Requirements knowledge acquisition from online user forums

Khan, Javed Ali; Liu, Lin; Wen, Lijie

doi:10.1049/iet-sen.2019.0262

Cited by 26 publications

(8 citation statements)

References 41 publications

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“…In the future, we aim to conduct exploratory research studies on the trusted facet ensured by the software vendors and developers through the end‐user feedback collected from these social media platforms. Another future direction is to identify critical stakeholders 41 in the Google Play Store and prioritize or group 42,43 them based on their severe deceptive review contributions to provide remedies in either blocking, limiting, or restricting them to enhance end‐user trust in the software applications 20 . Another possible future direction is to employ the extended proposed approach in the Internet of Medical Things (IoMTs) to identify the malware that seriously threatens medical software assets, such as abusing critical information, sensitive patient data, and so forth 44–48 .…”

Section: Discussionmentioning

confidence: 99%

“…Research studies have revealed that feedback analytics is pivotal in software and requirements engineering to make essential decisions. 2,20 Recently, Dhinakaran et al 21 used active and machine-learning approaches to identify new features from social media platforms. Maalej and Nabil 22 and Khan et al 9,23,24 utilized ML and DL classifiers to capture and identify bugs or issues in end-user feedback.…”

Section: Mining End-user Feedback For Requirements Engineeringmentioning

confidence: 99%

“…Research studies have revealed that feedback analytics is pivotal in software and requirements engineering to make essential decisions 2,20 . Recently, Dhinakaran et al 21 used active and machine‐learning approaches to identify new features from social media platforms.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Khan,

Ullah,

Khan

et al. 2023

Concurrency and Computation

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SummaryEnd‐user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end‐user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high‐star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end‐users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end‐user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end‐users and professionals use to report fake reviews by critically analyzing the end‐user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd‐users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end‐users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end‐user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f‐measures. The accuracy of manually identifying fake reviews by the crowd‐users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.

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Section: Discussionmentioning

confidence: 99%

Section: Mining End-user Feedback For Requirements Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Khan,

Ullah,

Khan

et al. 2023

Concurrency and Computation

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“…Massive usergenerated content has extremely high mining and utilization value. Compared with service log data, user comment data are easier to obtain on web pages where the data are disclosed (Khan et al, 2020). User comment data usually include numbers, review text, and images (Hossin et al, 2019).…”

Section: Data Collectionmentioning

confidence: 99%

A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory

et al. 2022

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Along with the rapid application of new information technologies, the data-driven era is coming, and online consumption platforms are booming. However, massive user data have not been fully developed for design value, and the application of data-driven methods of requirement engineering needs to be further expanded. This study proposes a data-driven expectation prediction framework based on social exchange theory, which analyzes user expectations in the consumption process, and predicts improvement plans to assist designers make better design improvement. According to the classification and concept definition of social exchange resources, consumption exchange elements were divided into seven categories: money, commodity, services, information, value, emotion, and status, and based on these categories, two data-driven methods, namely, word frequency statistics and scale surveys, were combined to analyze user-generated data. Then, a mathematical expectation formula was used to expand user expectation prediction. Moreover, by calculating mathematical expectation, explicit and implicit expectations are distinguished to derive a reliable design improvement plan. To validate its feasibility and advantages, an illustrative example of CoCo Fresh Tea & Juice service system improvement design is further adopted. As an exploratory study, it is hoped that this study provides useful insights into the data mining process of consumption comment.

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“…• Ali et al [66] and Han et al [75] used user comments on Facebook and Weibo, respectively. Seven out of 11 studies that used microdata performed sentiment analysis of tweets [64-66, 68, 69, 72, 73] Online discussion/forum Eight studies (12%) elicited requirements from different online discussion forums: online forum posts from the feature tracker of the Password Safe project on SourceForge [76], questions and answers on Stack Overflow [77], and feature requests from open-source forums of SugarCRM, SecondLife, and an Amazonlike portal specifically developed for students [78], OpenOffice online discussion forum [79], the Reddit forum [80][81][82], and the VLC media player and Firefox web browser forums [83]…”

Section: Microblogsmentioning

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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Requirements knowledge acquisition from online user forums

Cited by 26 publications

References 41 publications

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory

Data-Driven Requirements Elicitation: A Systematic Literature Review

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