TestSelector: Automatic Test Suite Selection for Student Projects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364213" target="_blank" >RIV/68407700:21730/22:00364213 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-17196-3_17" target="_blank" >https://doi.org/10.1007/978-3-031-17196-3_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-17196-3_17" target="_blank" >10.1007/978-3-031-17196-3_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

TestSelector: Automatic Test Suite Selection for Student Projects

Popis výsledku v původním jazyce

Computer Science course instructors routinely have to create comprehensive test suites to assess programming assignments. The creation of such test suites is typically not trivial as it involves selecting a limited number of tests from a set of (semi-)randomly generated ones. Manual strategies for test selection do not scale when considering large testing inputs needed, for instance, for the assessment of algorithms exercises. To facilitate this process, we present TESTSELECTOR, a new framework for automatic selection of optimal test suites for student projects. The key advantage of TESTSELECTOR over existing approaches is that it is easily extensible with arbitrarily complex code coverage measures, not requiring these measures to be encoded into the logic of an exact constraint solver. We demonstrate the flexibility of TESTSELECTOR by extending it with support for a range of classical code coverage measures and using it to select test suites for a number of real-world algorithms projects, further showing that the selected test suites outperform randomly selected ones in finding bugs in students’ code.

Název v anglickém jazyce

TestSelector: Automatic Test Suite Selection for Student Projects

Popis výsledku anglicky

Computer Science course instructors routinely have to create comprehensive test suites to assess programming assignments. The creation of such test suites is typically not trivial as it involves selecting a limited number of tests from a set of (semi-)randomly generated ones. Manual strategies for test selection do not scale when considering large testing inputs needed, for instance, for the assessment of algorithms exercises. To facilitate this process, we present TESTSELECTOR, a new framework for automatic selection of optimal test suites for student projects. The key advantage of TESTSELECTOR over existing approaches is that it is easily extensible with arbitrarily complex code coverage measures, not requiring these measures to be encoded into the logic of an exact constraint solver. We demonstrate the flexibility of TESTSELECTOR by extending it with support for a range of classical code coverage measures and using it to select test suites for a number of real-world algorithms projects, further showing that the selected test suites outperform randomly selected ones in finding bugs in students’ code.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LL1902" target="_blank" >LL1902: Obohacování SMT řešičů pomocí strojového učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Runtime Verification 22nd International Conference, RV 2022, Tbilisi, Georgia, September 28–30, 2022, Proceedings

ISBN

978-3-031-17195-6

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

10

Strana od-do

283-292

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Tbilisi

Datum konání akce

28. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000866539700017