Workflow · saas · workflow
Supervised ML classifier detects SAP Community feature requests with 0.819 accuracy
Enterprise software vendors cannot rely on app store reviews for requirements elicitation and have no direct access to end users, while traditional techniques such as user interviews are too labor-intensive to scale across the millions of posts in developer communities.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · SAP Community data collection
Researchers collected question data from the SAP Community to build a labeled dataset of 1,500 questions.
Tools used
LabelboxSBERT-ModelNaïve BayesFiverr
Outcome
The supervised binary ML classifier reached a high accuracy of 0.819, demonstrating that supervised machine learning models are an effective means for automatically identifying feature requests in developer community posts.
Results
Volume0.819
Grounding & classification
Source type: technical build writeup
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