The first "Insights in Technology Conference" was in Schaffhausen on December 16, 2019. The event is organized by the Schaffhausen Institute of Technology SIT. Special guest is Nobel Prize winner Wolfgang Ketterle. Schaffhausen Institute of Technology website: http://sit.org

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Bertrand Meyer,
Professor of Software Engineering
and Provost, SIT
Challenges in computing and
software engineering research

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Five directions that
are changing the world
1) The new Artificial Intelligence
2) Big data and machine learning
3) Cybersecurity
4) Cloud computing and DevOps
5) Large-scale software engineering

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The old AI
• Logic-based
• Human input
• Failed! “AI Winter”, 1970s-2000s

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The revenge of the flies
Maurice Maeterlinck, La Vie des Abeilles, 1901

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The new AI
• Statistics-based, Bayesian methods
• Automatic input, from examples
• Is succeeding on a large scale!
• But many challenges ahead
Case in point: Google Translate

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Software engineering for AI and ML
Critical issues remain:
• Justification of results
• Correctness / reliability of results
• Avoiding the reproduction of current biases
• Dealing with cases with very small data records

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AI and ML for software engineering
A major transformation: empirical software engineering
Questions:
• Are more tested modules more or less buggy than others?
• Is the complexity of a module a good predictor of its bug rate?
• Are bugs in Eclipse due more to novices or experts? Zeller
Succi
Nagappan, Basili
Histories going back to: • Windows, 1983
• EiffelStudio, 1990
• Linux, 1991
• Apache, 1995
• Eclipse, 2001

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Ethics
Henry Baker, comp.risks forum, 26 Sep 2015 (abridged)
Consider a software model of a computer with hundreds of sensors and actuators (that
watches over a diesel engine while driven through a standard EPA emissions test. It can
sense air temperature, humidity, engine speed etc., and control air flow, fuel flow etc.
We put this system through millions of miles of "learning“ so
that the AI/ML algorithm learns to optimize inputs like fuel
while meeting EPA criteria.
This algorithm will quickly notice that the best way to optimize for the EPA test is that when the
hood is open and the steering wheel is straight ahead is a good time to optimize NOx and other
emissions, while during other conditions (perhaps a curving country lane) emissions aren't so
important relative to performance.
Is this learning program "unethical" wrt the EPA tests ?
Should it be fined or go to jail?

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Software verification
Annotation effort
Benefits Small Medium Large
Spot checks
Model checking
Abstract Interpretation
Testing
(need test cases & oracles)
Full correctness
Axiomatic
semantics

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A dark spot of software engineering:
requirements
At the frontier between geek and non-geek
Complete disconnect between academia and practice
What industry does: “User Stories” (!)
Agile methods: no “big upfront requirements”

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Mars Polar Lander
11

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The problem
12

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Annotations: formal methods
{s: Swiss | (∀y: Year | s.is_moving (y))} = slice
∀y: Year | {s: Swiss | s.is_moving (y)} = slice

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DevOps: what version
of search are you using?
1980s: Design and development: unite!
2000s: Requirements and development: unite!
2010s: Operation, deployment and development: unite!
Object technology
Agile!
DevOps

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The size of what we do
Unix V7: 10K 1990
1992
1995 1998 2000
Red Hat 7.1: 30
Linux: 10 K
10
20
40
30
Lines of code (millions)
Windows 3.1: 3 M
Windows NT: 4 M
Windows 95: 15
Windows 98: 18
Windows 2000: 40
Red Hat 6.2: 17
Solaris 7: 12
Windows XP: 45
2006
50
Vista: 50
Debian 2.2: 55
Debian 3.1: 213!
2001
Google: 2 billion
Compare with:
• Technical systems
• Human systems

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SIT research today
Two research chairs in software
engineering / verification / security
Bertrand
Meyer
SIT Professor of
Software Engineering
and Provost
Mauro
Pezze
SIT Professor of
Software Quality and
Cybersecurity
Software Analysis Factory
SAFProgram
Questions
Language
Answers
Outputs
• Software
• Papers
• PhD theses
• Patents
• Technology transfer

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Come join us!
UNIVERSITY • RESEARCH • TECHPARK • ECOSYSTEM • R&D CENTERS • STARTUPS