9 jul 2016 año - Automatic Grading of Programming Exercises Using Property-Based Testing

Descripción:

Authors:
- Clara Benac Earle
- Lars-Äke Fredlund
- John Hughes

Conference:
Innovation and Technology in Computer Science Education 2017 (ITiCSE'17)

Abstract:
We present a framework for automatic grading of programming exercises using property-based testing, a form of model-based black-box testing. Models are developed to assess both the functional behaviour of programs and their algorithmic complexity. From the functional correctness model a large number of test cases are derived automatically. Executing them on the body of exercises gives rise to a (partial) ranking of programs, so that a program A is ranked higher than program B if it fails a strict subset of the test cases failed by B. The model for algorithmic complexity is used to compute worst-case complexity bounds. The framework moreover considers code structural metrics, such as McCabe's cyclomatic complexity, giving rise to a composite program grade that includes both functional, non-functional, and code structural aspects. The framework is evaluated in a course teaching algorithms and data structures using Java.

Keywords:
Automated assessment; Testing; Java

Link:
file:///C:/Users/yeray/Zotero/storage/7ADTSDG6/Benac%20Earle%20et%20al.%20-%202016%20-%20Automatic%20Grading%20of%20Programming%20Exercises%20Using%20P.pdf