Investigating Challenges and Best Practices of Salesforce Test Automation
Undoubtedly, Salesforce is the globally recognized CRM solution with 150,000+ customers. However, to check whether configurations and codesareoperational and Salesforce instance is supporting your business processes, testing of Salesforce is crucial. Whether you’re system architecture, admin, developer or QA head, you need to test your instance to identify the release readiness, potential errors, and areas that might need improvement. Thus, we can say that Salesforce testing is important because
- A bad release can cost you revenue, reputation and profit.
- A bug leakage to production can have a devastating impact on business continuity.
- Poor quality can lead to bad business reputation.
However, testing critical workflows, key integrations, multitude of data sources, enhancements, customizations, and custom reports is not a walk in the park with respect to Salesforce. Enterprises that are still using manual testing approaches need to understand that it cannot be considered as a viable solution because it is time consuming, offers inadequate coverage, inaccurate and can lead to business disruption.
Salesforce is a complex application and enhancements, customizations, or modifications in the business rules or new Releases have potential to impact your current business processes. Validating software for multiple iterations manually is daunting.
Some of the challenges that enterprises might face in Salesforce testing are given below.
- If you’re looking to move from “Classic” to “Lightning”, then you need to understand that test scripts written for one version may not be used for other.
- Lightning comes with new layouts, navigation, integrations, & workflows. So, number of use cases has been increased tremendously.
- Salesforce rolls out releases three times a year. So, testing thrice the software can be daunting and inadequate coverage can lead to business disruptions.
Address these Challenges with Salesforce Test Automation
To address these challenges enterprises should opt for Salesforce Test Automation. Introducing automation will help QA teams to achieve increased test coverage along with faster feedback. Automation will improve the whole testing process by speeding up feedback, quick detection of bugs, and robust reporting.
Although there are numerous benefits of introducing automation in Salesforce testing, there are certain challenges also that need to be addressed. Some of these challenges are listed below.
- Salesforce is a complex app in which front-end is built with dynamic frames. Since, Salesforce don’t have fix ID, name, class or CSS attributes, you cannot hard code the locator of the element.
- Salesforce uses components such as Shadow DOM to deliver compatibility with all browsers. However, many open source test automation platforms do not support them natively.
- Tables in Salesforce are database driven. The rows and columns are created dynamically as per the user privileges. Performing actions on these tables using standard automation tools is complicated.
- Salesforce has two major versions i.e. “Classic” and “Lightning”. So, it is not necessary that automation scripts that supports “Classic” will also support “Lightning”.
- Salesforce rolls out releases thrice a year. In case of code-based solutions, test scripts might get break due to change in dynamic elements while in recorders, testers might need to re-record every time due to changes in the configurations.
The proposed solution: AI-Powered Test Automation Framework
Enterprise should look for AI-powered test automation platform for Salesforce that not only identifies where automation scripts have broken but also heals them automatically to reduce the maintenance burden. The test automation framework should be able enough to identify how changes have impacted the current configurations, enabling QA teams to understand risk and production readiness of the releases. The test scripts should work on both the interfaces and while AI-driven risk-based testing approach should help in minimizing efforts by recommending what needs to be tested based on risk levels.