# TestLab: An Intelligent Automated Software Testing Framework

arXiv: https://arxiv.org/abs/2306.03602 · HTML: https://mlsanity.com/paper/2306.03602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)

## Built on by (most cited first, 1 of 1)

- [FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer](https://mlsanity.com/paper/2407.14361/) (2024)

## Abstract (arXiv)

The prevalence of software systems has become an integral part of modern-day living. Software usage has increased significantly, leading to its growth in both size and complexity. Consequently, software development is becoming a more time-consuming process. In an attempt to accelerate the development cycle, the testing phase is often neglected, leading to the deployment of flawed systems that can have significant implications on the users daily activities. This work presents TestLab, an intelligent automated software testing framework that attempts to gather a set of testing methods and automate them using Artificial Intelligence to allow continuous testing of software systems at multiple levels from different scopes, ranging from developers to end-users. The tool consists of three modules, each serving a distinct purpose. The first two modules aim to identify vulnerabilities from different perspectives, while the third module enhances traditional automated software testing by automatically generating test cases through source code analysis.