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  5. Categorizing Compiler Error Messages with Principal Component Analysis
 
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Categorizing Compiler Error Messages with Principal Component Analysis

Author(s)
Becker, Brett A.  
Mooney, Catherine  
Uri
http://hdl.handle.net/10197/7889
Date Issued
2016-05-29
Date Available
2016-09-07T14:41:40Z
Abstract
Being a competent programmer is critical for students in all computing disciplines and software engineering in particular. Novice programming students face a number of challenges and these have been shown to contribute to worrying dropout rates for students majoring in computing, and the growing number of non-majors who are learning to program. Methods of identifying and helping at-risk programming students have been researched for decades. Much of this research focuses on categorizing the errors that novice programmers make, in order to help understand why these errors are made, with the goal of helping them overcome these errors quickly, or avoid them altogether. This paper presents the first known work on categorizing compiler errors using Principal Component Analysis. In this, we find a new way of discovering categories of related errors from data produced by the students in the course of their programming activity. This method may be used to identify where these students are struggling and provide direction in efforts to help them.
Type of Material
Conference Publication
Subjects

Compiler errors

Compiler error messag...

Novice programmers

CS1

Error message categor...

Java programming

Principal component a...

Web versions
http://2016.ceisee.top/
Language
English
Status of Item
Peer reviewed
Conference Details
12th China-Europe International Symposium on Software Engineering Education (CEISEE 2016), Shenyang, China, 28-29 May 2016
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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Becker_CEISEE_2016_FINAL_V2_Preprint.pdf

Size

248.17 KB

Format

Adobe PDF

Checksum (MD5)

a640eb207c6d98c45e4ecbeb9ec09a18

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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