App Review Analysis Via Active Learning: Reducing Supervision Effort without Compromising Classification Accuracy

Dhinakaran, Venkatesh T.; Pulle, Raseshwari; Ajmeri, Nirav; Murukannaiah, Pradeep K.

doi:10.1109/re.2018.00026

Cited by 49 publications

(34 citation statements)

References 32 publications

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“…By crowd [48,54,72], by textual data analysis [13,20,25,33,41,42,49,51,52,62,64,80,86,88,89], by prototyping [22], sentiment analysis [21,79], image and unstructured data analysis [21,73] 22…”

Section: Analysis and Validationmentioning

confidence: 99%

“…For requirements analysis and validation, Mead et al [51] proposed that machine learning algorithms can be used to analyse individual Personae Non Gratae created by crowd users. To accommodate AI and exploit human intelligence in requirements analysis, Dhinakaran et al [88] proposed an active learning approach to classify requirements into features, bugs, rating and user experience. Recently, Williams et al [87] proposed that automated social mining and domain modeling techniques can be used to analyse mobile app success and failure stories to identify end-users' concerns of domain.…”

Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

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Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Requirements Engineering: Foundation for Software Quality

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Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering.

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Section: Analysis and Validationmentioning

confidence: 99%

Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Requirements Engineering: Foundation for Software Quality

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“…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%

“…Several different algorithms are often applied in the same study to compare the performance of classification. • Active learning based on uncertain sampling, which selects data points which a model is most uncertain about for manual labeling, was applied to classify app reviews into a feature request, bug report, rating, and user experience [35]. • A semi-supervised learning technique was used to classify app reviews into either functional or non-functional requirements [43].…”

Section: Approaches Used For Human-sourced Datamentioning

confidence: 99%

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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“…Online user forum and social media provided another channel to communicate with the wider user community and obtain diverse input and increase inclusivity. 11,12 Recent studies have shown that end-user reviews, 13 tweets 14 and posts 15 contain relevant and insightful information on user experience, 16 feature requests 17,18 and user rationale that can facilitate future design decisions. 4,19,20 These intuitive RE-related information can be found in mobile app stores, issue tracking systems, user forums and social media like Twitter.…”

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confidence: 99%

Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework

Khan

Liu

Wen

et al. 2020

J Software Evolu Process

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Due to the pervasive use of online forums and social media, users' feedback are more accessible today and can be used within a requirements engineering context. However, such information is often fragmented, with multiple perspectives from multiple parties involved during ongoing interactions. In this paper, the authors propose a Crowdbased Requirements Engineering approach by Argumentation (CrowdRE-Arg). The framework is based on the analysis of the textual conversations found in user forums, identification of features, issues and the arguments that are in favour or opposing a given requirements statement. The analysis is to generate an argumentation model of the involved user statements, retrieve the conflicting-viewpoints, reason about the winning-arguments and present that to systems analysts to make informedrequirements decisions. For this purpose, the authors adopted a bipolar argumentation framework and a coalition-based meta-argumentation framework as well as user voting techniques. The CrowdRE-Arg approach and its algorithms are illustrated through two sample conversations threads taken from the Reddit forum. Additionally, the authors devised algorithms that can identify conflict-free features or issues based on their supporting and attacking arguments. The authors tested these machine learning algorithms on a set of 3,051 user comments, preprocessed using the content analysis technique. The results show that the proposed algorithms correctly and efficiently identify conflict-free features and issues along with their winning arguments. K E Y W O R D S argumentation, machine learning, natural language processing, new features, requirements, user forum 1 | INTRODUCTION Recently, with the increasing popularity and development of social media, the role of rationale management became more prominent for marketdriven software applications. Mainly, rationale management focuses on capturing and recording the justification why a decision was taken. 1 In software engineering literature, requirements and design rationale management have been a main research area. 2,3 Software developers of social applications make various decisions in finalizing the set of software features. 4 To justify their decisions, different alternatives are considered, and argumentation that helps in finalizing the decision constitutes the software rationale. 5 During software evolution, the rationale serves as a source for development decision making and enhancing the software system explainability. 6,7 Also, the rationale is meant to provide a sound reasoning of the chosen design of the system 8 and help in the understanding of

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App Review Analysis Via Active Learning: Reducing Supervision Effort without Compromising Classification Accuracy

Cited by 49 publications

References 32 publications

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Data-Driven Requirements Elicitation: A Systematic Literature Review

Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework

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