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AI powered Software Defect Density Prediction

  • AI powered Software Defect Density Prediction

Requs.ai Defect Prediction

AI Powered Software Defect Prediction

Predict defect density and shift to the left

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Traditionally, software teams wait until testing to measure or track defects—often when it’s too late to prevent schedule slips or performance issues. Requs AI Predict changes that. By applying machine learning to the world’s largest database of software defects, it predicts defect density, escaped defects and identifies risks before code is even written. This “shift-left” approach to predict defect density enables teams to proactively manage release stability, performance, and safety. 
Other models are reactive
  • Other tools are used later

    C-SFRAT, validata sense.ai, ContextQa, Lambda test intelligent, QMetry are used later in development and are reactive.

  • Other tools and models require defect discovery data

    By the time you have this data, the project can already be late due to resource misallocation

Requs AI Defect Prediction Solution
  • Data driven predictions

    Stop guessing and start using data to make informed decisions.

  • No need for failure data

    Just answer some questions about your software and how it's developed

  • AI-Powered

    Built from the world's largest defect density benchmarking study.

How Requs.ai Defect Prediction it works

From Q&A to defect density and escaped defects

Answer some questions

About the product, development practices and environment. The more you know the more accurate the prediction.

AI Analysis

The defect density and risk level (percentile ranking) is predicted from the AI model

Escaped defects predicted

Escaped defects = defect density * effort (story points)

Agile Development Forecasts

In angle world the software is never done. Requs.ai forecasts the defects for each program increment so you can spot technical debt pile up

The only proactive defect density prediction software

  • Comprehensive

    Requs Ai has 6x data as Rome Laboratory TR-92-52 and 60,000x data of Musa prediction model

  • Modern

    TR-92-52 and Musa models 40+ years old. The ancient models can't model effects of MBSE, Agile development, etc. on escaped defects

  • Easy

    If you don't know an input, it's not considered in the prediction.

  • Confidence

    The more questions you answer (accurately) the better the confidence in the prediction

Predict defect density with AI

Flexible purchasing options

  • Integrated product suite

    Requs AI Predicts covers the software failure rate, site reliability and error budget while Requs AI Software FMEA generates an effective software FMEA.

  • Licensing options

    Dedicated or floating licenses. Yearly subscriptions or perpetual plans. Cloud or Desktop solution.

  • Tiered editions

    Feature tiers allow you to purchase only the features you need.

  • Standards

    IEEE 1633

  • Air Gapped Support

    Requs AI runs on air gapped systems

  • Integrated workflow/API

    Requs AI S has an API and open architecture.