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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
• 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
• 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
• Multi-modal feedback / monitoring
• Feedback analysis / Data analytics
• Automated reasoning at support of decision
making in software development
Motivations:
Candidate solutions from research
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
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
Main Achievements
SUPERSEDE solutions
• Development of software tools - available for download at
https://github.com/supersede-project
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
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
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
• 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
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
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
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
Analysing SUPERSEDE Alerts in JIRA
16
Prioritising Requirements with
DMGame as JIRA plug-in
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
Release planning with REPlan
as JIRA plug-in
18
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
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.
21
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
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
Thank you for your attention
Questions?

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Hamburg Requirements Engineering Symposium

  • 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/
  • 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.
  • 21. 21 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?