A worldwide company that manufactures DNA tools used in medicine, agriculture, data storage, and fighting COVID-19.
The company’s ecosystem included a highly dynamic application and environment. Their application UI was based on Angular.js with a microservices back-end. An e-commerce application uploaded customer order files, produced a price quote, and handled invoices. They used Salesforce Data Cloud (SFDC) to review order details like shipping, billing, and handling payments. The company developed a custom back-end microservices application framework that moves customer orders through the actual product planning and production process. The image shown here illustrates the company environment.
Prior to GSPANN's involvement, the company's development team pushed changes to its MES application modules directly to the live servers. Although a separate team handled the e-commerce and SFDC testing, there was no testing of any MES applications other than end-user acceptance testing (UAT). Since there was no formal end-to-end testing infrastructure in place, the company had no way to confirm the viability of an order from beginning to end. Even worse, no consistent documentation was available to help build the testing infrastructure.
This approach was adequate when the company first started; however, its application production quality had declined as the business expanded. Various industry studies have shown that a bug caught in development takes significantly less time and resources to resolve compared with the amount of effort required to resolve a bug that has reached the user acceptance stage. Therefore, one of the primary objectives established by our QE team was to catch bugs early in the development process.
Another challenge our team faced was inadequate test coverage. For instance, although the company allowed its customers to upload spreadsheet data in various formats such as Excel, CSV, TSV, or FASTA, they only performed testing for Excel. Significant gaps in test coverage were also present in the e-commerce application.
To summarize, the company was looking for:
The solution involved three main phases: getting a clear picture of the current problem domain, building an automated end-to-end testing infrastructure, and putting in place tools that would allow the company to create their own automation workflows.
We assisted the company in implementing several industry best practices to improve the quality of code being produced and decrease the amount of time taken to perform regression tests. Greater collaboration between the company business and development teams improved the overall efficiency of the production process.
Here are some key takeaways from our solution:
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