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Crowd-Informed Goal Models

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Crowd-Informed Goal Models. / Kanchev, G.M.; Murukannaiah, P.K.; Chopra, A.K.

2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE, 2018. p. 47-53.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Kanchev, GM, Murukannaiah, PK & Chopra, AK 2018, Crowd-Informed Goal Models. in 2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE, pp. 47-53. https://doi.org/10.1109/AIRE.2018.00013

APA

Kanchev, G. M., Murukannaiah, P. K., & Chopra, A. K. (2018). Crowd-Informed Goal Models. In 2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE) (pp. 47-53). IEEE. https://doi.org/10.1109/AIRE.2018.00013

Vancouver

Kanchev GM, Murukannaiah PK, Chopra AK. Crowd-Informed Goal Models. In 2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE. 2018. p. 47-53 https://doi.org/10.1109/AIRE.2018.00013

Author

Kanchev, G.M. ; Murukannaiah, P.K. ; Chopra, A.K. / Crowd-Informed Goal Models. 2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE, 2018. pp. 47-53

Bibtex

@inproceedings{c7b8e55952cb473bb4d99d20f3d2a73d,
title = "Crowd-Informed Goal Models",
abstract = "A topic of recent interest is how to apply crowdsourced information toward producing better software requirements. A research question that has received little attention so far is how to leverage crowdsourced information toward creating better-informed models of requirements. In this paper, we contribute a method following which information in online discussions may be leveraged toward constructing goal models. A salient feature of our method is that it applies high-level queries to draw out potentially relevant information from discussions. We also give a subjective logic-based method for deriving an ordering of the goals based on the amount of supporting and rebutting evidence in the discussions. Such an ordering can potentially be applied toward prioritizing goals for implementation. {\textcopyright} 2018 IEEE.",
keywords = "crowdsourcing, discussions, evidence, goals, methodology, modeling, Artificial intelligence, Models, Requirements engineering, High-level queries, Online discussions, Research questions, Software requirements, Crowdsourcing",
author = "G.M. Kanchev and P.K. Murukannaiah and A.K. Chopra",
note = "{\textcopyright}2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2018",
month = aug,
day = "21",
doi = "10.1109/AIRE.2018.00013",
language = "English",
pages = "47--53",
booktitle = "2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Crowd-Informed Goal Models

AU - Kanchev, G.M.

AU - Murukannaiah, P.K.

AU - Chopra, A.K.

N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2018/8/21

Y1 - 2018/8/21

N2 - A topic of recent interest is how to apply crowdsourced information toward producing better software requirements. A research question that has received little attention so far is how to leverage crowdsourced information toward creating better-informed models of requirements. In this paper, we contribute a method following which information in online discussions may be leveraged toward constructing goal models. A salient feature of our method is that it applies high-level queries to draw out potentially relevant information from discussions. We also give a subjective logic-based method for deriving an ordering of the goals based on the amount of supporting and rebutting evidence in the discussions. Such an ordering can potentially be applied toward prioritizing goals for implementation. © 2018 IEEE.

AB - A topic of recent interest is how to apply crowdsourced information toward producing better software requirements. A research question that has received little attention so far is how to leverage crowdsourced information toward creating better-informed models of requirements. In this paper, we contribute a method following which information in online discussions may be leveraged toward constructing goal models. A salient feature of our method is that it applies high-level queries to draw out potentially relevant information from discussions. We also give a subjective logic-based method for deriving an ordering of the goals based on the amount of supporting and rebutting evidence in the discussions. Such an ordering can potentially be applied toward prioritizing goals for implementation. © 2018 IEEE.

KW - crowdsourcing

KW - discussions

KW - evidence

KW - goals

KW - methodology

KW - modeling

KW - Artificial intelligence

KW - Models

KW - Requirements engineering

KW - High-level queries

KW - Online discussions

KW - Research questions

KW - Software requirements

KW - Crowdsourcing

U2 - 10.1109/AIRE.2018.00013

DO - 10.1109/AIRE.2018.00013

M3 - Conference contribution/Paper

SP - 47

EP - 53

BT - 2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)

PB - IEEE

ER -