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Title:Učinkovitost avtomatiziranega oblikovanja testnih primerov s pomočjo velikih jezikovnih modelov
Authors:ID Murdjeva, Jovana (Author)
ID Beranič, Tina (Mentor) More about this mentor... New window
ID Karakatič, Sašo (Comentor)
Files:.pdf MAG_Murdjeva_Jovana_2024.pdf (1,31 MB)
MD5: BBB4A8768285AE00DA88F9599212C6A6
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu je bila raziskana uporabo ChatGPT-ja kot veliki jezikovni model za avtomatizirano oblikovanje testnih primerov v primerjavi s tradicionalnimi metodami, ki jih uporabljajo strokovnjaki za testiranje programske opreme. Delo se je osredotočilo na vpliv natančno opredeljenih pozivov (inženiring pozivov) na kakovost, pokritost kode in učinkovitost pri odkrivanju napak. Rezultati raziskave kažejo, da ChatGPT z ustrezno oblikovanimi vnosnimi zahtevami dosega primerljivo ali celo boljšo uspešnost kot ročno oblikovani testi, kar pomeni velik potencial za optimizacijo procesov testiranja programske opreme.
Keywords:avtomatizirano testiranje, veliki jezikovni modeli, inženiring pozivov, ChatGPT, kakovost testnih primerov
Place of publishing:Maribor
Year of publishing:2024
PID:20.500.12556/DKUM-90728 New window
Publication date in DKUM:22.10.2024
Views:0
Downloads:21
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:18.09.2024

Secondary language

Language:English
Title:The effectiveness of automated test case design using large language models
Abstract:In the master's thesis, we explored the use of ChatGPT, as a large language model, for automated test case design compared to traditional methods used by software testing experts. We focused on the impact of precisely defined prompts (prompt engineering) on the quality, code coverage, and effectiveness in detecting errors. The research results indicate that ChatGPT, with properly crafted input requirements, achieves comparable or even better performance than manually designed tests, highlighting significant potential for optimizing software testing processes.
Keywords:automated testing, large language models, prompt engineering, ChatGPT, test case quality


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