More Web Proxy on the site http://driver.im/

research-article

Common logic errors made by novice programmers

Authors:

Andrew Luxton-Reilly,

Paul DennyAuthors Info & Claims

ACE '18: Proceedings of the 20th Australasian Computing Education Conference

Pages 83 - 89

https://doi.org/10.1145/3160489.3160493

Published: 30 January 2018 Publication History

Abstract

Errors in the logic of a program (sometimes referred to as semantic errors) can be very frustrating for novice programmers to locate and resolve. Developing a better understanding of the kinds of logic error that are most common and problematic for students, and finding strategies for targeting them, may help to inform teaching practice and reduce student frustration.

In this paper we analyse 15,000 code fragments, created by novice programming students, that contain logic errors, and we classify the errors into algorithmic errors, misinterpretations of the problem, and fundamental misconceptions. We find that misconceptions are the most frequent source of logic errors, and lead to the most difficult errors for students to resolve. We list the most common errors of this type as a starting point for designing specific teaching interventions to address them.

References

[1]

Marzieh Ahmadzadeh, Dave Elliman, and Colin Higgins. 2005. An Analysis of Patterns of Debugging Among Novice Computer Science Students. SIGCSE Bull. 37, 3 (June 2005), 84--88.

Digital Library

[2]

Amjad Altadmri and Neil C.C. Brown. 2015. 37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE '15). ACM, New York, NY, USA, 522--527.

Digital Library

[3]

Brett A. Becker. 2016. An Effective Approach to Enhancing Compiler Error Messages. In Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE '16). ACM, New York, NY, USA, 126--131.

Digital Library

[4]

Brett A. Becker. 2016. A New Metric to Quantify Repeated Compiler Errors for Novice Programmers. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '16). ACM, New York, NY, USA, 296--301.

Digital Library

[5]

Brett A. Becker, Graham Glanville, Ricardo Iwashima, Claire McDonnell, Kyle Goslin, and Catherine Mooney. 2016. Effective compiler error message enhancement for novice programming students. Computer Science Education 26, 2-3 (2016), 148--175.

[6]

Clara Benac Earle, Lars-Åke Fredlund, and John Hughes. 2016. Automatic Grading of Programming Exercises Using Property-Based Testing. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '16). ACM, New York, NY, USA, 47--52.

Digital Library

[7]

Paul Denny, Andrew Luxton-Reilly, and Ewan Tempero. 2012. All Syntax Errors Are Not Equal. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE '12). ACM, NewYork, NY, USA, 75--80.

Digital Library

[8]

Paul Denny, Andrew Luxton-Reilly, Ewan Tempero, and Jacob Hendrickx. 2011. CodeWrite: Supporting Student-driven Practice of Java. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (SIGCSE '11). ACM, New York, NY, USA, 471--476.

Digital Library

[9]

Sue C Fitzgerald, Gary Lewandowski, Renée Mccauley, Laurie Murphy, Beth Simon, Lynda Thomas, and Carol Zander. 2008. Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers. Computer Science Education 18 (June 2008), 93--116.

[10]

Maria Hristova, Ananya Misra, Megan Rutter, and Rebecca Mercuri. 2003. Identifying and Correcting Java Programming Errors for Introductory Computer Science Students. SIGCSE Bull. 35, 1 (Jan. 2003), 153--156.

Digital Library

[11]

Davin McCall and Michael Kölling. 2014. Meaningful categorisation of novice programmer errors. In Frontiers in Education Conference (FIE 2014). IEEE.

[12]

Michael McCracken, Vicki Almstrum, Danny Diaz, Mark Guzdial, Dianne Hagan, Yifat Ben-David Kolikant, Cary Laxer, Lynda Thomas, Ian Utting, and Tadeusz Wilusz. 2001. A Multi-national, Multi-institutional Study of Assessment of Programming Skills of First-year CS Students. SIGCSE Bull. 33, 4 (Dec. 2001), 125--180.

Digital Library

[13]

Raymond S. Pettit, John Homer, and Roger Gee. 2017. Do Enhanced Compiler Error Messages Help Students? Results Inconclusive. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE '17). ACM, New York, NY, USA, 465--470.

Digital Library

[14]

James Prather, Raymond Pettit, Kayla Holcomb McMurry, Alani Peters, John Homer, Nevan Simone, and Maxine Cohen. 2017. On Novices' Interaction with Compiler Error Messages: A Human Factors Approach. In Proceedings of the 2017 ACM Conference on International Computing Education Research (ICER '17). ACM, New York, NY, USA, 74--82.

Digital Library

[15]

Teemu Sirkiä and Juha Sorva. 2012. Exploring Programming Misconceptions: An Analysis of Student Mistakes in Visual Program Simulation Exercises. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research (Koli Calling '12). ACM, New York, NY, USA, 19--28.

Digital Library

[16]

James C. Spohrer and Elliot Soloway. 1986. Novice Mistakes: Are the Folk Wisdoms Correct? Commun. ACM 29, 7 (July 1986), 624--632.

Digital Library

Cited By

MacNeil SRogalska MLeinonen JDenny PHellas ACrosland XDorodchi MZhange MCooper S(2024)Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing StudentsProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690100(137-143)Online publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1145/3649165.3690100
S Kumar SAdam Lones MMaarek MZantout H(2024)Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student ProgrammersProceedings of the 1st International Workshop on Large Language Models for Code10.1145/3643795.3648380(88-93)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3643795.3648380
Fwa HRoychoudhury APaiva AAbreu RStorey MGama KSiegmund J(2024)Experience Report: Identifying common misconceptions and errors of novice programmers with ChatGPTProceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training10.1145/3639474.3640059(233-241)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639474.3640059
Show More Cited By

Index Terms

Common logic errors made by novice programmers
1. Social and professional topics
  1. Professional topics
    1. Computing education

Recommendations

A New Look at Novice Programmer Errors
Special Section on ML Education and Regular Articles

The types of programming errors that novice programmers make and struggle to resolve have long been of interest to researchers. Various past studies have analyzed the frequency of compiler diagnostic messages. This information, however, does not have a ...
Investigating Novice Programmers' Interaction with Programming Environments
ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

Learning computer programming can be challenging for novices. Students have to deal with theoretical aspects of programming and problem solving in general, as well as mastering the syntax of a programming language. However, the feedback students receive ...
Exploring Novice Programming Behavior over Time
ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 2

This work focuses on the effect that programming time has on novice programmers' interaction with two versions of the BlueJ programming environment that differ in compilation mechanism and error message presentation. We utilize programming process data ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACE '18: Proceedings of the 20th Australasian Computing Education Conference

January 2018

127 pages

ISBN:9781450363402

DOI:10.1145/3160489

Conference Chairs:
Raina Mason
Southern Cross University, Australia
,
Simon
University of Newcastle, Australia

Copyright © 2018 ACM.

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 the author(s) 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].

Sponsors

The University of Newcastle, Australia
CORE - Computing Research and Education
Southern Cross UNIVERSITY: Southern Cross UNIVERSITY
SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 January 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ACE 2018

Sponsor:

Southern Cross UNIVERSITY
SIGCSE

ACE 2018: 20th Australasian Computing Education Conference

January 30 - February 2, 2018

Queensland, Brisbane, Australia

Acceptance Rates

ACE '18 Paper Acceptance Rate 14 of 36 submissions, 39%;

Overall Acceptance Rate 161 of 359 submissions, 45%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

38
Total Citations
View Citations
921
Total Downloads

Downloads (Last 12 months)155
Downloads (Last 6 weeks)20

Reflects downloads up to 21 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

MacNeil SRogalska MLeinonen JDenny PHellas ACrosland XDorodchi MZhange MCooper S(2024)Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing StudentsProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690100(137-143)Online publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1145/3649165.3690100
S Kumar SAdam Lones MMaarek MZantout H(2024)Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student ProgrammersProceedings of the 1st International Workshop on Large Language Models for Code10.1145/3643795.3648380(88-93)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3643795.3648380
Fwa HRoychoudhury APaiva AAbreu RStorey MGama KSiegmund J(2024)Experience Report: Identifying common misconceptions and errors of novice programmers with ChatGPTProceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training10.1145/3639474.3640059(233-241)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639474.3640059
Macneil SDenny PTran ALeinonen JBernstein SHellas ASarsa SKim J(2024)Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language ModelsProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636245(11-18)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3636243.3636245
Hoq MVandenberg JMott BLester JNorouzi NAkram BStephenson BStone JBattestilli LRebelsky SShoop L(2024)Towards Attention-Based Automatic Misconception Identification in Introductory Programming CoursesProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 210.1145/3626253.3635575(1680-1681)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1145/3626253.3635575
Jordan MLy KSoosai Raj AStephenson BStone JBattestilli LRebelsky SShoop L(2024)Need a Programming Exercise Generated in Your Native Language? ChatGPT's Got Your Back: Automatic Generation of Non-English Programming Exercises Using OpenAI GPT-3.5Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630897(618-624)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630897
Al-Hossami EBunescu RSmith JTeehan RStephenson BStone JBattestilli LRebelsky SShoop L(2024)Can Language Models Employ the Socratic Method? Experiments with Code DebuggingProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630799(53-59)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630799
Utomo MKusumo DRichasdy D(2024)Analyzing Sweep Integration in GitHub for AI-Augmented Issue Resolution2024 12th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT61617.2024.10698190(462-469)Online publication date: 7-Aug-2024
https://doi.org/10.1109/ICoICT61617.2024.10698190
Alqadi B(2024)Classifying Programming Ability: A Machine Learning Approach Using Debugging Metrics2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE)10.1109/ICECCE63537.2024.10823541(1-6)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ICECCE63537.2024.10823541
Alqadi B(2024)Enhancing Novice Programmers’ Debugging Skills Through Systematic Education: A Comparative StudyIEEE Access10.1109/ACCESS.2024.350964112(181192-181204)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3509641
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents