Vision talk on artificial intelligence techniques in requirements engineering gave at the RE@40 seminar on April 25, 2017 in Kappel am Albis, Switzerland.

1. The Automated Requirements Engineer:
Utopia or Dystopia?
Fabiano Dalpiaz
Requirements Engineering Lab
Department of Information and Computing Sciences
Utrecht University
RE@40 Seminar – April 25, 2017

2. Disclaimer
This talk is provocative and intended to
trigger discussion!
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3. 1.The Automated Requirements
Engineer: Vision
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4. 1. The Automated Req. Engineer: Vision
 Most of today’s RE tasks extensively rely on humans
 Is that necessary?
 Dan Berry (REFSQ 2012) distinguishes between
 Clerical RE activities: simple, repetitive – tools may do this job
 Thinking-required activities that require human analysts
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5. 1. The Automated Req. Engineer: Vision
 Most of today’s RE tasks extensively rely on humans
 Is that necessary?
 Dan Berry (REFSQ 2012) distinguishes between
 Clerical RE activities: simple, repetitive – tools may do this job
 Thinking-required activities that require human analysts
 I argue for swapping the roles: from tool-assisted RE
to human-assisted RE
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6. 1. The Automated Req. Engineer: Vision
 You may already say this is a dystopic vision…
 … I will try to convince you it is a utopic vision instead
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7. 2.Traits of a Good Requirements Engineer
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8. 2. Traits of a Good Req. Engineer
 What makes a requirements engineer good?
 Many characteristics, among which*
1. Competence
2. Ethicality
3. Creativity
* Disclaimers: (1) the list is very incomplete; (2) I picked traits that fit well my claims
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9. 2. Traits of a Good Req. Engineer
Let me illustrate what I mean, rather than provide definitions
1. Competence
 Can employ the most suitable elicitation techniques, has effective
social skills for negotiation etc., can turn needs into specs, …
2. Ethicality
 Responsibility for actions, respect for privacy & other values, …
3. Creativity
 Continuously seeking for innovation? Out-of-the-box thinking?
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10. 2. Traits of a Good Req. Engineer
1. Competence
2. Ethicality
3. Creativity
 … will the automated requirements engineer possess all
these qualities?
 How and when?
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11. 3. Realizing the vision:
where do we stand and where do we go?
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12. 3. Realizing the vision: competence
 What did the RE community
achieve so far?
 NLP analysis of textual requirements
 Defects, ambiguity, tacit knowledge,
extraction of models
 (Semi-)automated traceability
 Logic reasoning on req. models
 E.g., goal model analysis, spec
verification, variants derivation
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Results on simple tasks are
quite impressive…
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13. 3. Realizing the vision: competence
 But we are in the infancy of automated reasoning.Take NLP
(from Kambria &White’s Jumping NLP Curves paper)
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14. 3. Realizing the vision: competence
 Why is this so hard? Examples
 Every branch of the bank that is built on a bank shall store its
servers above the water level
 From Facebook: “We analyze the content of your posts…”
 Meaning: “we can” – permission
 “We delete data after 15 days”
 Meaning,“we must?” – obligation
 Pragmatics is needed:“… because of the right-to-be-forgotten law”
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Bottom line: as per today, humans understand this way better than software
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15. 3. Realizing the vision: ethicality
 Does the requirements engineer act ethically?
 Impartially
 Following a code of conduct
 Respecting privacy
 Preventing potential software misuse
 Transparently
 Respecting the customers/users ethical values
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16. 3. Realizing the vision: ethicality
 Do we really trust humans for this?
 Power and politics in RE (Neil Maiden)
 Wouldn’t an algorithm be less biased, auditable, consistent
in its judgment?
 Perhaps not a dystopia after all…
 Challenges: who is held responsible for the algorithm’s decisions?
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17. 3. Realizing the vision: creativity
 Creative thinking is important in RE, we do not want just
the basic requirements!
 How do we define
creativity?
 Often correlated with
novelty and originality
 But how many unique
ideas are there?
 Can a tool look for creative
ideas by scraping the web?
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18. 4. Conclusion and Discussion
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19. 4. Conclusion and Discussion
 The automated requirements engineer
 Swapping roles: human-assisted RE as opposed to tool-assisted RE
 Is still utopic: many years of good RE research are needed
 Is perhaps not that dystopic
 Depends on advances in information retrieval, machine learning,
natural language processing, …
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20. Thanks from the Requirements
Engineering Lab at Utrecht University!
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Fabiano Dalpiaz
Sjaak Brinkkemper
F. Basak Aydemir
Sietse Overbeek
Marcela Ruiz
Garm Lucassen
GerardWagenaar
Davide dell’Anna
Govert-Jan Slob
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