XCSF for Automatic Test Case Prioritization

2020-11-04 · IJCCI 2020 11 · Lukas Rosenbauer, Anthony Stein, David Pätzel, Jörg Hähner

Testing is a crucial part in the development of a new product. Due to the change from manual testing to automated testing, companies can rely on a higher number of tests. There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary. This prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an XCS classifier system have been applied to this task. Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both the neural network and XCS for this problem.

📄 PDF Abstract BibTeX

Code (1)

LagLukas/xcsf_atcs

Tasks

reinforcement-learning Reinforcement Learning (RL)

XCSF with Experience Replay for Automatic Test Case Prioritization

2020-12-04 · IEEE Symposium Series on Computational Intelligence 2020 12 · Lukas Rosenbauer, Anthony Stein, David Pätzel, Jörg Hähner

The verification of a new product is of major importance for companies. With the rise of test automation, companies start to rely on huge numbers of tests. Often, it is not feasible to run all available tests due to time…

Transfer Learning for Automated Test Case Prioritization Using XCSF

2021-03-15 · Springer EvoStar 2021 3 · Lukas Rosenbauer, Anthony Stein, David Pätzel, Jörg Hähner

With the rise of test automation, companies start to rely on large amounts of test cases. However, there are situations where it is unfeasible to perform every test case as only a limited amount of time is available. Und…

reinforcement-learning Reinforcement Learning (RL)Transfer Learning

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

2012-01-26 · Richard J. Preen, Larry Bull

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete a…

Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

2018-11-09 · Helge Spieker, Arnaud Gotlieb, Dusica Marijan, Morten Mossige

Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

X-KAN: Optimizing Local Kolmogorov-Arnold Networks via Evolutionary Rule-Based Machine Learning

2025-05-20 · Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata

Function approximation is a critical task in various fields. However, existing neural network approaches struggle with locally complex or discontinuous functions due to their reliance on a single global model covering th…

Kolmogorov-Arnold Networks