SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:DiVA.org:ri-52628"
 

Sökning: id:"swepub:oai:DiVA.org:ri-52628" > (2022) > An autonomous perfo...

An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning

Helali Moghadam, Mahshid (författare)
Mälardalens högskola,RISE,Industriella system,Mälardalen University, Sweden,Inbyggda system,RISE Research Institutes of Sweden,Software Testing Lab
Saadatmand, Mehrdad, 1980- (författare)
Mälardalens högskola,RISE,Industriella system,Inbyggda system,RISE Research Institutes of Sweden
Borg, Markus (författare)
RISE,Mobilitet och system,RISE Research Institutes of Sweden
visa fler...
Bohlin, Markus, 1976- (författare)
Mälardalen University, Sweden,Mälardalens universitet, Innovation och produktrealisering
Lisper, Björn (författare)
Mälardalens högskola,Inbyggda system
visa färre...
 (creator_code:org_t)
2021-03-10
2022
Engelska.
Ingår i: Software quality journal. - : Springer. - 0963-9314 .- 1573-1367. ; , s. 127-159
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case-based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments in a simulated performance testing setup, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process and performs adaptively without access to source code and performance models. © 2021, The Author(s).

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Autonomous testing
Performance testing
Reinforcement learning
Stress testing
Test case generation
Automation
Computer programming languages
Testing
Transfer learning
Automated generation
Optimal performance
Performance Model
Performance testing framework
Performance tests
Simulated performance
Software systems
Software testing
Computer Science

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy