ASRLab develops software platforms and research tools for software testing, verification and validation, fault injection, artificial intelligence, robotics, and dependable autonomous systems. These tools support both academic research and practical experimentation, with several projects available as open-source software through the ASRLab GitHub organisation.
STLC Manager
AI-Assisted Software Testing Lifecycle Platform
STLC Manager is an integrated research platform designed to support and automate activities throughout the Software Testing Life Cycle (STLC).
The platform brings together requirements analysis, test design and generation, test optimisation, execution, reporting, and test closure within a unified workflow.
Artificial intelligence and large language models are incorporated into different stages of the process to support more systematic, traceable, and efficient software testing.
STLC Manager is also used as an experimental platform for ASRLab research on AI-assisted software engineering, automated testing, and intelligent verification workflows.
IM-FIT
Mutation-Based Software Fault Injection Tool
IM-FIT is a software fault injection and mutation testing tool developed for Python and ROS-based software systems.
The tool analyses source code, allows users to select target locations, and generates fault-injection scenarios for evaluating software behaviour under abnormal conditions.
IM-FIT can also be used together with robotic simulation environments to investigate software robustness, fault tolerance, and system reliability.
GitHub:
https://github.com/ESOGU-SRLAB/imfit
The existing ASRLab tool page identifies IM-FIT as a fault-injection tool supporting Python and ROS software and simulation-based execution.
CamFITool
Camera Fault Injection Tool
CamFITool is designed for injecting faults into camera-based robotic perception systems.
The tool can modify both live RGB and ToF camera streams and previously recorded image data. Different image degradation scenarios can be introduced to evaluate how perception and AI systems behave under faulty sensor conditions.
CamFITool can also be used to generate fault-injected image datasets for research on anomaly detection, perception robustness, computer vision, and verification of autonomous systems.
GitHub:
https://github.com/ESOGU-SRLAB/camfitool
Related Research:
https://arxiv.org/abs/2108.13803
The existing tool description specifically supports fault injection into RGB/ToF camera data and generation of new image libraries.
SRVT
Simulation-Based Robot Verification Tool
SRVT provides a simulation-based environment for the verification and validation of robotic systems.
The platform combines technologies from the ROS ecosystem to support robot simulation, motion planning, mission execution, and dynamic verification within a common testing environment.
Its architecture integrates Gazebo for simulation, MoveIt for trajectory planning, and ROS-based state-machine mechanisms for mission execution and verification.
SRVT supports repeatable experimentation without requiring every validation scenario to be executed directly on a physical robotic system.
GitHub:
https://github.com/ESOGU-SRLAB/srvt-ros
The original ASRLab page describes SRVT as an integrated ROS verification environment combining Gazebo, MoveIt and ROS SMACH.
CleanAI
Deep Neural Network Model Quality Evaluation Tool
CleanAI is a research tool for analysing and improving the quality of deep neural network models.
It provides model evaluation capabilities based on different neuron coverage metrics and supports the identification of neurons that have limited influence on model behaviour.
The tool can also support pruning experiments aimed at reducing model complexity while preserving similar levels of predictive performance.
CleanAI is used in ASRLab research on AI quality evaluation, trustworthy AI, and systematic testing of neural network models.
GitHub:
https://github.com/ESOGU-SRLAB/CleanAI
The existing Tools page describes CleanAI as a DNN quality-evaluation and optimisation tool providing neuron-coverage metrics and pruning capabilities.
ChArIoT
Cloud and AI-Based Software Testing Platform
ChArIoT explores the integration of cloud technologies and artificial intelligence into software testing processes.
The platform supports research on AI-assisted software testing and provides an experimental environment for investigating how intelligent techniques can improve the generation and evaluation of software test artefacts.
Research conducted around ChArIoT also resulted in the ChArIoT Dataset, which provides software defect and mutation data for machine-learning-based software engineering studies.
Related Dataset:
See the Datasets page.
PHM Tool
Prognostics and Health Management Tool for ROS
The PHM Tool supports reliability and health analysis of robotic systems within the Robot Operating System ecosystem.
Users can model systems using mechanical and electrical components and calculate indicators such as failure rate, reliability, and probability of task completion.
The tool can also receive sensor information from physical robots through ROS topics, allowing reliability-related metrics to be evaluated using real operational data.
GitHub:
https://github.com/ESOGU-SRLAB/phm_tools
ROS Package:
https://github.com/ESOGU-SRLAB/phm_tools-release
Open-Source Development
Several ASRLab research tools are made publicly available to support reproducible research, experimentation, and further development by the research community.
Additional repositories and software resources are available through the ASRLab GitHub organisation.
ASRLab GitHub:
https://github.com/ESOGU-SRLab