Investigating mobile applications' requirements evolution through sentiment analysis of users' reviews

Rizk, Nancy M.; Ebada, Amr; Nasr, Eman S.

doi:10.1109/icenco.2015.7416336

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…Users" comments and reviews can contain useful information for developers; they include good, bad, or recommended features [45]. Thus, the analysis of these reviews is important for the requirements engineering activities [46]. The proposed framework suggests the use of feedback analysis methods to gain the benefits from eLS users" comments and reviews in requirements elicitation activity while developing eLS.…”

Section: ) Elearning Systemmentioning

confidence: 99%

CREeLS: Crowdsourcing based Requirements Elicitation for eLearning Systems

Rizk¹,

Mervat²,

Ahmed³

et al. 2019

IJACSA

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Crowdsourcing is the process of having a task performed by the crowd. Because of the Web evolution, recently crowdsourcing is being used in the field of Requirements Engineering to help in simplifying its activities. Among the information systems that were highly affected by the Web evolution are the eLearning Systems (eLS). eLS has special characteristics, such as the large number and diversity of users who could be geographically dispersed. To the best of our knowledge, there is little evidence that a crowdsourcing based requirements elicitation approach especially tailored for eLS that addresses their special characteristics exists. In this paper we attempt to fill in this gap. We present Crowdsourcing based Requirements Elicitation for eLS (CREeLS), which is made up of a framework of the necessary elements of crowdsourcing suggesting specific tools for each element, and a phased approach to implement the framework. We evaluated our approach through analyzing real-life users' reviews and extracted keywords that represent users' requirements by using topic modeling techniques. The reached results were then evaluated by manual text reviewing and the extracted features were found to be coherent. CREeLS has 0.66 precision and 0.79 recall. Hence we contend that CREeLS can help requirements engineers of eLS to analyze users' opinions and identify the most common users' requirements for better software evolution.

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Section: ) Elearning Systemmentioning

confidence: 99%

CREeLS: Crowdsourcing based Requirements Elicitation for eLearning Systems

Rizk¹,

Mervat²,

Ahmed³

et al. 2019

IJACSA

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show abstract

“…In the areas of software engineering (SE) and information systems research, there are 11 literature reviews, that form the related work for our study. For more information on those reviews, we refer interested readers to Table 9 in Appendix .…”

Section: Related Workmentioning

confidence: 99%

“…We found only two SLRs that explicitly investigated empirical evidence on using crowdsourced user feedback for the purpose of requirements evolution. Both examined techniques that are applicable on the user review repositories for apps: Rizk et al examined automated tools for sentiment analysis by means of natural language processing, while Genc‐Nayebi and Abran focused on opinion mining techniques for the purpose of requirements evolution. Both studies focus on tools and technical solutions without discussion on (a) the nature of the crowdsourced user feedback, eg, implicit/explicit and (b) the specific ways in which feedback is used in RE activities.…”

Section: Related Workmentioning

confidence: 99%

A systematic mapping study on crowdsourced requirements engineering using user feedback

Wang

Daneva

Sinderen

et al. 2019

J Software Evolu Process

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Crowdsourcing is an appealing concept for achieving good enough requirements and just-in-time requirements engineering (RE). A promising form of crowdsourcing in RE is the use of feedback on software systems, generated through a large network of anonymous users of these systems over a period of time. Prior research indicated implicit and explicit user feedback as key to RE-practitioners to discover new and changed requirements and decide on software features to add, enhance, or abandon.However, a structured account on the types and characteristics of user feedback useful for RE purposes is still lacking. This research fills the gap by providing a mapping study of literature on crowdsourced user feedback employed for RE purposes. On the basis of the analysis of 44 selected papers, we found nine pieces of metadata that characterized crowdsourced user feedback and that were employed in seven specific RE activities. We also found that the published research has a strong focus on crowdgenerated comments (explicit feedback) to be used for RE purposes, rather than employing application logs or usage-generated data (implicit feedback). Our findings suggest a need to broaden the scope of research effort in order to leverage the benefits of both explicit and implicit feedback in RE. KEYWORDScrowdsourced feedback, evidence-based software engineering, large-scale user involvement, requirements engineering, systematic mapping study, user feedback 1 | INTRODUCTION Crowd-based requirements engineering (RE) is the practice of large-scale user involvement in RE activities. Users are unknown volunteers and massive in number. Their involvement can take a variety of forms. For example, users generate information that becomes freely available for requirements specialists to use for requirements elicitation purposes, or participate in distributed problem-solving where they find workarounds in an application, which in turn may shape the requirements for a subsequent application release. To IT-consulting companies and software development organizations, this opportunity for large-scale user involvement means a way to get good-enough requirements and to achieve just-intime RE, which implies a significant potential to reduce the cost of RE processes. One promising form of crowdsourcing in RE-that is exploited by businesses and that attracts much research attention-is the use of feedback volunteered by large networks of anonymous users of software systems over a period of time in an RE activity, such as elicitation, validation, or prioritization.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…A number of studies have focused on requirement extraction for mobile applications [1]. Rizk et al investigated users' reviews' Sentiment Analysis (SA) in order to extract user requirements to build new applications or enhance existing ones [2]. The authors analyzed users' feedback to extract the features and sentiment score of a mobile app in order to find useful information for app developers [3].…”

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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Investigating mobile applications' requirements evolution through sentiment analysis of users' reviews

Cited by 10 publications

References 3 publications

CREeLS: Crowdsourcing based Requirements Elicitation for eLearning Systems

CREeLS: Crowdsourcing based Requirements Elicitation for eLearning Systems

A systematic mapping study on crowdsourced requirements engineering using user feedback

Sentiment Analysis Based Requirement Evolution Prediction

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