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Research Article |

Debugging Support Tool Using Knowledge of Failure

The objective of this study is to realize a debugging support tool to help programming novices solve program errors efficiently. The proposed tool accumulates and stores failure knowledge, including examples of errors encountered by novice programmers and their solutions. By utilizing this database, the tool can search and find appropriate solutions to errors encountered by users in real time. Errors are categorized as compile errors, link errors, run-time errors, and logic errors, each of which has its own unique solution. In this study, particular emphasis is placed on assistance with compile errors. Failure knowledge consists of sample source code that includes the location and cause of errors encountered by the novice user, as well as solutions. When an error occurs, the user enters the relevant source code and error message into the tool, and the tool searches the database to provide the corresponding solution and explanation. The system also provides a space for users to enter their own analyses and explanations, which can be shared with other novices to promote mutual understanding. The proposed system utilizes a structured database for efficient error resolution, containing tables for storing and retrieving error messages, corresponding references, and user considerations. The effectiveness and applicability of the proposed tool should be verified.

Programming Education, Web Application, Debugging Support, Machine Learning

APA Style

Zhu, X. (2023). Debugging Support Tool Using Knowledge of Failure. Science Journal of Education, 11(6), 211-219. https://doi.org/10.11648/j.sjedu.20231106.15

ACS Style

Zhu, X. Debugging Support Tool Using Knowledge of Failure. Sci. J. Educ. 2023, 11(6), 211-219. doi: 10.11648/j.sjedu.20231106.15

AMA Style

Zhu X. Debugging Support Tool Using Knowledge of Failure. Sci J Educ. 2023;11(6):211-219. doi: 10.11648/j.sjedu.20231106.15

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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