Poster : Performance Testing Driven by Reinforcement Learning

• Performance testing remains a challenge, particularly for complex systems. Different application-, platform- and workload-based factors can influence the performance of software under test. Common approaches for generating platform- and workload-based test conditions are often based on system model or source code analysis, real usage modeling and use-case based design techniques. Nonetheless, creating a detailed performance model is often difficult, and also those artifacts might not be always available during the testing. On the other hand, test automation solutions such as automated test case generation can enable effort and cost reduction with the potential to improve the intended test criteria coverage. Furthermore, if the optimal way (policy) to generate test cases can be learnt by testing system, then the learnt policy can be reused in further testing situations such as testing variants, evolved versions of software, and different testing scenarios. This capability can lead to additional cost and computation time saving in the testing process. In this research, we present an autonomous performance testing framework which uses a model-free reinforcement learning augmented by fuzzy logic and self-adaptive strategies. It is able to learn the optimal policy to generate platform- and workload-based test conditions which result in meeting the intended testing objective without access to system model and source code. The use of fuzzy logic and self-adaptive strategy helps to tackle the issue of uncertainty and improve the accuracy and adaptivity of the proposed learning. Our evaluation experiments show that the proposed autonomous performance testing framework is able to generate the test conditions efficiently and in a way adaptive to varying testing situations.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

learning (artificial intelligence)

program testing

source code (software)

complex systems

workload-based factors

workload-based test conditions

system model

usage modeling

use-case based design techniques

test automation solutions

automated test case generation

intended test criteria coverage

testing system

testing situations

testing variants

testing process

autonomous performance testing framework

model-free reinforcement learning

intended testing objective

source code

Unified modeling language

Stress

Time factors

Sensitivity

Error analysis

Adaptation models

performance testing

stress testing

load testing

machine learning

reinforcement learning

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)