1) The document discusses a machine learning approach to classify cloud application services into reusable categories to aid in development. 2) A BERT model is fine-tuned on a benchmark dataset of top 50 classified service classes including data and algorithm methods to create a service classification model. 3) The model is empirically validated on open source and closed source services, achieving an average accuracy of 0.78. Incorrect classifications are examined to understand factors like lacking service descriptions.

1. Service Classification through Machine Learning:
Aiding in the Efficient Identification of Reusable
Assets in Cloud Application Development
Zakieh
Alizadehsani
Daniel
Feitosa
Theodoros
Maikantis
Apostolos
Ampatzoglou
David
Berrocal
Alexander
Chatzigeorgiou
Alfonso
González Briones
Juan M.
Corchado
Marcio
Mateus
Johannes
Groenewold

2. support discovery, creation,
composition, testing, and deployment
of full-stack data-centered
services and applications in the cloud
@SmartCLIDE
eclipse-researchlabs/smartclide-docs
https://smartclide.eu
Lower
entry barriers
Improve
readability
Increase
Quality & Security
Increase
reusability
Improve
productivity

3. Where will the reuse take place?
3
front end

4. ReACt | Reusable Artifact and Components Adoption
4
domain is key

5. Classification approach
select top 50 classes
include data-level and
algorithm-level methods

6. data
augmentation

7. Classification approach
select top 50 classes
include data-level and
algorithm-level methods
fine-tune BERT using the
benchmark dataset

8. classifier
encoder
encoder
encoder
⁞
[cls] this service provides …
1 2 3 4 …
…
BERT
Service classification model
services probability
top class
https://github.com/eclipse-researchlabs
/smartclide-smart-assistant
/smartclide-dle-models
/serviceclassification

9. Empirical validation
RQ1 - What is the approach accuracy?
… given we are using a contextual model …
RQ2 - Can we explain incorrect classifications?
9

10. Trained on open source → Validated on closed source
10
4 engineers
7 engineers
annotate 67 services
if multiple classes are suitable,
pick the one with highest confidence

11. RQ1 | Accuracy
11
↓confidence ∝ accuracy↓
fuzziness of human labeling
may be "learned" by the model.
Average: 0.78

12. RQ2 | Explanation
12
What affected confidence?
Data profile isn't quite the same
- Services may fit 2+ domains
- Lacking descriptions

13. Implications
Practitioners
- improve descriptions
- merge categories (deployment-specific?)
Researchers
- context matters and makes a difference
- how to get more (or less) out of categorization?
- self-thought categories (via a BERT-like model)?
13

14. Still…
Reuse isn't just finding a suitable service
- integration effort
- activities and practices in place to enable and promote reuse
Partners are optimistic
- already have elementary service reuse activities
- integration is facilitated through SmartCLIDE
14