  In Software Development Life Cycle (SDLC), security vulnerabilities are one
of the points introduced during the construction stage. Failure to detect
software defects earlier after releasing the product to the market causes
higher repair costs for the company. So, it decreases the company's reputation,
violates user privacy, and causes an unrepairable issue for the application.
The introduction of vulnerability detection enables reducing the number of
false alerts to focus the limited testing efforts on potentially vulnerable
files. UMKM Masa Kini (UMI) is a Point of Sales application to sell any Micro,
Small, and Medium Enterprises Product (UMKM). Therefore, in the current work,
we analyze the suitability of these metrics to create Machine Learning based
software vulnerability detectors for UMI applications. Code is generated using
a commercial tool, SonarCloud. Experimental result shows that there are 3,285
vulnerable rules detected.
