This document discusses a mapping study on the use of social metrics in software engineering prediction models. It aims to identify which social metrics have been used and whether they had a positive effect. The study found that social metrics were often classified under other dimensions and there was inconsistent terminology. It identified papers reporting on various social metrics and grouped them into categories and sub-categories. The results showed that most papers reported social metrics had a positive effect in prediction models, while some reported negative or neutral effects. The conclusions note more research is needed on social metrics in different contexts and using larger datasets.

1. SOCIAL METRICS INCLUDED IN PREDICTION
MODELS ON SOFTWARE ENGINEERING: A MAPPING
STUDY
Igor Wiese, Filipe Côgo, Reginaldo Ré, Igor Steinmacher
and Marco Aurélio Gerosa

2. 2
MOTIVATION SCENARIO
Subsystem A
Class
a
Class
c
Class
b
Subsystem B
Class
d
Class
e
Software
systems are
composed by
Artifacts that
dependes one
each other
HISTORICAL
SIDE

3. 3
PROBLEM STATEMENT
Even when social metrics were
considered, they were classified as
part of other dimensions, such as
process, history, or change.
Is not clear yet which social metrics
are used in prediction models and
what are the results of their use in
different contexts.

4. 4
OBJECTIVE
Classify
Identify

5. 5
RESEARCH QUESTION
Which social metrics were used in prediction
models?
Did the social metrics have positive effect
when they were considered as predictor?
RQ1
RQ2
We found that previous SLR did not discussed explicitly about
social metrics
inconsistent terminology for classifying social metrics and often do
not report their individual result
we identified papers describing evidences about the effects of social
metrics
we summarized the proposed classification, linking each group of
metrics to the applicability of prediction models
we mapped in which application each group of social metrics were
used so far

6. 6
SNOWBALLING PROCESS METHOD
Papers that explicitly mention only code metrics,
objected oriented metrics, or static metrics

7. 7
DATA EXTRACTION
ICSE
10 papers
FSE
6 papers
PROMISE
5 papers
48 Primary Papers, 103 distinct authors

8. 8
RQ1: WHICH SOCIAL METRICS WERE
USED IN PREDICTION MODELS?

9. 9
RQ1: WHICH SOCIAL METRICS WERE
USED IN PREDICTION MODELS?
Group (3)
Category (9)
Sub-category (51)

10. 10
RQ1: WHICH SOCIAL METRICS WERE
USED IN PREDICTION MODELS?

11. 11
RQ1: WHICH SOCIAL METRICS WERE
USED IN PREDICTION MODELS?

12. 12
RQ2: DID THE SOCIAL METRICS HAVE
POSITIVE EFFECT WHEN THEY WERE
CONSIDERED AS PREDICTOR?
6 papers reported
negative effects
21 papers reported
positive effects
2 papers reported
neutral effects

13. 13
RQ2: DID THE SOCIAL METRICS HAVE
POSITIVE EFFECT WHEN THEY WERE
CONSIDERED AS PREDICTOR?

14. CONCLUSIONS
• social metrics were classified as part of other dimension, such
as process, history, or change
• Considering the results published so far, it could be risky to
draw generalized conclusions about social metrics.
• New opportunities of research concerning social metrics
• different techniques and limited number of software
projects in different contexts.
• To consider large scale and longitudinal analysis
• To investigate the effectiveness of social metrics to build
prediction models
14

15. OUR RESEARCH
15
Artifacts a1
Artifacts a2
time
Change coupling
commit
A change dependency indicates that two
artifacts changed together (co-changed)
in the past, making them evolutionarily
connected

16. 16
PROBLEM STATEMENT
File a1
File a2
time
Change coupling
commit
+ SOCIAL
+ HISTORICAL
D´ambros - benchmark
Tracy hall - SLR, etc
D´ambros – OSS
Kirbas/Ayse Bener – Industrial
Gustavo Oliva,
Markus Geipel

17. http://lapessc.ime.usp.br/
Thank you
igor@utfpr.edu.br