# Automated Performance Testing Based on Active Deep Learning

arXiv: https://arxiv.org/abs/2104.02102 · HTML: https://mlsanity.com/paper/2104.02102/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [On the Evaluation of Conditional GANs](https://mlsanity.com/paper/1907.08175/) (2019)
- [An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning](https://mlsanity.com/paper/1908.06900/) (2019)

## Built on by (most cited first, 4 of 4)

- [NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification](https://mlsanity.com/paper/2112.13214/) (2021)
- [Harnessing the Power of LLMs in Source Code Vulnerability Detection](https://mlsanity.com/paper/2408.03489/) (2024)
- [A Hierarchical Deep Neural Network for Detecting Lines of Codes with Vulnerabilities](https://mlsanity.com/paper/2211.08517/) (2022)
- [The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study](https://mlsanity.com/paper/2206.10210/) (2022)

## Abstract (arXiv)

Generating tests that can reveal performance issues in large and complex software systems within a reasonable amount of time is a challenging task. On one hand, there are numerous combinations of input data values to explore. On the other hand, we have a limited test budget to execute tests. What makes this task even more difficult is the lack of access to source code and the internal details of these systems. In this paper, we present an automated test generation method called ACTA for black-box performance testing. ACTA is based on active learning, which means that it does not require a large set of historical test data to learn about the performance characteristics of the system under test. Instead, it dynamically chooses the tests to execute using uncertainty sampling. ACTA relies on a conditional variant of generative adversarial networks,and facilitates specifying performance requirements in terms of conditions and generating tests that address those conditions.We have evaluated ACTA on a benchmark web application, and the experimental results indicate that this method is comparable with random testing, and two other machine learning methods,i.e. PerfXRL and DN.