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Using fast model-based fault localisation to aid students in self-guided program repair and to improve assessment

Using fast model-based fault localisation to aid students in self-guided program repair and to improve assessment
Using fast model-based fault localisation to aid students in self-guided program repair and to improve assessment
Computer science instructors need to manage the rapid improvement of novice programmers through teaching, self-guided learning, and assessment. Appropriate feedback, both generic and personalised, is essential to facilitate student progress. Automated feedback tools can also accelerate the marking process and allow instructors to dedicate more time to other forms of tuition and students to progress more rapidly. Massive Open Online Courses rely on automated tools for both self-guided learning and assessment.

Fault localisation takes a significant part of debugging time. Popular spectrum-based methods do not narrow the potential fault locations sufficiently to assist novices. We therefore use a fast and precise model-based fault localisation method and show how it can be used to improve self-guided learning and accelerate assessment. We apply this to a large selection of actual student coursework submissions, providing more precise localisation within a sub-second response time. We show this using small test suites, already provided in the coursework management system, and on expanded test suites, demonstrating scaling.
We also show compliance with test suites does not predictably score a class of "almost correct" submissions, which our tool highlights
168-173
Association for Computing Machinery
Birch, Geoff
4e118f9f-4a3a-4f28-893d-57423b360c16
Fischer, Bernd
0c9575e6-d099-47f1-b3a2-2dbc93c53d18
Poppleton, Michael
4c60e63f-188c-4636-98b9-de8a42789b1b
Birch, Geoff
4e118f9f-4a3a-4f28-893d-57423b360c16
Fischer, Bernd
0c9575e6-d099-47f1-b3a2-2dbc93c53d18
Poppleton, Michael
4c60e63f-188c-4636-98b9-de8a42789b1b

Birch, Geoff, Fischer, Bernd and Poppleton, Michael (2016) Using fast model-based fault localisation to aid students in self-guided program repair and to improve assessment. In ITiCSE '16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery. pp. 168-173 . (doi:10.1145/2899415.2899433).

Record type: Conference or Workshop Item (Paper)

Abstract

Computer science instructors need to manage the rapid improvement of novice programmers through teaching, self-guided learning, and assessment. Appropriate feedback, both generic and personalised, is essential to facilitate student progress. Automated feedback tools can also accelerate the marking process and allow instructors to dedicate more time to other forms of tuition and students to progress more rapidly. Massive Open Online Courses rely on automated tools for both self-guided learning and assessment.

Fault localisation takes a significant part of debugging time. Popular spectrum-based methods do not narrow the potential fault locations sufficiently to assist novices. We therefore use a fast and precise model-based fault localisation method and show how it can be used to improve self-guided learning and accelerate assessment. We apply this to a large selection of actual student coursework submissions, providing more precise localisation within a sub-second response time. We show this using small test suites, already provided in the coursework management system, and on expanded test suites, demonstrating scaling.
We also show compliance with test suites does not predictably score a class of "almost correct" submissions, which our tool highlights

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More information

Accepted/In Press date: 29 February 2016
e-pub ahead of print date: 11 July 2016
Published date: 11 July 2016
Venue - Dates: 2016 ACM Conference on Innovation and Technology in Computer Science Education, Arequipa, Peru, 2016-07-11 - 2016-07-13
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 401411
URI: http://eprints.soton.ac.uk/id/eprint/401411
PURE UUID: 949b401c-d177-44d1-8329-c5d5749a3276

Catalogue record

Date deposited: 17 Oct 2016 10:50
Last modified: 15 Mar 2024 02:46

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Contributors

Author: Geoff Birch
Author: Bernd Fischer
Author: Michael Poppleton

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