase.ist.tugraz.at/ASE/wp-content/uploads/2014/01/openreq-4.pdf},
year = {2017},
date = {2017-12-01},
booktitle = {RS-BDA'17},
pages = {1-4},
address = {Graz, Austria },
keywords = {OpenReq, Recommender Systems, Recommender Systems in RE},
pubstate = {published},
tppubtype = {conference}
}
International Journal of Software Engineering and its Applications 10(129):140 · January 2016 with 38 Reads, 10 (1), pp. 129-140, 2016, ISSN: 1738-9984.
@article{garcia_paiva_2016b,
title = {REQAnalytics: A Recommender System for Requirements Maintenance},
author = {Jorge Esparteiro Garcia and Ana C R Paiva },
doi = {10.14257/ijseia.2016.10.1.13},
issn = {1738-9984},
year = {2016},
date = {2016-01-31},
journal = { International Journal of Software Engineering and its Applications 10(129):140 · January 2016 with 38 Reads},
volume = {10},
number = {1},
pages = {129-140},
abstract = {In the context of SaaS, where the change requests can be frequent, there is the need for a systematic requirements management process so as to maintain requirements updated and ease the management of changes required to improve the service to provide. Changes to perform need to be prioritized and their impact on the system should be assessed. The extraction and analysis of the use of the services provided through the web and their relationship to the requirements can help identify improvements and help keep the service useful for longer period of time. This paper presents REQAnalytics, a recommender system that collects information on the usage of a web service, relates that information back to the requirements, and generates reports with recommendations and change suggestions that can increase the quality of that service. The proposed approach aims to provide reports of the analysis made in a language closer to the business where, for example, it indicates new workflows and navigation paths, identifies the features that can be removed and presents the relationship between requirements and the proposed changes helping to maintain the software requirements specification updated and useful.},
keywords = {Recommender Systems},
pubstate = {published},
tppubtype = {article}
}
In the context of SaaS, where the change requests can be frequent, there is the need for a systematic requirements management process so as to maintain requirements updated and ease the management of changes required to improve the service to provide. Changes to perform need to be prioritized and their impact on the system should be assessed. The extraction and analysis of the use of the services provided through the web and their relationship to the requirements can help identify improvements and help keep the service useful for longer period of time. This paper presents REQAnalytics, a recommender system that collects information on the usage of a web service, relates that information back to the requirements, and generates reports with recommendations and change suggestions that can increase the quality of that service. The proposed approach aims to provide reports of the analysis made in a language closer to the business where, for example, it indicates new workflows and navigation paths, identifies the features that can be removed and presents the relationship between requirements and the proposed changes helping to maintain the software requirements specification updated and useful.
@article{Mobasher_Cleland_2011b,
title = {Recommender Systems in Requirements Engineering},
author = {Jane Cleland-Huang and Bamshad Mobasher},
url = {https://www.aaai.org/ojs/index.php/aimagazine/article/view/2366/2228},
doi = {10.1609/aimag.v32i3.2366 },
year = {2011},
date = {2011-09-01},
journal = {AI Magazine},
volume = {32},
number = {3},
pages = {81-89},
abstract = {Requirements engineering in large-scaled industrial, government, and international projects can be a highly complex process involving thousands, or even hundreds of thousands of potentially distributed stakeholders. The process can result in massive amounts of noisy and semistructured data that must be analyzed and distilled in order to extract useful requirements. As a result, many human intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain.},
keywords = {Recommender Systems},
pubstate = {published},
tppubtype = {article}
}
Requirements engineering in large-scaled industrial, government, and international projects can be a highly complex process involving thousands, or even hundreds of thousands of potentially distributed stakeholders. The process can result in massive amounts of noisy and semistructured data that must be analyzed and distilled in order to extract useful requirements. As a result, many human intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain.
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