Can a Conversation Paint a Picture? Mining Requirements In Software Forums

Tizard, James; Wang, Hechen; Yohannes, Lydia; Blincoe, Kelly

doi:10.1109/re.2019.00014

Cited by 31 publications

(21 citation statements)

References 26 publications

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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%

“…Of the 68 studies included in the analysis, 57 studies (85%) used dynamic data which was externally related to a given organization (i.e., data were collected outside of an organization's platforms) [36-77, 80-82, 86-94, 96, 101]. Nine studies (13%) used dynamic data that were collected from platforms belonging to the organization: issue tracking systems [84,85,102]; user feedback from the online discussion and open-source software mailing lists [79]; sensors equipped with an intelligent product which is also known as a product embedded information devices (PEID) [95]; software production forum [103]; user feedback tool [99]. On the other hand, only two studies (3%) used both internal and external dynamic data [78,100].…”

Section: Relation Of Dynamic Data To An Organization Of Interestmentioning

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: Microblogsmentioning

confidence: 99%

Section: Relation Of Dynamic Data To An Organization Of Interestmentioning

confidence: 99%

Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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“…The other POS tags that are considered important for the text classification task and which frequently occurs in the user comments are cardinal number (CD), adjectives (JJ), noun singular (NN), noun proper singular (NNP), adverb (RB), and infinitival to (TO). 19,47 Sentiment of the user comments: In literature, the sentiment of the user comments is considered an important feature for text classification. 1,16,19 For this, the full user's comment can be given as an input to the sentistrength library, 48 which will turn two integer values ranging from +1 to +5 indicating how positive user comments is, the other values range from -5 to -1 indicating how negative the user comments is.…”

Section: Feature Engineeringmentioning

confidence: 99%

“…The CrowdRE-Arg classifier's performance can be boosted up with this novel text classification feature. 47 The possible values of user post position can be (1) tile of the post, (2) initial reply to the post, (3) first reply, (4) second reply, (5) third reply, amd so on. In initial reply to the post, in user forum, many users reply against the main title of the post, against which other users reply up to n-numbers.…”

Section: Feature Engineeringmentioning

confidence: 99%

“…Part of speech tagging : to boost the efficacy of the machine learning classifiers, the CrowdRE‐Arg approach can experiment with the other parts of speech (POS) tags triggered by the NLTK as a text feature. The other POS tags that are considered important for the text classification task and which frequently occurs in the user comments are cardinal number (CD), adjectives (JJ), noun singular (NN), noun proper singular (NNP), adverb (RB), and infinitival to (TO) 19,47 …”

Section: Automated Classification Of User Statementsmentioning

confidence: 99%

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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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Social network sites and requirements engineering: A systematic literature review

Ali

Hong

Chung

2020

J Software Evolu Process

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A large volume of highly diverse and real‐time data provided by social network sites has revolutionized several data analytical sciences such as stock exchange fluctuation prediction, sentiment analysis, and political affiliations prediction. From a software engineering viewpoint, social network sites have produced an unprecedented opportunity for software development organizations to monitor the opinion of the large population of users of their systems. The extracted data from social network sites can be used to extract information about newly launched systems, and it enable software development organizations to obtain rapid technical and social feedback about their systems. This paper presents on a systematic literature review of the published research articles on how requirements engineering (RE) activities are conducted using social network sites, elaborated in studies published between 2011 and 2019. From the resulting studies, we extracted data to answer our four questions related to social network site (SNS)‐based RE approaches focusing on requirements elicitation, prioritization, and negotiation activities. Thirty‐one relevant studies were identified and analyzed to answer our research questions. Among 31 studies, 62% of the studies reportedly support requirements elicitation activity, and 14% of the studies reportedly support requirements elicitation, prioritization, and negotiation activities. Ten percent studies were about requirements elicitation and prioritization activities, 7% of the studies support both requirements elicitation and negotiation activities, and prioritization activity was supported by 7% of the studies. It was also observed that 58% of the selected studies adopted semi‐automatic approaches to conduct RE activities while 21% of the selected studies proposed automatic tools, and 21% of the selected studies adopted manual approaches to conduct RE activities. The outcomes of this systematic literature review indicate that social network sites can be a major source that can be used successfully to extract and identify user requirements. Moreover, this systematic literature review also presents implications of this study for practitioners and researchers including challenges, unresolved issues, and future research directions.

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Can a Conversation Paint a Picture? Mining Requirements In Software Forums

Cited by 31 publications

References 26 publications

Data-Driven Requirements Elicitation: A Systematic Literature Review

Data-Driven Requirements Elicitation: A Systematic Literature Review

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

Social network sites and requirements engineering: A systematic literature review

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