Leveraging crowd knowledge to curate documentation for agile software industry using deep learning and expert ranking

Agility gives the ability to be highly responsive to changes and make improvements in the way the agile project is documented. The just-in-time and just barely good enough documentation may miss out on important executable specifications. Interestingly, the frequently asked ‘how-to-do’ questions on...

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Bibliographic Details
Published in:Multimedia systems Vol. 29; no. 3; pp. 1799 - 1813
Main Author: Kumar, Akshi
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
Springer Nature B.V
Subjects:
Collaboration
Computer Communication Networks
Computer Graphics
Computer Science
Crowdsourcing
Cryptology
Data Storage Representation
Documentation
Innovations
Knowledge
Matching
Multilayer perceptrons
Multilayers
Multimedia Information Systems
Operating Systems
Product development
Questions
Ranking
Role of Deep Learning Models & Analytics in Industrial Multimedia Environment
Semantics
Software
Software development
Software engineering
Software industry
Software quality
Special Issue Paper
User experience
Websites
Deep learning
Agile software industry
Question-answering websites
Crowd documentation
ISSN:0942-4962, 1432-1882
Online Access:Get full text
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Summary:Agility gives the ability to be highly responsive to changes and make improvements in the way the agile project is documented. The just-in-time and just barely good enough documentation may miss out on important executable specifications. Interestingly, the frequently asked ‘how-to-do’ questions on the popular question answering (Q&A) websites like stack overflow are strong indicators of gaps in documentation and respondent answers can complement conventional software documentation practices. Social interaction within these QA websites generates partially structured content commonly referred to as crowd knowledge that can offer a peer-reviewed re-documentation by integrating the answers of these ‘how-to-do’ concerns. But finding the best, value-added answer to the question which can contribute toward an enriched and curated documentation is computationally difficult. Moreover, query duplicates can cause seekers to spend more time finding these best answers. As a solution, the research proffers a novel question-answering crowd documentation model (QACDoc) which is based on a socially mediated documentation mechanics involving the dynamics of community-based web. Firstly, duplicate questions are detected using Siamese neural architecture where two identical hierarchical attention networks are used to generate vectors for similarity matching. Semantic matching is done using Manhattan distance function, and a multi-layer perceptron is trained to output the predictions. Next, all respondents of semantically matched questions are grouped to form an intent-based crowd and lastly, top k -experts are identified using representative social presence features for expert ranking. The crowd documentation is then filtered to only include answers of these identified experts.
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ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-020-00741-x