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The Changing Landscape of Programming Technology. Karl Lieberherr Northeastern University. Swiss connection. PhD from ETH Zurich: Pascal, Modula, Oberon (Wirth) Sabbatical 2000 with UBS Zurich Sabbatical 2006 with Novartis Collaborations with ABB and Mettler-Toledo One Swiss PhD

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the changing landscape of programming technology

The Changing Landscape of Programming Technology

Karl Lieberherr

Northeastern University

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swiss connection
Swiss connection
  • PhD from ETH Zurich: Pascal, Modula, Oberon (Wirth)
  • Sabbatical 2000 with UBS Zurich
  • Sabbatical 2006 with Novartis
  • Collaborations with ABB and Mettler-Toledo
  • One Swiss PhD
  • Yearly summer vacation in Switzerland

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how i came to boston
How I came to Boston
  • 1983: GTE in Waltham, now Verizon
  • 1985: tenured full professor at Northeastern
  • I like it here!

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slide4
Goal
  • We want reliable and secure software.
  • No technical vulnerabilities.
  • Not enough: social vulnerabilities are also very important in the age of the web.

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outline
Outline
  • Program Organization
    • Crosscutting Concerns
    • Law of Demeter
  • Programming Team Organization
    • Extreme Programming
  • Security:
    • Social Phishing
    • SAT solvers

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software bugs
Software bugs
  • Excel 2007 multiplication bug: September 2007
    • 850 * 77.1 should be 65535 but displays as 100000.

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program organization
Program Organization
  • cross-cutting concerns
  • the tyranny of the primary decomposition
    • organize software into modules
  • group functionality that lives in several modules

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law of demeter
Law of Demeter
  • Organization principle for objects
    • “talk only to your friends”
    • “each object talks only to a limited number of other objects”
    • avoids information overload for the programmer
    • from Northeastern
  • Leads to objects that are easier to change

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extreme programming
Extreme Programming
  • Planning around User Stories
  • Small Releases of Functionality
  • Simple Design
    • build what is asked for, no design for the future
  • Continuous testing

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extreme programming10
Extreme Programming
  • Pair Programming
  • Collective Product (Code) Ownership
    • Design, Coding Standards
  • Continuous integration
  • On-site Customer Representative

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sat solvers
SAT Solvers
  • a fundamental topic in computer science
  • express your “wishes”
  • computer satisfies as many as possible
  • used for software and hardware verification
  • based on learning from mistakes (non-chronological backtracking)
  • can solve systems with over 100000 parameters (variables)

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social phishing
Social Phishing
  • friendly email message tempts recipients to reveal more online than they otherwise would.
  • impersonating a trustworthy entity

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class project at johns hopkins
class project at Johns Hopkins
  • find publicly available information from social networks: MySpace, Facebook, LinkedIn.
  • how can a phisher exploit social network data? very easily and effectively
  • over four times as likely to become a victim if they are solicited by someone appearing to be a known acquaintance

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observations
Observations
  • 77% females, 65% males
  • But trick males by sending them a spoofed message appearing to come from a female (68% if message from female versus 53% if from another male)
  • somewhat reassuringly: computer science students were the least vulnerable

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observations observed reactions after debriefing message
Observations: observed reactions after debriefing message
  • Anger
    • significant social cost to victims
  • Denial
    • we find it difficult to admit our own vulnerability: many successful phishing attacks go unreported

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conclusions
Conclusions
  • Still a very exciting time in computer and information science.
  • Computer science students have excellent job prospects, despite outsourcing.

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