- Anna Perini is a project coordinator for the SUPERSEDE project, which aimed to support data-driven software evolution using end-user feedback and runtime data. - The project developed tools to collect and analyze user feedback and data, support decision making for software evolution, and enact the decisions through integration with JIRA. - A demonstration of the tools used feedback from the ATOS Smart Player media platform to identify requirements and prioritize them for future releases.

1. Anna Perini – Project coordinator
FBK, Center for Information and Communication Technology – ICT
Trento (Italy)
Software Engineering Unit
http://se.fbk.eu
Data-driven software evolution
The SUPERSEDE way
Hamburg Requirements Engineering Symposium
Wednesday, May 23rd, 2018

2. 2
• SUpporting evolution and adaptation of PERsonalized
Software by Exploiting contextual Data and End-user
feedback
• An European funded project (HORIZON 2020 framework, RIA)
• Call: H2020-ICT-2014-1 (Tools and methods for Software
Development)
• Started May 1, 2015 (3 Years) <- JUST ended!
What is SUPERSEDE?
• Consortium: 4
Academic/Research
and 4 Industrial
partners

3. 3
• Software at use …
Software applications and
services increasingly
exploit and generate
data
– sensors embedded in the
environment
– online data sources
– users’ feedback
Motivations:
Observed trends …
• Software development
- The “move fast” trend:
Agile approaches
- Quality & Efficiency:
• software product quality
matters more and more
• taking care of
changing/evolving
customers' needs

4. 4
• Multi-modal feedback / monitoring
• Feedback analysis / Data analytics
• Automated reasoning at support of decision
making in software development
Motivations:
Candidate solutions from research

5. 5
Objective & Approach
Enable a data-driven engineering process
• collect end-users’ feedback and runtime data in an efficient, scalable and adaptable way
• perform an integrated analysis of the collected data for evolution and adaptation decision-making
• support decision-making in the evolution and runtime adaptation of services and applications based on
user’s feedback and contextual data
• enact the decisions made

6. 6
ü The SUPERSEDE project
• Facts – Motivations – Vision – Objective – Approach
• Results at Y3
• Glimpse on main achievements
• Illustrating the Data-Driven Software Evolution
– the ATOS’s UC
• Conclusion
Outline

7. 7
Main Achievements
SUPERSEDE solutions
• Development of software tools - available for download at
https://github.com/supersede-project

8. 8
The ATOS Smart Player
•Webcasting Media platform for large sport events
•Allows people to watch sport videos on demand, in real time
•Give stats with: live results and sport info

9. 9
The ATOS Smart Player
REFERENCES
M. Stade, F. Fotrousi, N. Seyff, O. Albrecht (2017). Feedback Gathering from an Industrial Point of
View. 25th IEEE International Requirements Engineering Conference (RE’17) pp. 71-79.

10. 10
Big Data architecture
REFERENCES
S. Nadal, V. Herrero, O. Romero, A. Abelló, X. Franch, S. Vansummeren, & D. Valerio (2017). A Software
Reference Architecture for Semantic-Aware Big Data Systems. Information and Software Technology.

11. 11
• Results:
- Found association between types of speech acts (e.g. Informative, Responsive, Requestive,
etc.) and type of issues (e.g. Enhancement, Defect)
- Distribution of speech acts for the first ten comments for Defect and Enhancement, and
identified common patterns
Textual feedback classification
Speech-act & sentiment analysis
Textual online discussion • Techniques:
- NLP techniques for pre-processing
- speech acts and the sentiment as parameters
for training three machine learning algorithms
(Random Forest, J48 and SMO) classify
comments into Enhancement, Feature and
Defect
REFERENCES
Moralez-Ramirez, Kifetew, Perini, CAISE 17 Analysis of Online Discussions in Support of Requirements Discovery

12. 12
ECA rule
If the we receive more
than N (e.g. N=2)
feedbacks in the
window’s time frame
an alert is raised for
decision

13. 13
Rule 1 (R1):
“Two or more negative
feedbacks in 5 min”
Sliding Window R1“I don’t like Y”“I don’t like X”
Big-data platform & Analytics
Running example

14. 14
SUPERSEDE at Y3
Decision-Making and release planning as
JIRA plug-in
Alerts are sent to JIRA *
*https://www.atlassian.com/software/jira
demo video at:
https://www.supersede.eu/do
wnloads/other-publications/

15. 15
Analysing SUPERSEDE Alerts in JIRA

16. 16
Prioritising Requirements with
DMGame as JIRA plug-in

17. Requirements lifecycle in
SUPERSEDE
Existing requirements
• ASP-1 Deployment
configuration
• ASP-2 Connection issues
• ASP-3 Infra not 100 percent
redundant
• ASP-4 Fail over
• ASP-5 Insufficient security
level
• ASP-6 Insufficient
Monitoring
• ASP-7 Third party integration
• ASP-8 Advertising needs to
be monitored
• ASP-9 Convert xml to jsons
• ASP-10 Plan to port the App
from Flash to HTML5
• ASP-11 Design a new Menu
Requirements after the alerts
• ASP-1 Deployment
configuration
• ASP-2 Connection issues
• ASP-3 Infra not 100 percent
redundant
• ASP-4 Fail over
• ASP-5 Insufficient security
level
• ASP-6 Insufficient
Monitoring
• ASP-7 Third party integration
• ASP-8 Advertising needs to
be monitored
• ASP-9 Convert xml to jsons
• ASP-10 Plan to port the App
from Flash to HTML5
• ASP-11 Design a new Menu
• ASP-16 Add support for
Chrome
Prioritized requirements
• ASP-4 Fail over
• ASP-11 Design a new Menu
• ASP-10 Plan to port the App
from Flash to HTML5
• ASP-16 Add support for
Chrome
• ASP-2 Connection issues
• ASP-6 Insufficient
Monitoring
• ASP-1 Deployment
configuration
• ASP-9 Convert xml to jsons
• ASP-5 Insufficient security
level
• ASP-3 Infra not 100 percent
redundant
• ASP-7 Third party integration
• ASP-8 Advertising needs to
be monitored

18. Release planning with REPlan
as JIRA plug-in
18

19. 19
What’s behind
DMGame
• Automated reasoning:
• Analytic Hierarchy Process (AHP) method that was selected because its
pairwise comparison mechanism allows us to perform a fine-grained analysis of
the motivations that lead to a resulting ranking, thus exploiting at best the
different skills and expertise of the decision makers
• Genetic Algorithms (GA) because it allows to overcome some of the limitations
of AHP, at the cost of a reduced granularity of the ranking
• Gamification
• To foster user engagement, game elements are included, such as:
- Progress, that is user completion rate is reported to each user
- Time Pressure, that is the process has a fixed duration, and actions done after the
process expiration are discarder;
- Pointsification, that is a point attribution mechanism has been designed, with the
purpose of providing an incentive to (i) perform the voting task quickly, and (ii) perform
an accurate
REFERENCES
F. Kifetew, D. Munante, A. Perini, A. Susi, A. Siena, P. Busetta D. Valerio, Gamifying Collaborative Prioritization: Does Pointsification Work?, RE’17
I. Morales-Ramirez, D. Munante, F. Kifetew, A. Perini, A. Susi and A. Siena. Exploiting User Feedback in Tool-supported Multi-criteria Requirements
Prioritization. RE’17.

20. 20
What’s behind
REPlan
• RePlan Optimizer uses optimization algorithms
from the JMetal library
REFERENCES
D. Ameller, C. Farré, X. Franch, D. Valerio, A. Cassarino, and V. Elvassore, “Replan: a Release Planning Tool,” in 24th IEEE International Conference
on Software Analysis, Evolution, and Reengineering (SANER), 2017.
D. Ameller, C. Farré, X. Franch, D. Valerio, and A. Cassarino, “Towards Continuous Software Release Planning,” in 24th IEEE International
Conference on Software Analysis, Evolution, and Reengineering (SANER), 2017.

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How to use the SUPERSEDE tool-suite?
A tool supported Methodology
• Situational Method Engineering to describe SUPERSEDE and guide
its tailoring to a particular context
• A catalogue of method chunks
• A process for their selection and assembly
• https://www.supersede.eu/method-explorer/#!/overview
REFERENCES
X. Franch, J.Ralyté,A. Perini, A. Abelló, D. Ameller, J. Gorroñogoitia, S. Nadal, M. Oriol, N.Seyff, A. Siena, A. Susi, A
Situational Approach for the Definition and Tailoring of a Data-Driven Software Evolution Method, CAISE 2018.
.

22. 22
Conclusion
6
Objective & Approach
Support a data-driven engineering process
• collect end-users’ feedback and runtime data in an efficient, scalable and adaptable way
• perform an integrated analysis of the collected data for evolution and adaptation decision-
making
• support decision-making in the evolution and runtime adaptation of services and applications
based on user’s feedback and contextual data
• enact the decisions made
12
The ATOS Smart Player
• A tool chain for supporting data-driven software evolution
• available for download at https://github.com/supersede-project
• SUPERSEDE’s solutions integrated into the JIRA© issue tracking system
• demo software evolution SUPERSEDE-JIRA at:
https://www.supersede.eu/downloads/other-publications/
• A tool-supported methodology:
• https://www.supersede.eu/method-explorer/#!/overview
• Scientific publications: https://www.supersede.eu/scientific-publications/
• Please contact us if you are interested to use the SUPERSEDE tools

23. Thank you for your attention
Questions?