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ACCELQ vs Tosca – A Snapshot

Cloud based modern platform IDE based 1st Gen tool
User experience focused design, simple to use Old school complex and clunky interface
Intuitive and Natural UI flow enables Fast Adoption (~1-3 weeks) Needs specialized skills and months of experience to gain expertise
Design first approach with Business process Application modelling Folder based object library approach with no relation to Application flow business processes
Test development exponentially accelerates when scaling up automation Script development becomes complex and slow when scaling up automation
Maintenance significantly reduces when scaling up automation Maintenance significantly increases when scaling up automation
Seamless extendible approach for Web-Mobile-API in one codeless flow Engine based disintegrated step based approach with complex reusability
Embedded asset collaboration and management for Smart Test Plans & Governance Disconnected Repository with no transparency of test asset management and planning
One native platform built with no Silos Separate tools with patches to get functionality check box
Included Test Management Requires external tools to integrate with disparate data flow

Why ACCELQ is a better alternative to TOSCA?

AI and ML

Built with AI/ML ground up organically across each area to bring automation across lifecycle AI is forced into object handling with no connectivity to design and flow
Natural Language Automation Zero code, but as powerful. No programming needed across Web UI, Mobile, API, Files, Packaged Apps, Databases etc. No use of AI, keyword driven traditional approach to logic development
Functional Virtualization & Reconciliation AI based reconciliation of abstraction. Enables in-sprint automation removing application development and stability dependency and implements true TDD No capability around functional abstraction, only limited service virtualization
Element handling & Bot healing AI based object interaction algorithm with run-time self-healing and BOT based troubleshooting analysis for robust and sustainable automation Newly introduced limited AI based interaction not embedded with development flow and design
Auto generation of test cases Algorithmic AI driven approach to automatic calculation of permutation and combinations of data to ensure coverage with a scientific approach Not available
Adaptive Change Management Universe driven with referential integrity and path analysis algorithms automate change impact significantly reducing automation maintenance No AI in change impact, manual process with resource knowledge dependency. Slow and not scalable

In-sprint Automation & Shift-left

Application functional abstraction Not available
Design & author tests parallel to Application development enabling true shift-left Complex image based approach
AI based Re-conciliation of abstraction Not available
API, DB and middleware Automation with UI Available with complex engine module, not seamless

Test Design

Universe based visual Application modelling Not available
Test scenarios are modularized into Pages and User Actions Folder based libraries with function based technical approach lacks relationship to Application
Page objects are naturally & automatically centralized across test scenarios Objects are duplicated across libraries that may or may not get re-used
Design is enforced with intuitive and automatic universe concept Design is manually implemented, and knowledge is limited with specialized "Architects" in team
Data-driven scenarios and Test Case data is inherent to scenario design Data-driven is a disconnected step once test case is automated and adds overhead
Parameterization with design alignment to input, environment, run-time, linking and more Technically oriented variable concept defined after the test case is defined, which reduces modularity

Automation Development

Codeless natural English coupled with recording Locked down interface to define logic needing specialized training
Logic editor provides readymade conditional rule validations, loops etc.… for real life tests Complex way to achieve business rule validations needing to build connectors
Logic editor provides seamless transition from UI to Mobile to DB to API to File etc. Engine modules need to be added, and logic is dis-integrated across steps. Not in same flow
AI based robust object handling and self-healing (works seamlessly with Angular, iframes, Shadow DOMs, charts, SVG, canvas etc.) Traditional object interaction with limitations to handle complexity and newer tech stacks
Element Id is visual and intuitive with instant feedback on Selector choice Complex list based attribute selection, with no feedback on selector setup
Behavior driven predictive test designer for fast test creation and reusability Every test case starts with blank slate, and tester needs to have framework knowledge
Automated and AI based reconciliation for UI and Object changes No centralized handling, No reconciliation
AI driven data permutations auto-generates test cases providing accurate coverage Complex, de-centralized data definitions
Capture the structure of test data based on business semantics. Ensure data abstraction is consistently driven across test scenarios. Not available
Seamlessly manage multiple variants of test data suitable for different test execution environments Not available
Cloud driven automatic extendibility to newer tech stacks (UI controls, DB, Mainframes, Files etc.) Manual process to create and add engines. Requires specialized skills

Automation Execution

Cloud and Local Execution agents allow just-in-time execution against any tech stack Cloud agent not natively supported
Cross OS, Cross browser, Cross Mobile execution natively packaged with same agent Limited support with script setup approach
Inbuilt parallel execution with robust high performance agent providing scalability No parallel execution for web/UI on one agent
Secured and Encrypted communication between Agent and Cloud to test inside firewalls without any special VPN's or network restrictions Repository based workspace sharing with traditional "print report" , not real time or dynamic
Docker support for Agent farm expansion with dynamic scalability and parallelization Not supported
Integration with 3P cloud infrastructure farms like sauce labs, browser stacks Limited support with technical setup requirement

Change Management

Naturalized model with referential integrity across pages, test steps, objects, suites automates the change impact process efficiently propagating changes across test assets No referential integrity possible between test assets
Objects are centrally managed in correlation with Application pages, and object changes are automatically reflected across all tests Objects are managed in library manner that do not relate back to common shared model
Collaborative platform on cloud with real time sharing, version control, branching with intuitive design not requiring any special training or "source control" management skills Offline repository for black-box script database. Requires maintaining a separate source control system and related operations.

CI/CD & Tool Integrations

Bi-directional and natively built synchronizer for Traceability tracking, coverage analysis and requirements based Test suite planning on a modern Web Interface on cloud Technical level integration with no clear visibility (IDE based developer view)
Two-way Defect creation, synchronization, and status tracking for full suite management Database level duplicate data synch with no suite management views and portlets
CI job execution with pre-set configurations automatically execute test suites. Adapters support Jenkins, Team city, Bamboo, AzureDevOps, AutoRabit, Copado, Command line API etc. Technical integration needing synch rules with overheads.
Workflow orchestration with CI workflows One-way test execution trigger with minimal feedback

Technology Extendibility

Open concept of user extensibility to expand scope of existing technology stack, as well as build-import to extend for new technology stack Engine based concept requiring very specialized skill set
Cloud based community expansion is significantly rapid Offline concept duplicates effort and high overhead and dependency of support
No impact from upgrades and no operational responsibility on the customer Needs migrations and upgrades

Governance & Reporting

ACCELQ's app universe and analytic based algorithms drive automated Test Planning ensuring coverage No intelligence in test planning and suite creation, folder style traditional planning
Dynamic live results views with actionable reports to trigger reruns Static Report is dumped into an offline folder type repository
Embedded screen captures, directly associated with automation interactions Screen dump report that is separate from test result report
Umbrella style re-run capability for suite re-executions and roll up tracking Not available

The ACCELQ difference

Simple to adopt and elegantly designed, with the power to give your testing real acceleration

API and UI automated in the same flow

Unique capability to integrate API and UI testing in the same flow, enabling true end-to-end validation without handoffs. API testing at the same simplicity and regression maturity as UI automation.

Automated test case generation and data planning

Design your data behavior on a canvas and let ACCELQ take care of automated test case generation with proven risk coverage assurance. Centrally manage data changes without impacting test cases.

Seamless CI/CD integration and natural traceability

Native integration with CI/CD tools such as Jira, Jenkins ensure test automation is integral to development lifecycle. Traceability is redefined with the intelligent, connected test repository.

Salesforce Release

Being an ISV partner, ACCELQ is aligned to Salesforce releases to ensure smooth Salesforce upgrades with robust Automation testing

Automation Support

Automation support for diverse technology stack for end-to-end Salesforce process validations

In-sprint automation without need for programming

Develop automation test logic in plain English, concurrently with application development. Address in-sprint volatility with intelligent change management and powerful reconciliation engine.

Enable manual testers to automate testing

Powerful natural language editor allows you to write test automation logic in plain English. Design-first approach on UX driven platform allows manual testers to scale up without learning curve.

Visual application model for lifecycle automation

ACCELQ’s Universe is a visual blueprint of your application and drives automation across quality lifecycle with business process focus. Develop test scenarios with predictive analytics.

Self-healing autonomic test automation

ACCELQ’s analytic runtime engine ensures a reliable test execution by intelligently adapting to unexpected application changes. Design-first approach ensure Robust element ID based on AI.

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