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TEAM Software

Case Study: Performance and Capacity Testing

QA Consultants was invited by Team Software to assist with a critical need for their software system to handle a dramatic sudden scale in both data volume and concurrent users on their platform while approaching a major application upgrade. Using a tool called LOGINVSI, the tests were able to mimic the users on the platform to identify and optimize database queries. The upgrade went live without any issues, leaving Team Software with a model for continuous performance engineering.

“I am very pleased with the project myself. We went live and had no issues! The proof of the pudding is in the eating – and we all did that. Great job helping us get there, being flexible, and acting with urgency.”
Chip Irek, CTO and Project Sponsor TEAM Software

Challenge & Test Requirements

  • TEAM Software was challenged by a software modification that needed to meet high growth objectives
  • QA Consultants identified risk areas, established clear requirements for the areas under test, and selected tools to meet project objectives
  • QA Consultants was challenged with developing a load test solution that included a thick client Windows application delivered via a Parallels, Web based portals, and API infrastructure
  • The solution required an integrated performance and capacity test across 3 platforms to the same database


  • Zero production performance capacity issues on go live
  • Under budget through time and resource management
  • Established a model for baseline CI/CD/CQ with continuous performance engineering

Solution & Approach

  • Identify performance objectives and risks
  • Conduct a tool analysis and POC
  • Selected LoginVSI for Windows application testing
  • Performance testing that mimicked production usage and growth
  • Determine system breaking points under stress and tuning needs


  • Identified and optimized slow database queries
  • Future-proofed testing in a Continuous Integration Pipeline
  • Seamless knowledge transfer for internal staff
  • Determined web front end break point capacity
  • Improved infrastructure based on test results
  • Implemented API performance related improvements