Grading-Based Test Suite Augmentation

Osei-Owusu, Jonathan; Astorga, Angello; Butler, Liia; Xie, Tao; Challen, Geoffrey

doi:10.1109/ase.2019.00030

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…The authors compare the technique against propertybased testing, a well-known approach for testing functional programs, achieving improvements in detection and eiciency. Another example is GRASA (GRAding-based test Suite Augmentation) [168], which detects and clusters incorrect submissions through paired-program symbolic execution behavioral equivalence, and then generates a minimal set of additional tests exposing the issue to augment the existing test suite.…”

Section: Functionality (Source Code)mentioning

confidence: 99%

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

110

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Practical programming competencies are critical to the success in computer science education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deeply, and fairly fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state-of-the-art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

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Section: Functionality (Source Code)mentioning

confidence: 99%