Automated generation of creative software requirements: a data-driven approach

Quoc, Anh; Bhowmik, Tanmay

doi:10.1145/3278177.3278180

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Finally, a very important direction relates to the proposals for enabling context-awareness to capture requirements that changes dynamically over time. [73] 2016 International Conference on Research Challenges in Information Science (RCIS) C33 Bakar et al [62] 2015 International Conference on Information Science and Security (ICISS) C34 Maalej and Nabil [39] 2015 IEEE International Requirements Engineering Conference (RE) C35 Panichella et al [45] 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME) C36 Takahashi et al [100] 2015 International Conference on Software Engineering & Knowledge Engineering (SEKE) C37 Sun and Peng [53] 2015 Asia Pacific Requirements Engineering Symposium (APRES) C38 Guzman and Maalej [40] 2014 IEEE International Requirements Engineering Conference (RE) C39 Jiang et al [41] 2014 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) C40 Zhang et al [60] 2014 International Conference on Enterprise Systems (ES) C41 Carreño and Winbladh [52] 2013 International Conference on Software Engineering (ICSE) W1 Stanik et al [65] 2019 International Requirements Engineering Conference Workshops (REW) W2 Do and Bhowmik [36] 2018 ACM SIGSOFT International Workshop on Automated Specification Inference W3 Abad et al [44] 2017 International Requirements Engineering Conference Workshops (REW) W4 Bakiu and Guzman [55] 2017 International Requirements Engineering Conference Workshops (REW) W5 Deocadez et al [43] 2017 International Requirements Engineering Conference Workshops (REW) W6 Jha and Mahmoud [50] 2017 REFSQ Workshops W7 Portugal et al [86] 2015 IEEE Workshop on Just-In-Time Requirements Engineering (JITRE) S1…”

Section: Discussionmentioning

confidence: 99%

“…Online reviews are the most frequently used type of dynamic data for eliciting requirements (53%), followed by micro-blogs (18%) and online discussions/forums (12%), software repositories Online reviews • Online reviews included app reviews, reviews compiled by experts, and online user reviews. Among the studies which used online reviews, a majority of the studies used app reviews as the sources of potential requirements (75%) [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Of them, 14 used app reviews from multiple distribution platforms such as Apple AppStore and Google Play to increase the level of generalizability, while eleven used those from a single distribution platform, and one did not specify the number of app distribution platforms • Of the studies which used online reviews, 17% (n = 6) extracted user reviews of software and video games [55], IoT products [56], compact cameras [57], internet security [58], Jira and Trello [59], and Jingdong.…”

Section: The Specific Types Of Dynamic Data Used For Automated Requirmentioning

confidence: 99%

“…The method first performed the bagging clustering algorithm to select instances, whose results are used for building a RNN model to predict missing features. Elicitation of requirements • Requirements have been elicited at an abstract level in the form of textual descriptions [36,44,78,94], goal models [72], and aggregated scenarios [53]. • In a few studies, requirements have been elicited at a lower level than the above studies.…”

Section: Expression Of Final Outcomes Produced By the Automated Part mentioning

confidence: 99%

“…Summary of the automated requirements elicitation approaches for human-sourced data that is grouped by classification approach and clustering approach Classification with supervised learning has been performed in numerous studies[36,38,39,42,45,47,48, 50, 51, 53, 55, 56, 58-60, 64-71, 77, 79-82, 84, 85, 87, 91-93, 103, 104] to classify textual data into pre-defined labels that are relevant for performing requirements elicitation, including identification of relevant requirements sources and stakeholders as well as extraction of candidate features.…”

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confidence: 99%

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Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

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