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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 l.jpg

The Changing Landscape of Programming Technology

Karl Lieberherr

Northeastern University

Friends of Switzerland


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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

  • 1983: GTE in Waltham, now Verizon

  • 1985: tenured full professor at Northeastern

  • I like it here!

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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

  • Program Organization

    • Crosscutting Concerns

    • Law of Demeter

  • Programming Team Organization

    • Extreme Programming

  • Security:

    • Social Phishing

    • SAT solvers

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Software bugs

  • Excel 2007 multiplication bug: September 2007

    • 850 * 77.1 should be 65535 but displays as 100000.

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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

  • 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

  • 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 Programming

  • Pair Programming

  • Collective Product (Code) Ownership

    • Design, Coding Standards

  • Continuous integration

  • On-site Customer Representative

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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

  • 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

  • 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

  • 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

  • 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

  • Still a very exciting time in computer and information science.

  • Computer science students have excellent job prospects, despite outsourcing.

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