Cassantec prognostic solution

Type Web application
TECHNOLOGIES CSV, Java, JavaScript, JSON, PHP, PostgreSQL, Scala
AREAS OF EXPERTISE Browser graphic, Mathematical statistics and probability theory, Cloud computing, Flexible configuration, Forecasting, JSON data format, PDF-report generating, Distributed Computing, Service Integration
QA TOOLS Selenium, Browser DevTools, MySQL Workbench, Putty, JIRA, TestLink, Blazemeter
TEAM 11 employees

Cassantec is an industry-leading provider of a SaaS-solution that uses unique mathematical algorithms to forecast equipment malfunctions. This allows businesses (nuclear power, fossil power, railways, etc.) to make timely decisions to repair or replace equipment depending on forecast results and save millions of dollars.
The prognostic solution is the only solution which provides reasonably accurate forecasts for 3 years ahead. It defines the level of malfunctions: standard, high, critical. The forecasts are based on data analysis of a great number of sensor controllers not on the analysis of past malfunctions that other systems make.


Development challenges:

  1.  Cassantec’s customers had difficulties working with large amounts of data. The reports couldn’t be viewed in a browser.
  2.  Cassentec wanted to make the report generation in the backend faster.
  3.  The problems on the server caused instability of the backend.

QA challenges:

This is a big data application. It processes a huge amount of data related to the forecast of possible equipment malfunction. The equipment for which the forecast is calculated may include a number of units, components. Each of them may have a number of parameters, monitored and sampled over the time, on which the prognosis is calculated. The primary challenge for QA team was to understand the business domain and understand how the configuration of the monitored equipment is described. Then to create test equipment models with sets of units and components and to craft the test parameter datasets.

  1. The frontend needed to be optimized. Then the reports could have been viewed in the browser on Cassantec site.
  2. For speeding the report generation some calculations are made in the background (so called pre-computing results).
  3. We optimized the code and tuned GarbageCollection.

QA activities:

Functional testing, API testing, performance testing, configurational testing, web Automation, QA process establishment and management.

  1. We have optimized the frontend and the reports can be viewed in the browser. We have shared our experience and written an article about frontend optimization in the blog.
  2. We have added a new feature - Machine Learning. And the reports are now generated faster.
  3. The system works stably and Casantec’s customers save dozens of million dollars.

ISS Art continues to assist in optimizing the work on the project as it is a long-term project.

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