What is iASPEC AI software testing platform?
iASPEC AI software testing platform integrates large language models for natural language processing of requirements and machine learning for predictive analytics in defect detection. This enables seamless integration with CI/CD pipelines, supporting agile and waterfall methodologies while ensuring data sovereignty through on-premise deployment.
End-to-End Testing Automation from Concept to Production
Our solutions manages the complete testing process, compatible with tools such as Git for version control, Jenkins for orchestration, and Jira for issue tracking.
Efficiency Boost
Parallel test execution and containerized environments reduce testing cost by 50 to 70%, utilizing scalable resources like Docker and Kubernetes.
Quality Assurance
Achieves over 95% test coverage through AI-generated edge cases, testing script generation and execution, automatic testing execution and issues follow up.
Optimizing Testing Processes with AI Automation
iASPEC AI software testing solutions resolves enterprise challenges in IT application quality management, from testing planning, design, execution, issue tracking and regression testing.
Cost Savings
Reduces testing expenditures by 40 to 60 percent via automation of 70 to 90 percent of tasks, allowing reallocation of senior specialists to strategic initiatives.
Time Efficiency
Accelerates time-to-market by automating manual processes, with predictive analytics identifying potential failures early in the development cycle.
Features of AI Software Testing Platform
Requirement & design document analysis
Utilise LLM to analysis the requirement & design document of an application, automatically identifying testable requirements, flagging ambiguities and suggesting initial high-level test scenarios.
Test planning, case & procedure development
Utilise LLM to analysis test plan and strategy documents, identify tools and test environment, with AI-powered test cases generation and synthetic test data generation.
Test execution, report & compliance monitoring
Intelligent automatic test execution, with anomaly detection. Incorporates machine learning-driven checks for regulatory adherence, including predictive modeling to anticipate compliance risks.
Advancing Enterprise Testing with AI Automation
Our AI Software Testing Platform enhances validation of system functionality, performance under load, security vulnerabilities, and overall reliability. It employs explainable AI techniques to build trust in automated outcomes.
Test coverage with AI-augmented scenarios.
Potential reduction in overall testing costs.
FAQs
Find answers to your questions about our AI Software Testing Platform and their benefits.
iASPEC software testing platform is a specialized software platform that automates the process of checking IT systems for errors before they go live. For banks, this means verifying that core banking software, payment gateways, or compliance tools function correctly under various conditions, such as high transaction volumes or security threats. Banks need it to avoid costly downtime or regulatory penalties, as manual testing often leads to delays and oversights in complex financial environments.
On-premise means the software runs entirely on your bank's own servers, keeping sensitive data like customer transaction details secure within your infrastructure. Unlike cloud options, which might involve third-party data centers, this approach ensures full control over information, helping banks meet strict privacy laws without external access risks.
It handles a range of systems, including those for transaction processing, API testing, user interfaces like online banking apps.
Initial setup, including assessment and basic integration, typically takes 4 to 6 weeks. Banks often notice immediate improvements in testing speed during the pilot phase, with full benefits—like reduced project timelines—realized within 4 to 6 months as the AI adapts to your specific data and processes.
AI software testing solutions connects through standard APIs and connectors to tools such as Jenkins for orchestration and Git for version control. Configuration involves defining hooks in your pipeline scripts to trigger AI-driven test generation and execution phases. For instance, upon code commit, it can automatically analyze changes and run targeted regression tests without manual setup.
Yes, initial model deployment includes basic fine-tuning during the setup phase, with advanced customization in the full rollout stage. This process uses your historical test data to refine large language models and machine learning algorithms, improving accuracy for domain-specific scenarios like financial transaction validation, typically achieving enhancements within 4 to 8 weeks of pilot testing.
The platform accommodates hybrid approaches, including DevOps and shift-left testing, by embedding AI at early development stages. It also supports behavior-driven development through natural language processing of Gherkin syntax for test cases, ensuring alignment with your team's preferred workflow without requiring extensive reconfiguration.
iASPEC AI software testing platform includes adapters for legacy integration, using machine learning to analyze outdated codebases and generate compatible test scripts. For example, it can validate mainframe interactions via API wrappers, reducing error-prone manual testing in mixed environments common in financial institutions.
Ongoing support encompasses 24/7 maintenance, performance tuning based on usage metrics, and SLA-backed response times. This includes access to audit logging for compliance reviews and role-based access controls to manage team permissions, with options for custom SLAs during the contract phase.
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