Resumen:
Automatic Grading of Programming Exercises using Property-Based Testing

Fecha

2016-09-02

Editor

Sistedes

Publicado en

Actas de las XVI Jornadas de Programación y Lenguajes (PROLE 2016)

Licencia Creative Commons

Resumen

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.

Descripción

Acerca de Benac Earle, Clara

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