Analysis of Economic Impact of Online Reviews: An Approach for Market-Driven Requirements Evolution

Jiang, Wei; Ruan, Haibin; Zhang, Li

doi:10.1007/978-3-662-43610-3_4

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…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. com [60].…”

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

confidence: 99%

“…• K-means clustering has been used to extract software features from online reviews [41,58,63,75,78,90], and to cluster informative app reviews [53] Jiang et al [41] used S-GN to cluster online reviews, while Cleland-Huang et al in [78] used the modified Spherical K-Means to extract and cluster feature requests from threads in open discussion forums. • Kang et al [91] used the bagging clustering algorithm, which combines the EM, K-means, and MTree clustering algorithms for grouping similar data to select transfer instances.…”

Section: Traditional Clusteringmentioning

confidence: 99%

“…Two studies performed classification using ML, which was followed by unsupervised clustering analysis [53,58]. Jiang et al [58] used Support Vector Machine, or a supervised machine-learning algorithm, for pruning incorrect software features that were extracted from online reviews. K-means clustering, an unsupervised clustering analysis, was then performed to categorize the extracted features into semantically similar system aspects.…”

Section: Rule-based Clusteringmentioning

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

confidence: 99%

Section: Traditional Clusteringmentioning

confidence: 99%

Section: Rule-based Clusteringmentioning

confidence: 99%

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Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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“…Guzman et al presented an exploratory study of Twitter messages to help requirements engineers better capture user needs information for software evolution [4]. Jiang et al used text mining technology to evaluate quantitatively the economic impact of user opinions that might be a potential requirement of software [5]. Guzman and Maalej proposed an automated approach to extract the user sentiments about the identified features from user reviews.…”

Section: Requirements Evolution Prediction Through Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis Based Requirement Evolution Prediction

Zhao

2019

Future Internet

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To facilitate product developers capturing the varying requirements from users to support their feature evolution process, requirements evolution prediction from massive review texts is in fact of great importance. The proposed framework combines a supervised deep learning neural network with an unsupervised hierarchical topic model to analyze user reviews automatically for product feature requirements evolution prediction. The approach is to discover hierarchical product feature requirements from the hierarchical topic model and to identify their sentiment by the Long Short-term Memory (LSTM) with word embedding, which not only models hierarchical product requirement features from general to specific, but also identifies sentiment orientation to better correspond to the different hierarchies of product features. The evaluation and experimental results show that the proposed approach is effective and feasible.

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“…The process did not consider the evolution of SPLs at all and did not address the requirements engineering phase. Jiang et.al [14] proposed an approach for requirements evolution from an economic perspective. The authors have examined many online reviews and combined the techniques of machine learning, opinion mining, and text clustering with a utility-oriented econometric model to find system aspects related to software marketing and sales for the revising requirements.…”

Section: Related Workmentioning

confidence: 99%

Your Opinions Let us Know: Mining Social Network Sites to Evolve Software Product Lines

Ali¹,

Hwang²,

Hong³

2019

KSII TIIS

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Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

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Analysis of Economic Impact of Online Reviews: An Approach for Market-Driven Requirements Evolution

Cited by 7 publications

References 14 publications

Data-Driven Requirements Elicitation: A Systematic Literature Review

Data-Driven Requirements Elicitation: A Systematic Literature Review

Sentiment Analysis Based Requirement Evolution Prediction

Your Opinions Let us Know: Mining Social Network Sites to Evolve Software Product Lines

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