Nafi KKar TRoy BRoy CSchneider KZimmermann TLawall JMarinov D(2019)CLCDSAProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00099(1026-1037)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00099
Gopinath DConverse HPăsăreanu CTaly AZimmermann TLawall JMarinov D(2019)Property inference for deep neural networksProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00079(797-809)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00079
Tao YTang SLiu YXu ZQin SZimmermann TLawall JMarinov D(2019)How do API selections affect the runtime performance of data analytics tasks?Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00067(665-668)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00067
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Index Terms

Top-down teaching: object-oriented programming in CS 1
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Object oriented languages

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Published In

SIGCSE '93: Proceedings of the twenty-fourth SIGCSE technical symposium on Computer science education

April 1993

311 pages

ISBN:0897915658

DOI:10.1145/169070

Chairmen:
Bruce J. Klein
Grand Valley State Univ., Allendale, MI
,
Cary Laxer
Rose-Hulman Institute of Technology, Terre Haute, IN
,
Frank H. Young
Rose-Hulman Institute of Technology, Terre Haute, IN

ACM SIGCSE Bulletin Volume 25, Issue 1
March 1993
307 pages
ISSN:0097-8418
DOI:10.1145/169073
Editor:
James E. Miller
Univ. of Southern Mississippi, Hattiesburg
Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 01 March 1993

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SIGCSE93

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SIGCSE

SIGCSE93: 1993 ACM/SIGCSE Technical Symposium

February 18 - 19, 1993

Indiana, Indianapolis, USA

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Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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Nafi KKar TRoy BRoy CSchneider KZimmermann TLawall JMarinov D(2019)CLCDSAProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00099(1026-1037)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00099
Gopinath DConverse HPăsăreanu CTaly AZimmermann TLawall JMarinov D(2019)Property inference for deep neural networksProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00079(797-809)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00079
Tao YTang SLiu YXu ZQin SZimmermann TLawall JMarinov D(2019)How do API selections affect the runtime performance of data analytics tasks?Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00067(665-668)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00067
Jiang LLiu HJiang HZimmermann TLawall JMarinov D(2019)Machine learning based recommendation of method namesProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00062(602-614)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00062
Michael LDonohue JDavis JLee DServant FZimmermann TLawall JMarinov D(2019)Regexes are hardProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00047(415-426)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00047
Cashin PMartinez CWeimer WForrest SZimmermann TLawall JMarinov D(2019)Understanding automatically-generated patches through symbolic invariant differencesProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00046(411-414)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1109/ASE.2019.00046
Behrang FOrso AZimmermann TLawall JMarinov D(2019)Test migration between mobile apps with similar functionalityProceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2019.00016(54-65)Online publication date: 10-Nov-2019
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Gaeta R(2018)A Model of Information Diffusion in Interconnected Online Social NetworksACM Transactions on the Web10.1145/316000012:2(1-21)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1145/3160000
Svärd PHudzia BTordsson JElmroth E(2011)Evaluation of delta compression techniques for efficient live migration of large virtual machinesACM SIGPLAN Notices10.1145/2007477.195269846:7(111-120)Online publication date: 9-Mar-2011
https://dl.acm.org/doi/10.1145/2007477.1952698
Zhang IGarthwaite ABaskakov YBarr K(2011)Fast restore of checkpointed memory using working set estimationACM SIGPLAN Notices10.1145/2007477.195269546:7(87-98)Online publication date: 9-Mar-2011
https://dl.acm.org/doi/10.1145/2007477.1952695
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