Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE

Charrada, Eya Ben

doi:10.1109/rew.2016.022

Cited by 10 publications

(7 citation statements)

References 50 publications

(107 reference statements)

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“…Rationale management remains a key focus for researchers in the field of software and requirements engineering from a decade [28, 29]. Lee [8] emphasises on the importance of rationale in software engineering and considers design rationale as an important tool that not only captures reasons behind the design decisions but it also captures justification behind it, considers other alternatives and argumentation which leads the decision to a conclusion.…”

Section: Related Workmentioning

confidence: 99%

Requirements knowledge acquisition from online user forums

2020

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Online discussion forums can be used for reflecting on the overall user experience of a system. If a user forum is well-structured, it can be a valuable source of requirements-related information, which can potentially be accommodated in the requirements engineering process to enhance the current and future software. However, presently, there are limited approaches for extracting such requirements-related information from the relevant community forums. To fill this gap, this study proposes an automated approach, which automatically identifies requirements information using natural language processing and machine learning. For this purpose, the authors analysed 3319 user comments collected from the seven discussion topics in the Reddit forum. Then, using a content analysis approach, they studied how frequently end-users submit such information across each discussion topic. Also, they developed an automated approach that identifies key stakeholders, who frequently contribute his rationales in the forum discussion. Further, they employed different machine learning algorithms to classify user comments into rationale elements of different types. The authors' results show that online forums, such as Reddit, can be a rich source of requirements elicitation. Also, machine learning is a promising tool to detect user's rationale and identify different kinds of requirements modelling elements.

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Section: Related Workmentioning

confidence: 99%

Requirements knowledge acquisition from online user forums

2020

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“…It makes the decision process more user‐driven and transparent, for example, when explaining how arguments were made and how tacit knowledge were unpacked through peer interactions. Also, end‐user comments typically contain rationale and follow argumentative structure and this adds additional knowledge in comparison to analyst‐led structured approaches to requirements elicitation 28 . Finally, a requirements analyst can exploit such crowd‐generate content to identify not only opposing views, conflicts and variability but also conflict‐free new features and alternatives so that usability and acceptance are likely to increase.…”

Section: Introductionmentioning

confidence: 99%

“…First, it makes the decision process transparent by revealing the crowd‐users tacit knowledge. Second, it is reported in the literature that the end‐user comments which contain rationale and argumentative structure are considered more useful, 28 hence improves the decision‐making process. Third, it helps requirements analyst in identifying conflict‐free new features, alternatives suggested or issues encountered based on their supporting and attacking arguments.…”

Section: Introductionmentioning

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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“…Helpfulness prediction aims to identify and recommend high-quality reviews to customers. Prior literature [64,33,7] has explored various features and models. One critical drawback of most existing work is the assumption that customers are unbiased and process reviews independently.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Review Neighbors for Contextualized Helpfulness Prediction

Du,

Rong,

Wang

et al. 2020

Preprint

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Helpfulness prediction techniques have been widely used to identify and recommend high-quality online reviews to customers. Currently, the vast majority of studies assume that a review's helpfulness is self-contained. In practice, however, customers hardly process reviews independently given the sequential nature. The perceived helpfulness of a review is likely to be affected by its sequential neighbors (i.e., context), which has been largely ignored. This paper proposes a new methodology to capture the missing interaction between reviews and their neighbors. The first end-to-end neural architecture is developed for neighbor-aware helpfulness prediction (NAP). For each review, NAP allows for three types of neighbor selection: its preceding, following, and surrounding neighbors. Four weighting schemes are designed to learn context clues from the selected neighbors. A review is then contextualized into the learned clues for neighbor-aware helpfulness prediction. NAP is evaluated on six domains of real-world online reviews against a series of state-of-the-art baselines. Extensive experiments confirm the effectiveness of NAP and the influence of sequential neighbors on a current reviews. Further hyperparameter analysis reveals three main findings. (1) On average, eight neighbors treated with uneven importance are engaged for context construction. (2) The benefit of neighboraware prediction mainly results from closer neighbors. (3) Equally considering up to five closest neighbors of a review can usually produce a weaker but tolerable prediction result.

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Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE

Cited by 10 publications

References 50 publications

Requirements knowledge acquisition from online user forums

Requirements knowledge acquisition from online user forums

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

Exploiting Review Neighbors for Contextualized Helpfulness Prediction

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