{
  "id": 11003301,
  "title": "Perforce Applies Machine Learning to Generate Synthetic Data for App Testing",
  "url": "https://urgent.news/2026/09/30/perforce-applies-machine-learning-to-generate-synthetic-data-for-app",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-30T18:10:13.000Z",
  "source": {
    "name": "DevOps.com",
    "slug": "devops-com",
    "url": "https://devops.com/perforce-applies-machine-learning-to-generating-synthetic-data-for-app-testing/"
  },
  "original_language": "en",
  "account": "Perforce Software has introduced an AI-powered tool designed to streamline synthetic data generation for app testing, according to a recent report. This new feature, called Delphix Synthetic Data, utilizes machine learning algorithms to create data that accurately reflects specific use cases. According to Mayank Ahluwalia, a senior product manager for Perforce Delphix, the tool automatically determines data structures, relationships, and business context from various sources, eliminating the need for manual intervention typically required by DevOps teams in the past.\n\nAhluwalia highlighted that the primary objective of this tool is to reduce or remove the necessity for application development teams to access production data to conduct tests. This process has often become a bottleneck, but with Delphix Synthetic Data, a self-service platform for generating synthetic data can now be employed. As AI agents are increasingly used for test creation and execution, this capability will become even more crucial.\n\nHowever, the effectiveness of synthetic data has been questioned. A recent Perforce survey revealed that only 34% of respondents found synthetic data to provide referential integrity, and 36% believed it to be data realistic. Ahluwalia noted that machine learning algorithms trained specifically to generate synthetic data can produce results that are more closely aligned with particular use cases, making it easier for DevOps teams to decide on a mix of synthetic and masked data for creating tests that better represent real-world environments.\n\nThe tool allows for data access via a graphical user interface, application programming interfaces (APIs), or the Model Context Protocol (MCP). DevOps teams can load this data into any large language model (LLM) of their choice to generate tests, helping to control costs. While it remains unclear how quickly DevOps teams are currently integrating AI into application testing, the potential for improved application quality as this process becomes more streamlined is evident. Nevertheless, the rapid pace at which code is now being generated in the AI era is putting a strain on existing testing workflows, leading to an increase in issues that DevOps teams encounter post-deployment in the AI landscape.\n\nIn conclusion, while the state of AI testing needs to catch up to the speed at which AI tools are used to generate code, DevOps teams are currently grappling with the challenges of this new reality. Until a complete agentic approach to managing the software development lifecycle (SDLC) is adopted, these teams can expect to remain busy addressing the issues arising from the accelerated pace of AI-driven code generation.",
  "summary": "Perforce Software has added a tool that leverages artificial intelligence (AI) to make it simpler for application development teams to generate synthetic data for application testing purposes. Mayank Ahluwalia, a senior product manager for Perforce Delphix, said Delphix Synthetic Data makes use of machine learning algorithms to generate synthetic data for specific use cases. It […]",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}