research-article

Using run time traces in automated programming tutoring

Authors:

Michael Striewe,

Michael GoedickeAuthors Info & Claims

ITiCSE '11: Proceedings of the 16th annual joint conference on Innovation and technology in computer science education

Pages 303 - 307

https://doi.org/10.1145/1999747.1999832

Published: 27 June 2011 Publication History

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Abstract

Running test cases against a student's solution of a programming assignment is one of the easiest ways to generate feedback. If black-box tests are used, students may have difficulties to retrace the complete system behaviour and to find erroneous programming statements. This paper discusses the use of automated trace generation for assisting students in this task. Both manual and automated trace interpretation is discussed and evaluated by examples.

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Cited By

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Lehtinen TSantos ASorva J(2021)Let’s Ask Students About Their Programs, Automatically2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)10.1109/ICPC52881.2021.00054(467-475)Online publication date: May-2021
https://doi.org/10.1109/ICPC52881.2021.00054
Gulwani SRadiček IZuleger F(2018)Automated clustering and program repair for introductory programming assignmentsACM SIGPLAN Notices10.1145/3296979.319238753:4(465-480)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3296979.3192387
Keuning HJeuring JHeeren B(2018)A Systematic Literature Review of Automated Feedback Generation for Programming ExercisesACM Transactions on Computing Education10.1145/323171119:1(1-43)Online publication date: 28-Sep-2018
https://dl.acm.org/doi/10.1145/3231711
Show More Cited By

Index Terms

Using run time traces in automated programming tutoring
1. Applied computing
  1. Education
    1. Computer-assisted instruction
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Process validation
        Traceability

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Information & Contributors

Information

Published In

ITiCSE '11: Proceedings of the 16th annual joint conference on Innovation and technology in computer science education

June 2011

418 pages

ISBN:9781450306973

DOI:10.1145/1999747

General Chair:
Guido Rößling
TU Darmstadt, Germany
,
Program Chairs:
Tom Naps
University of Wisconsin Oshkosh, USA
,
Christian Spannagel
PH Heidelberg, Germany

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

Publication History

Published: 27 June 2011

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SIGCSE

ITiCSE '11: Annual Conference on Innovation and Technology in Computer Science Education

June 27 - 29, 2011

Darmstadt, Germany

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Overall Acceptance Rate 552 of 1,613 submissions, 34%

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Cited By

View all

Lehtinen TSantos ASorva J(2021)Let’s Ask Students About Their Programs, Automatically2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)10.1109/ICPC52881.2021.00054(467-475)Online publication date: May-2021
https://doi.org/10.1109/ICPC52881.2021.00054
Gulwani SRadiček IZuleger F(2018)Automated clustering and program repair for introductory programming assignmentsACM SIGPLAN Notices10.1145/3296979.319238753:4(465-480)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3296979.3192387
Keuning HJeuring JHeeren B(2018)A Systematic Literature Review of Automated Feedback Generation for Programming ExercisesACM Transactions on Computing Education10.1145/323171119:1(1-43)Online publication date: 28-Sep-2018
https://dl.acm.org/doi/10.1145/3231711
Albrecht EGumz FGrabowski JMottok J(2018)Experiences in Introducing Blended Learning in an Introductory Programming CourseProceedings of the 3rd European Conference of Software Engineering Education10.1145/3209087.3209101(93-101)Online publication date: 14-Jun-2018
https://dl.acm.org/doi/10.1145/3209087.3209101
Gulwani SRadiček IZuleger FFoster JGrossman D(2018)Automated clustering and program repair for introductory programming assignmentsProceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3192366.3192387(465-480)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3192366.3192387
Daun MBrings JObe PPohl KMoser SSchumacher HRieB M(2017)Teaching Conceptual Modeling in Online Courses: Coping with the Need for Individual Feedback to Modeling Exercises2017 IEEE 30th Conference on Software Engineering Education and Training (CSEE&T)10.1109/CSEET.2017.30(134-143)Online publication date: Nov-2017
https://doi.org/10.1109/CSEET.2017.30
Le N(2016)A Classification of Adaptive Feedback in Educational Systems for ProgrammingSystems10.3390/systems40200224:2(22)Online publication date: 23-May-2016
https://doi.org/10.3390/systems4020022
Araujo EGaudencio MSerey DFigueiredo J(2016)Applying spectrum-based fault localization on novice's programs2016 IEEE Frontiers in Education Conference (FIE)10.1109/FIE.2016.7757727(1-8)Online publication date: Oct-2016
https://doi.org/10.1109/FIE.2016.7757727
Araujo ESerey DFigueiredo J(2016)Qualitative aspects of students' programs: Can we make them measurable?2016 IEEE Frontiers in Education Conference (FIE)10.1109/FIE.2016.7757725(1-8)Online publication date: Oct-2016
https://doi.org/10.1109/FIE.2016.7757725
Albrecht EGrabowski J(2016)Towards a framework for mining students' programming assignments2016 IEEE Global Engineering Education Conference (EDUCON)10.1109/EDUCON.2016.7474690(1096-1100)Online publication date: Apr-2016
https://doi.org/10.1109/EDUCON.2016.7474690
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